Sudden Cardiac Arrest: Studies on Risk and Outcome

UvA-DARE (Digital Academic Repository)

Sudden cardiac arrest: Studies on risk and outcome

Blom, M.T.

Publication date 2014 Document Version Final published version

Link to publication

Citation for published version (APA): Blom, M. T. (2014). Sudden cardiac arrest: Studies on risk and outcome. Boxpress.

General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons).

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)

Download date:05 Oct 2021

SUDDEN CARDIAC ARREST Studies on risk and outcome

Marieke Tabo Blom Sudden Cardiac Arrest: studies on risk and outcome Academic thesis, University of Amsterdam, The Netherlands

Cover design: Proefschriftmaken.nl || Uitgeverij BOXPress Printed & Lay Out by: Proefschriftmaken.nl || Uitgeverij BOXPress Published by: Uitgeverij BOXPress, ‘-Hertogenbosch

Financial support by the Dutch Heart Foundation and the Acadamic Medical Center – University of Amsterdam for the publication of this thesis is gratefully acknowledged. SUDDEN CARDIAC ARREST Studies on risk and outcome

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctor

aan de Universiteit van Amsterdam

ten overstaan van een door het college voor promoties ingestelde commissie,

in het openbaar te verdedigen in de Agnietenkapel

op woensdag 8 oktober 2014, te 12:00 uur

door

Marieke Tabo Blom

geboren te Senanga, Zambia

Promotores: Prof. dr. A.A.M. Wilde Prof. dr. A. de Boer

Co-promotor: Dr. H.L. Tan

Overige leden: Prof. dr. A.P.M. Gorgels Prof. dr. L. de Haan Prof. dr. H.G.M. Leufkens Prof. dr. S.E.J.A. de Rooij Prof. dr. M.C.J.M. Sturkenboom Prof. dr. A.H. Zwinderman

Faculteit der Geneeskunde CONTENTS

1. Introduction and outline 7

PART I: Markers of SCA risk on the ECG from non-cardiac risk factors

2. Increased prevalence of electrocardiogram markers for sudden cardiac arrest in epilepsy 17

3. Brugada syndrome ECG is highly prevalent in schizophrenia 33

4. Differential Changes in QTc Duration during In-hospital Haloperidol Use. 53

5. Haloperidol and peri-operative changes in the QTc-interval: a prospective in-hospital study. 67

PART II: non-cardiac risk factors for SCA in the general community

6. Genetic, clinical and pharmacological determinants of out-of-hospital cardiac arrest: rationale and outline of the Amsterdam Resuscitation Studies (ARREST) registry 87

7. Cardiac sodium channels and inherited electrophysiologic disorders: an update on the pharmacotherapy. 101

8. Sudden cardiac arrest associated with use of a non-cardiac drug that reduces cardiac excitability: evidence from bench, bedside, and community. 125

9. Patients with obstructive pulmonary disease have an increased risk of ECG documented out-of-hospital cardiac arrest. 149

PART III: survival after out-of-hospital cardiac arrest

10. Reduced in-hospital survival rates of out-of-hospital cardiac arrest victims with obstructive pulmonary disease. 171 11. Women are less likely to be resuscitated during out-of-hospital cardiac arrest than men 187

12. Improved survival after out-of-hospital cardiac arrest and use of 205

13. General summary and discussion 225

Nederlandse samenvatting

Dankwoord

List of Publications

PhD Portfolio

CV CHAPTER 1

Chapter 1

Sudden cardiac arrest (SCA) is a leading cause of death in industrialized countries, affecting 180,000-250,000 individuals per year in the United States.1,2 In Europe, 275,000 out-of-hospital SCAs are treated by emergency medical services annually.3 The largest absolute number of SCA events occurs out-of-hospital in the general population in persons who are not considered to be at high risk for SCA. Although the majority of out-of-hospital SCAs ( sign of a cardiovascular disease.4-6 Out-of-hospital SCAs are often lethal, with survival rates ranging from 3% to 19%.7-9 outcome of a culmination of multiple factors. A combination of inherited and/or acquired disorders, stressors and circumstantial triggers eventually initiate an SCA. SCA victims in the general population are hence a very heterogeneous group to study. In order to reduce the number of deaths due to SCA, two main approaches can be distinguished: 1) identify those at risk in order to prevent SCA, and 2) optimize out- of-hospital resuscitation care and in-hospital post-resuscitation care to improve survival after SCA.

There are many ways to investigate the complex interplay of risk factors of SCA. These risk factors can be studied in the general population (applying to all) or in certain risk groups (persons with a higher a-priori risk). Risk factors may be sought at all levels: inherited and/or acquired factors, stressors and circumstantial triggers can play a role at the health care system level, in the direct social and physical environment of the person, and in the body itself, at system, organ or (sub)cellular level. Mechanisms playing at the (sub)cellular level may result in increased risk of a particular drug at the population level. Other mechanism may only play a role in certain patient groups and/or in certain situations. In many cases, SCA risk can be appreciated through a ‘multiple hit model’. need to coincide in order to initiate the cascade of events eventually leading to SCA. Knowledge about these interacting mechanisms may facilitate preventive measures to avoid lethal combinations of inherited and/or aquired risk factors. For example, a non- risk factors as medication use, stressors or disease. Informed advice to patients will enable them to avoid these combinations.

In order to explore proposed mechanisms involved in SCA risk, the risk of the occurrence of SCA itself can be studied, but also of markers of SCA risk, such as certain abnormalities at the electrocardiogram (ECG). For instance, can we see more ECG- abnormalities in a patient group that is known to have a higher risk of SCA when

compared to a control group? Or can the established effects on the ECG of a certain drug be recognized in a patient group that has multiple co-morbidities? Indeed, several include QTc-prolongation,10,11 a Brugada ECG-pattern12,13 and an early repolarization pattern (ERP).14 into the mechanism underlying increased SCA risk. Also, it may provide insight into the importance of an established mechanism (i.e., QT-prolongation caused by potassium channel blocking drugs) in a certain population.

The second approach to prevent deaths due to SCA is to improve survival chances after an SCA has occurred. This applies in particular to out-of-hospital cardiac arrest (OHCA), because the majority of lethal SCAs occur out-of-hospital, and survival gains of improved treatments are highest here. Determinants of survival after OHCA can be patient characteristics (including medical history), the setting and adequate recognition of the OHCA, and elements of the chain-of-care involved when resuscitation is initiated. Chances on survival are highest when an OHCA victim has a so-called shockable Presence of a shockable initial rhythm depends on the direct cause of the OHCA, but also on timely recognition: while OHCA is usually caused by VF, VF will deteriorate into, mostly untreatable, asystole within minutes. recent initiatives to improve out-of-hospital care were primarily aimed at decreasing 15 Evaluation of these initiatives and other determinants of survival after OHCA are of vital importance in order to maximize outcome of resuscitation attempts.

It is not possible to address all issues at all levels concerning SCA at once. Nonetheless, the AmsteRdam REsuscitation STudies (ARREST), of which several projects are presented in this thesis, seeks to facilitate the study of SCA at all the levels mentioned above, if possible in interaction with each other. ARREST was set up to establish the determinants of outcome of OHCA and gain insight into the genetic, clinical and pharmacological determinants of OHCA in the general population. Its aims are deliberately set this wide since it recognizes that OHCA, both the risk of its occurrence and the odds of surviving from it, is a particularly multifactorial phenomenon.

10 Chapter 1

outcome. While cardiovascular risk factors such as heart failure and cardiac ischemia are well-known to contribute to SCA risk from several large population-based studies16, studies, to which ARREST aims to contribute. Apart from examples from ARREST, this thesis presents several studies that examine SCA risk markers on the ECG. Part I of this thesis presents four studies based on ECG-data, whereas Part II and Part III provide an anthology of ARREST studies.

Chapter 2 presents a cross-sectional, retrospective study comparing ECGs in patients with epilepsy with community-dwelling controls. In a recent community-based study, we found that people with epilepsy had a 2-3 fold increased risk of SCA.17 In the study presented here, we aimed to determine whether ECG-risk markers of SCA (severe QTc-prolongation, Brugada ECG-pattern, ERP) are more prevalent in people with epilepsy than in community-dwelling controls. Chapter 3 has a similar approach in a different patient group, i.e., patients with schizophrenia. While SCA risk is increased in schizophrenia, the causes of this association are not fully resolved.18 In this study, we compared ECG markers of SCA-risk, in particular, Brugada-ECG pattern and QTc- prolongation, between patients with schizophrenia and community-dwelling controls. Chapter 4 and 5 both investigate the effects of the antipsychotic drug haloperidol on the QT interval in two in-hospital populations. Haloperidol has established QT-prolonging effects on the ECG by blocking cardiac potassium channels,19 comorbidities is less well-studied. In chapter 4 the QT-interval before, during and after haloperidol use was measured, along with medication use and clinical variables in order to identify risk factors for potentially dangerous QT prolongation. In chapter 5, and investigate whether effects on the ECG of haloperidol can be distinguished in a perioperative setting.

The second part of this thesis presents studies performed with data from the ARREST registry, and focuses on non-cardiac risk factors for SCA in the general community. Chapter 6 describes the rationale and outline of the part of ARREST that was set up to study genetic, clinical and pharmacological determinants of OHCA, providing examples of possible study designs. Chapter 7 presents a review of the cardiac sodium channel and inherited electrophysiological disorders, and provides an overview on pharmacotherapy. The cardiac sodium channel plays a pivotal role in the propagation of

electrical activity through the heart. Studying this ion channel provides valuable insights into the electrophysiological mechanisms that are at play at the level of the cardiac e.g., the use of drugs prescribed for the treatment of non-cardiac disease, such as antidepressants and anti-epileptics. Chapter 8 is an example of how the combination of molecular-genetic work and patient data with a population study establishes increased SCA risk of nortriptyline, an antidepressant drug, while providing insight into the mechanism behind this increase. Chapter 9 presents a community based case-control study that investigates whether patients with obstructive pulmonary disease have an and/or respiratory drug use.

outcome of resuscitation attempts after OHCA, and determinants thereof. Chapter 10 presents a study that analyzes whether patients with obstructive pulmonary disease have a lower survival rate after OHCA than patients without obstructive pulmonary disease. Patient’s co-morbidities may determine survival after OHCA, and guide post- resuscitation care. Chapter 11 examines gender differences and aims to study whether access to and outcome of resuscitation attempts after OHCA differs between men and women. Finally, Chapter 12 examines whether temporal trends can be distinguished in neurologically intact survival after OHCA, and, if so, if a change in survival is

12 Chapter 1

1. Chugh SS, Reinier K, Teodorescu C, et al. Epidemiology of sudden cardiac death: clinical and research implications. Prog Cardiovasc Dis 2. Stroke Statistics Subcommittee. Heart disease and stroke statistics--2011 update: a report from the American Heart Association. Circulation 3. Atwood C, Eisenberg MS, Herlitz J, Rea TD. Incidence of EMS-treated out-of-hospital cardiac arrest in Europe. Resuscitation. 4. Myerburg RJ. Sudden cardiac death: exploring the limits of our knowledge. J Cardiovasc Electrophysiol. 5. Stecker EC, Vickers C, Waltz J, et al. Population-based analysis of sudden cardiac death with and Death Study. J Am Coll Cardiol. 6. status and challenges for the future. Eur Heart J. 7. Aufderheide TP, Yannopoulos D, Lick CJ, et al. Implementing the 2005 American Heart Association Guidelines improves outcomes after out-of-hospital cardiac arrest. Heart Rhythm 8. Berdowski J, Berg RA, Tijssen JG, Koster RW. Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies. Resuscitation 9. Blom MT, Beesems SG, Homma PC, et al. Improved survival after out-of-hospital cardiac arrest and This thesis, 2014. 10. Algra A, Tijssen JG, Roelandt JR, Pool J, Lubsen J. QTc prolongation measured by standard 12-lead electrocardiography is an independent risk factor for sudden death due to cardiac arrest. Circulation. 11. Straus SM, Kors JA, de Bruin ML, et al. Prolonged QTc interval and risk of sudden cardiac death in a population of older adults. J Am Coll Cardiol 12. Matsuo K, Akahoshi M, Nakashima E, et al. The prevalence, incidence and prognostic value of the Brugada-type electrocardiogram: a population-based study of four decades. J Am Coll Cardiol. 13. Postema PG, van Dessel PF, Kors JA, Linnenbank AC, van Herpen G, Ritsema van Eck HJ, van Geloven N, de Bakker JM, Wilde AA, Tan HL. Local depolarization abnormalities are the dominant pathophysiologic mechanism for type 1 electrocardiogram in brugada syndrome a study of electrocardiograms, vectorcardiograms, and body surface potential maps during ajmaline provocation. J Am Coll Cardiol. 14. Haïssaguerre M, Derval N, Sacher F, et al. Sudden Cardiac Arrest Associated with Early Repolarization. 15. Berdowski J, Blom MT, Bardai A, Tan HL, Tijssen JG, Koster RW. Impact of onsite or dispatched Circulation. 16. Zipes DP, Wellens HJ. Sudden cardiac death. Circulation. 17. Bardai A, Lamberts RJ, Blom MT, et al. Epilepsy is a risk factor for sudden cardiac arrest in the general population. PLoS One. 18. Koponen H, Alaräisänen A, Saari K, et al. Schizophrenia and sudden cardiac death: a review. Nord J Psychiatry

19. Mörtl D, Agneter E, Krivanek P, Koppatz K, Todt H. Dual rate-dependent cardiac electrophysiologic effects of haloperidol: slowing of intraventricular conduction and lengthening of repolarization. J Cardiovasc Pharmacol

14 PART I

CHAPTER 2

R.J. Lamberts, M.T. Blom, J. Novy, M. Belluzzo, A. Seldenrijk, B.W. Penninx, J.W. Sander, H.L. Tan, R.D. Thijs

J Neurol Neurosurg Psychiatry. 2014 In press.

People with epilepsy are at increased risk of sudden cardiac arrest (SCA) due to based study. We aimed to determine whether ECG-risk markers of SCA are more prevalent in people with epilepsy.

In a cross-sectional, retrospective study, we analyzed the ECG-recordings of 185 people with refractory epilepsy and 178 controls without epilepsy. Data on epilepsy characteristics, cardiac comorbidity, and drug use were collected and general ECG variables (heart rate [HR], PQ- and QRS-intervals) assessed. We analyzed ECGs for three markers of SCA risk: severe QTc-prolongation (male >450 msec, female >470 msec), Brugada ECG-pattern, and early repolarization pattern (ERP). Multivariate regression models were used to analyze differences between groups and to identify associated clinical and epilepsy-related characteristics.

People with epilepsy had higher HR (71 vs. 62 bpm, p<0.001) and a longer PQ- interval (162.8 vs. 152.6 msec, p=0.001). Severe QTc-prolongation and ERP were ERP: 34 vs. 13%, p<0.001), while the Brugada ECG-pattern was equally frequent in both groups (2% vs. 1%, p>0.999). After adjustment for covariates epilepsy

remained associated with ERP (ORadj 2.4, 95% CI 1.1-5.5) and severe QTc-

prolongation (ORadj 9.9, 95% CI 1.1-1317.7).

ERP and severe QTc-prolongation appear to be more prevalent in people with refractory epilepsy. Future studies must determine whether this contributes to increased SCA risk in people with epilepsy.

18 Chapter 2

A recent community-based study found that people with epilepsy had a 2-3 times SCA.1 The 12-lead standard ECG is a potential low-cost screening test for SCA risk. QTc-prolongation,2-4 Brugada ECG-pattern (Brugada-ECG), 5 and early repolarization pattern (ERP).6-9 comparing people with epilepsy and without epilepsy, mild QTc-prolongation was reported in those with epilepsy,10-12 while others reported similar QTc-durations in both groups,13-14 or QTc-shortening.15-16 The number of people with severe QTc-prolongation was not reported in these studies. Brugada-ECG is characteristic of Brugada syndrome, an inherited disease associated with disrupted cardiac depolarization.17 Sudden death in young people with structurally normal hearts in epilepsy and Brugada syndrome occurs mainly during rest or sleep.17-19 ERP, long considered a benign and more common variant of the Brugada- than in healthy controls.20-21 predictor of SCA in several population-based studies.6-9 We hypothesize that the prevalence of severe QTc-prolongation, Brugada- ECG, and ERP is increased in people with epilepsy, which may (partly) explain the higher SCA risk in epilepsy.

22 who were assessed at one epilepsy tertiary referral center between September 2009 and April 2011. In all, a resting 12-lead ECG was recorded as part of the routine assessment on initial evaluation.23 The anonymized data were obtained as part of an audit into epilepsy-associated comorbidities, which was approved as such by the local ethics committee. As all data was acquired during routine clinical care no informed consent was required.

Controls were drawn from a sub-study of the Netherlands Study of Depression and Anxiety.24 They were 18-65 years old, randomly selected from a general practitioners’ database in the Amsterdam area, and had no lifetime history of a psychiatric disorder.24

A resting 12-lead ECG was recorded in 179 subjects. We excluded all those with a diagnosis of active epilepsy or current use of AEDs (n=1), leaving 178 controls. The study was approved by the local ethics committee. Informed consent was obtained from all participants.

In all participants, conventional characteristics of the 12-lead ECG (heart rate [HR], as type-1 (coved ST-segment elevation in right precordial ECG leads 0.2 mV followed by a negative T-wave with little or no isoelectric separation), type 2 (coved ST-segment elevation in V1-V3 followed by a gradually descending ST-segment elevation remaining 0.1 mV above the baseline and a positive or biphasic T-wave that results in a saddle back 0.1 mV of saddle back type, coved type, or both), according to Brugada syndrome consensus criteria.25 QTc-duration was calculated using Bazett’s formula to correct for HR: QT/RR.26 Guidelines: >450 msec in men, >470 msec in women.27 elevation 0.1 mV in 2 adjacent leads with either slurring or notching morphology.7,20 Leads V1-V3 were not assessed to avoid confusion with ECG patterns typical of Brugada syndrome. ECGs with intraventricular conduction delay (QRS duration of 0.12s), which precluded reliable assessment of QTc-duration, Brugada-ECG or ERP (n=3, all cases), were excluded from analysis.7,20 An experienced cardiologist (HLT) reviewed all ECGs for Brugada-ECG absence of severe QTc-prolongation and ERP were assessed by two blinded researchers verdict. There was no systematic difference between the reviewers in their analysis of the QTc-interval (paired t-test: 0.59) or ERP (kappa score 0.75).

Variables were collected from medical records (in cases) and on self-reported/assessed information during a face-to-face interview (in controls). These variables were: gender, age, presence of 2 cardiac risk factors (hypertension, hypercholesterolemia, diabetes 1) QT-prolonging medication (www.azcert.org), 2) depolarization-blocking drugs (www.brugadadrugs.org), 3) cardiovascular drugs (-adrenoreceptor blockers, calcium channel antagonists, angiotensin-converting enzyme inhibitors, diuretics, angiotensin- II receptor blockers, nitrates, platelet aggregation inhibitors and/or statins) and 4) lipid-

20 Chapter 2

Among AEDs, QT-prolonging drugs were phenytoin and felbamate, while depolarization-blocking drugs included carbamazepine, oxcarbazepine, phenytoin, and lamotrigine. In people with epilepsy, additional data were recorded including epilepsy aetiology (symptomatic/non-symptomatic), history of epilepsy surgery (yes/no), age of onset and duration of epilepsy, seizure frequency (1 vs. <1/month), polytherapy (2 AEDs), presence of a learning disability, and family history of epilepsy (2 family members with epilepsy).

Differences between cohorts in baseline characteristics and ECG-parameters were analyzed using 2-statistics for categorical variables (Pearson/Fisher’s Exact test where appropriate) and the Student’s t-test/Mann-Whitney U test for continuous variables. We performed multivariate logistic regression models to determine whether epilepsy was independently associated with Brugada-ECG, severe QTc-prolongation, or ERP. associated (p<0.1) with outcome, whereas the second model included only those determinants that also changed the point estimate by 5%. As severe QTc-prolongation was not seen in controls, we used penalized logistic regression analysis to perform multivariate analysis applying the same strategy as above. Among people with epilepsy the same approach was used to determine which clinical (comorbidities and medication use) and epilepsy characteristics were associated with these SCA-predictors. Statistics Windows, Chicago IL, USA).

ECGs of 185 people with epilepsy and 178 controls were analyzed (Table 1). People more frequently used drugs with QT-prolonging or depolarization-blocking effects. QT- prolonging drugs used were AEDs (46%), antidepressants (30%), antipsychotics (20%), or antiemetics (5%), whereas depolarization-blocking drugs were almost exclusively AEDs (99%). The prevalence of 2 cardiac risk factors, heart disease, cardiovascular medication, and lipid lowering drugs did not differ between groups.

(n=185) (n=178) P-value Male gender 85 (46%) 65 (37%) 0.068 Mean age, years 38 (13.3) 48 (12.5) <0.001 7 (4%) 11 (6%) 0.293 2 (1%) 2 (1%) 0.969 3 (2%) 3 (2%) 0.962 QT-prolonging drugs 39 (21%) 1 (1%) <0.001 145 (78%) 1 (1%) <0.001 Cardiovascular drugs 32 (17%) 26 (15%) 0.484 Lipid lowering drugs 9 (5%) 6 (3%) 0.475 Heart rate, beats per min 70.7 (11.4) 61.8 (9.8) <0.001 PQ, msec 162.8 (26.0) 152.6 (32.6) 0.001 QRS, msec 88.7 (13.8) 91.0 (10.3) 0.066 QTc, msec 404.8 (33.0) 393.5 (24.8) <0.001 3 (2%) 2 (1%) >0.999 10 (5%) 0 (0%) 0.002 62 (34%) 23 (13%) <0.001 50 (27%) 20 (11%) <0.001

People with epilepsy had a higher HR (71 vs. 62 bpm, p<0.001), a longer PQ-interval, interval (89 vs. 91 msec, p=0.07). Mean QTc-duration was also longer: 405 vs. 394 msec, p<0.001. Brugada-ECG was equally prevalent in both groups (2% vs. 1%, p>0.999). The prevalence of both severe QTc-prolongation (5% vs. 0%, p= 0.002) and Figure 1) was higher in cases than in controls: Table 1.

22 Chapter 2

Apart from epilepsy, severe QTc-prolongation was univariately associated with (female) gender, (lower) age, (higher) HR, and use of depolarization-blocking drugs (Supplemental Table s1). QT-prolonging drugs were not used by those with severe QTc-prolongation. Due to the absence of severe QTc-prolongation in the control cohort, it was not possible to separate the effects of epilepsy and use of depolarization- blocking drugs (99% of which were AEDs) in multivariate analysis. Therefore, only epilepsy, gender, age, and HR were entered in the model (penalized logistic regression, Table 2). After correction for these variables epilepsy remained associated with severe

QTc-prolongation (Table 2, Model A: ORadj 9.9 (1.1-1317.7).

in cases (n=185) and controls (n=178)

(n=185) (n=178) Brugada-ECG 3 (2%) 2 (1%) 1.5 (0.2-8.8) NA NA Severe QTc- 10 (5%) 0 (0%) 21.0 9.9 9.9 (2.7-2708.2) (1.1-1317.7) (1.1-1317.7) ERP 62 (34%) 23 (13%) 3.4 (2.0-5.8) 2.3 (1.0-5.5) 2.4 (1.1-5.5)

e-1) or ERP (Table e-2). entered.

ERP was univariately associated with epilepsy (male) gender, heart disease (higher) HR and the use of QT-prolonging, depolarization-blocking, and cardiovascular drugs (Supplemental Table s2). Multivariate analysis showed epilepsy to be independently associated with ERP: Table 2, Model B: ORadj 2.4 (95% CI 1.1-5.5). In those with epilepsy (n=185) none of the epilepsy characteristics were associated with either severe QTc-prolongation or ERP (Supplemental Tables s3 and s4).

We systematically analyzed the prevalence of three ECG-risk markers of SCA and found that severe QTc-prolongation and ERP were more frequent in people with refractory epilepsy. Our study had some limitations. There were several differences between cases and controls: people with epilepsy were younger and more likely to be male. Younger age may result in a lower QTc-interval and a higher ERP prevalence.28,29 In view of the relatively small age differences in our study, however, only minor effects on QTc prolongation and ERP should be expected. Accordingly, epilepsy status and QTc-prolongation after correction for age. ERP is more frequently found in males, but epilepsy status remained an independent determinant after accounting for gender differences.29 As for severe QTc-prolongation, the association with epilepsy status and with previous studies, HR was higher in cases than in controls: this may be due to epilepsy-related abnormalities of cardiac autonomic balance.30 HR is incorporated in an overestimation of QTc-duration in people with higher HR: particularly those with epilepsy.26 We, therefore, included HR in the multivariate analysis of both severe QTc- using Bazett’s formula and for study comparability, we did not use alternative QT correction formulae. We found that QTc-duration was increased in people with epilepsy when compared with controls. This is concordant with some,10-12 but not all previous studies.13-16 or medication use between study populations. We analyzed people with refractory, more severe epilepsy than in previous studies. QTc-duration was dichotomized in one study (>440 msec, yes vs. no), allowing comparison between ours and their results. In in cases and controls.11

24 Chapter 2 prolongation (>450msec in men, >470msec in women), is recommended, however, by the current guidelines of the European Society of Cardiology and has been used in the more recent large-scale prospective, population-based studies of SCA-risk.3,27 We therefore believe our criteria to be more clinically relevant. In our analysis of severe QTc-prolongation, we could not separate the effects of epilepsy and use of depolarization-blocking drugs. Severe QTc-prolongation was only present in people with epilepsy and depolarization-blocking drugs (predominantly AEDs) were used almost exclusively by this group. We believe the use of depolarization- blocking drugs is more likely a proxy for epilepsy severity than directly affecting cardiac repolarization. In this study, use of depolarization-blocking drugs was not related with severe QTc-prolongation when analyzing only the cohort with epilepsy. Use of depolarization-blocking AEDs was not associated with QTc-prolongation in cross- sectional studies,11-14 nor in a prospective drug trial.31 QT-prolonging drugs are more likely to contribute to severe QTc-prolongation, but none of the individuals with this ECG risk marker used these drugs. In the multivariate analysis of ERP, we could separate the effects of epilepsy and depolarization-blocking drugs and found that the latter variable was not an independent determinant. Due to the higher prevalence of this SCA risk marker in our population, the evidence for the association of epilepsy with ERP is stronger than with severe QTc- prolongation. of SCA.1 ERP was associated with a 1.7-fold increased risk of SCA in a recent meta- analysis,32 and seizures may facilitate the transition from ERP into Brugada-ECG.33 Severe QTc-prolongation is associated with a three-fold increased risk of SCA, which may be aggravated by additional peri-ictal QTc-prolongation.34,35 Severe QTc-prolongation, ERP, and certain epilepsy syndromes are associated with sodium and potassium channel mutations.36,37 Conceivably, a single mutation expressed in heart and brain might confer both a propensity for epilepsy and an innate vulnerability to cardiac arrhythmias, thereby linking epilepsy with these ECG-markers and SCA. Routine performance of a 12-lead ECG in all adults with suspected epilepsy is recommended by the NICE guidelines but not listed in the AES/AAN guidelines.24,38 The diagnostic yield of this practice has not yet been determined. Our study suggests that an increased prevalence of severe QTc-prolongation and ERP occurs in people with epilepsy. Routine ECG-evaluation in people with epilepsy may be of importance in guiding clinicians in their choice of AED therapy, e.g. avoidance of QT-prolonging or depolarization-blocking drugs in people with ECG-markers of increased SCA risk.

Study designed by RJL, JWS, HLT and RDT. Data was collected by RJL, MTB, JN, MB, AS and BP. Data analyzed by RJL, MTB, JWS, HLT and RDT. Manuscript drafted by RJL and MTB and critically reviewed

The authors are grateful to S. Balestrini for her assistance with data collection, to Dr. M. Tanck for his statistical assistance, and to Dr. GS Bell for reviewing the manuscript.

JWS receives research support from Epilepsy Society, the Dr. Marvin Weil Epilepsy Research Fund, Eisai, GSK, UCB Pharma, the World Health Organization, and the National Institutes of Health (NIH), and has been consulted by and received fees for lectures from GSK, Viropharma, Eisai and UCB Pharma. RDT receives research support from NUTS Ohra Fund, Medtronic, and AC Thomson Foundation, and has received fees for lectures from Medtronic, UCB Pharma and GSK. JWS and RDT are members of NIH (NBIH/NINDS –1P20NS076965-01). JN was supported by the Swiss National Science Foundation- Fellowships for prospective researchers and the SICPA Foundation, Prilly, Switzerland

This study was supported by the Dutch Epilepsy Foundation (project number 10-07), Christelijke Vereniging voor de Verpleging van Lijders aan Epilepsie (Nederland), and Netherlands Organization for

26 Chapter 2

1. Bardai A, Lamberts RJ, Blom MT, et al. Epilepsy is a risk factor for sudden cardiac arrest in the general population. PLoS One 2. Algra A, Tijssen JG, Roelandt JR, et al. QTc prolongation measured by standard 12-lead electrocardiography is an independent risk factor for sudden death due to cardiac arrest. Circulation 3. Straus SM, Kors JA, de Bruin ML, et al. Prolonged QTc interval and risk of sudden cardiac death in a population of older adults. J Am Coll Cardiol 4. Soliman EZ, Prineas RJ, Case LD, et al. Electrocardiographic and clinical predictors separating atherosclerotic sudden cardiac death from incident coronary heart disease. Heart 5. Matsuo K, Akahoshi M, Nakashima E, et al. The prevalence, incidence and prognostic value of the Brugada-type electrocardiogram: a population-based study of four decades. J Am Coll Cardiol 6. Tikkanen JT, Anttonen O, Junttila MJ, et al. Long-term outcome associated with early repolarization on electrocardiography. N Engl J Med 7. Sinner MF, Reinhard W, Mueller M, et al. Association of Early Repolarization Pattern on ECG with Risk of Cardiac and All-Cause Mortality: A Population-Based Prospective Cohort Study (MONICA/ KORA). PLoS Medicine 8. Haruta D, Matsuo K, Tsuneto A, et al. Incidence and prognostic value of early repolarization pattern in the 12-lead electrocardiogram. Circulation early repolarization pattern in a population-based study. Am J Cardiol 10. Drake ME, Reider CR, Kay A. Electrocardiography in epilepsy patients without cardiac symptoms. Seizure 11. Neufeld G, Lazar JM, Chari G, et al. Cardiac Repolarization Indices in Epilepsy Patients. Cardiology Epilepsy Res 13. Akalin F, Tirtir A, Yilmaz Y. Increased QT dispersion in epileptic children. Acta Paediatr 920. 14. Krishnan V, Krishnamurthy KB. Interictal 12-lead electrocardiography in patients with epilepsy. Epilepsy Behav 15. Teh HS, Tan HJ, Loo CY, et al. Short QTc in epilepsy patients without cardiac symptoms. Med J Malaysia 16. Ramadan M, El-Shahat N, A Omar A, et al. Interictal electrocardiographic and echocardiographic changes in patients with generalized tonic-clonic seizures. Int Heart J 17. Postema PG, van Dessel PF, Kors JA, et al. Local depolarization abnormalities are the dominant pathophysiologic mechanism for type 1 electrocardiogram in brugada syndrome: a study of electrocardiograms, vectorcardiograms, and body surface potential maps during ajmaline provocation. J Am Coll Cardiol 18. Surges R, Thijs RD, Tan HL, et al. Sudden unexpected death in epilepsy: risk factors and potential pathomechanisms. Nat Rev Neurol 19. Lamberts RJ, Thijs RD, Laffan A, et al. Sudden unexpected death in epilepsy: People with nocturnal seizures may be at highest risk. Epilepsia

20. Haïssaguerre M, Derval N, Sacher F, et al. Sudden Cardiac Arrest Associated with Early Repolarization. New Engl J Med J Am Coll Cardiol 1238. the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies. Epilepsia 1077. 23. NICE clinical guideline 137. The epilepsies: the diagnosis and management of the epilepsies in adults and children in primary and secondary care. http://publications.nice.org.uk (Accessed 17 Jan 2014) 24. Penninx BW, Beekman AT, Smit JH, et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res 25. Wilde AA, Antzelevitch C, Borggrefe M, et al. Proposed diagnostic criteria for the Brugada syndrome: consensus report. Circulation 26. Luo S, Michler K, Johnston P, et al. A comparison of commonly used QT correction formulae: the effect of heart rate on the QTc of normal ECGs. J Electrocardiol 27. Committee for Proprietary Medicinal Products. Points to consider: the Assessment of the Potential for QT Interval Prolongation by Non-Cardiovascular Medicinal Products. London, 1997. 28. Mangoni AA, Kinirons MT, Swift CG, et al. Impact of age on QT interval and QT dispersion in healthy subjects: a regression analysis. Age Ageing 29. Walsh JA, Ilkhanoff L, Soliman EZ, et al. Natural history of the early repolarization pattern in a biracial cohort: CARDIA (Coronary Artery Risk Development in Young Adults) Study. J Am Coll Cardiol 30. Sevcencu C, Struijk JJ. Autonomic alterations and cardiac changes in epilepsy. Epilepsia 737. 31. Saetre E, Abdelnoor M, Amlie JP, et al. Cardiac function and antiepileptic drug treatment in the elderly: a comparison between lamotrigine and sustained-release carbamazepine. Epilepsia 1849. 32. Wu SH, Lin XX, Cheng YJ, et al. Early repolarization pattern and risk for arrhythmia death: a meta- analysis. J Am Coll Cardiol 33. Gussak I, Antzelevitch C. Early repolarization syndrome: clinical characteristics and possible cellular and ionic mechanisms. J Electrocardiol 34. Surges R, Scott CA, Walker MC. Enhanced QT shortening and persistent tachycardia after generalized seizures. Neurology 35. Seyal M, Pascual F, Lee CY, et al. Seizure-related cardiac repolarization abnormalities are associated with ictal hypoxemia. Epilepsia congenital long QT syndrome and epilepsy. Neurology 37. Watanabe H, Nogami A, Ohkubo K, et al. Electrocardiographic characteristics and SCN5A mutations Circ Arrhythm Electrophysiol seizure in adults (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology and the American Epilepsy Society. Neurology

28 Chapter 2

P-value (n=353) 10 (100%) 175 (50%) 0.002 Male gender 1 (10%) 149 (42%) 0.051 Age, years 35.6 (12.5) 43.0 (13.8) 0.095 0 (0%) 18 (5%) >0.999 0 (0%) 4 (1%) >0.999 0 (0%) 6 (2%) >0.999 Hypertension 0 (0%) 39 (11%) 0.609 0 (0%) 34 (10%) 0.608 Diabetes Mellitus 0 (0%) 12 (3%) >0.999 QT-prolonging drugs 0 (0%) 40 (11%) 0.610 9 (90%) 137 (39%) 0.002 Cardiovascular drugs 0 (0%) 58 (16%) 0.375 Lipid lowering drugs 0 (0%) 15 (4%) >0.999 Heart rate, beats per min 83.6 (5.8) 65.8 (11.2) <0.001 PQ, msec 162.8 (25.8) 159.5 (24.8) 0.676 QRS, msec 85.2 (7.9) 89.9 (12.3) 0.228 QTc, msec 481.0 (10.8) 396.9 (26.7) <0.001 0 (0%) 5 (1%) >0.999 3 (30%) 82 (23%) 0.705 3 (30%) 67 (19%) 0.413

(n=363)

ERP (n=85) No ERP (n=278) P-value 62 (73%) 123 (44%) <0.001 Male gender 43 (51%) 107 (38%) 0.047 Age, years 41.9 (14.2) 43.1 (13.7) 0.473 3 (4%) 15 (6%) 0.775 3 (4%) 1 (1%) 0.041 4 (5%) 2 (1%) 0.029 QT-prolonging drugs 14 (16%) 26 (9%) 0.067 51 (60%) 95 (34%) <0.001 Cardiovascular drugs 19 (22%) 39 (14%) 0.067 Lipid lowering drugs 4 (5%) 11 (4%) 0.758 Heart rate, beats per min 69.2 (12.7) 65.4 (11.0) 0.015 PQ, msec 160.6 (27.2) 159.2 (24.1) 0.666 QRS, msec 89.5 (14.3) 89.9 (11.6) 0.817 QTc, msec 400.2 (33.3) 399.0 (28.7) 0.743 0 (0%) 5 (2%) 0.595 3 (4%) 7 (3%) 0.705

30 Chapter 2

P-value (n=175) 3 (30%) 77 (44%) 0.518 0 (0%) 14 (8%) >0.999 14.8 (8.4) 14.2 (10.9) 0.874 20.8 (11.8) 23.9 (14.2) 0.499 9 (100%) 155 (96%) >0.999 8 (80%) 149 (85%) 0.650 2 (1-4) 2 (1-6) 0.168 QT-prolonging drugs 0 (0%) 39 (5%) 0.124 9 (90%) 136 (78%) 0.693 Learning disability 2 (20%) 28 (16%) 0.666 1 (10%) 21 (12%) >0.999

ERP (n=62) No ERP (n=123) P-value 28 (45%) 52 (42%) 0.709 5 (8%) 9 (7%) >0.999 15.4 (11.8) 13.7 (10.3) 0.327 23.0 (14.4) 24.1 (13.9) 0.594 55 (98%) 109 (95%) 0.429 53 (85%) 104 (85%) 0.868 2 (1-6) 2 (1-5) 0.894 QT-prolonging drugs 13 (21%) 26 (21%) 0.979 51 (82%) 94 (76%) 0.363 Learning disability 8 (13%) 22 (18%) 0.385 5 (8%) 17 (14%) 0.254

32 CHAPTER 3

M.T. Blom, D. Cohen, A. Seldenrijk, B.W. Penninx, G. Nijpels, C.D. Stehouwer, J.M. Dekker, H.L. Tan

Circ Arrhythm Electrophysiol. 2014;7(3):384-91.

The causes of the increased risk of sudden cardiac death (SCD) in schizophrenia are not resolved. We aimed to establish whether 1) ECG markers of SCD-risk, in particular, Brugada-ECG pattern, are more prevalent among schizophrenia patients, 2) increased prevalence of these ECG markers in schizophrenia is explained by confounding factors, notably sodium-channel blocking medication.

In a cross-sectional study, we analyzed ECGs of a cohort of 275 schizophrenia patients, along with medication use. We determined whether Brugada-ECG was present, and assessed standard ECG measures (heart rate, PQ-, QRS- and comparable age (NESDA cohort, N=179), and, to account for assumed increased ageing-rate in schizophrenia, with 20-year older individuals (Hoorn cohort, n=1168), using multivariate regression models. cohort (11.6%) compared with NESDA controls (1.1%) or Hoorn controls (2.4%). Moreover, schizophrenia patients had longer QT-intervals (410.9 vs. 393.1 and 401.9 msec, both p<0.05), an increased proportion of mild or severe QTc-prolongation (13.1% and 5.8% vs. 3.4% and 0.0% [NESDA], vs. 5.1 and 2.8%, [Hoorn]), and higher heart rates (80.8 vs. 61.7 and 68.0 beats per minute, both p<0.05). Prevalence of Brugada-ECG was still increased (9.6%) when schizophrenia patients without sodium-channel blocking medication were compared to either control cohort.

Brugada-ECG has increased prevalence among schizophrenia patients. This association is not explained by use of sodium-channel blocking medication.

34 Chapter 3

Patients with severe mental illness have a 14-32 years reduced life expectancy1. Schizophrenia is associated with increased standardized mortality ratios for all-cause death2, cardiovascular death3, and sudden cardiac death (SCD)4. The causes for SCD-risk in schizophrenia are unresolved5. SCD is mostly caused by lethal cardiac arrhythmias resulting from disrupted cardiac electrophysiology (depolarization and/or repolarization)6. Many researchers ascribe SCD in schizophrenia to antipsychotics, as antipsychotics may cause such disruptions7. For instance, increased SCD-risk during (SGA) is commonly ascribed to their repolarization-blocking effects, signaled by QTc- prolongation8,9. However, individual susceptibility is crucial. Co-morbidities that more prevalent among schizophrenic individuals3. The possibility that inherited factors are also relevant has so far received less recognition. These considerations prompted us to conduct the present study. We systematically compared ECGs of a cohort of schizophrenia patients to ECGs of two cohorts of non-schizophrenia control subjects, and took co-variates for ECG abnormalities into account. Our primary aim was to establish whether ECG markers of SCD-risk are more prevalent in schizophrenia patients than in non-schizophrenic controls. This included the Brugada ECG pattern (Brugada-ECG) and QTc duration. Our secondary aim was to study whether differences in prevalence of these ECG markers may be explained by use of sodium-channel blockers or QTc-prolonging drugs and/or presence of cardiovascular risk factors.

In a cross-sectional study, all outpatients at the Department of Severe Mental Illness, Mental Health Care Center-North Holland North (n=603), typically in psychiatric care for >10 years, were asked to participate in yearly metabolic screening in February 2008 - January 2011. Among 387 patients who agreed to participate, 275 with DSM- disorder) were included in the study cohort (Schizophrenia cohort). This study was conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from all participants who underwent ajmaline testing10. The Ethics Committee of the Academic Medical Center approved this study.

We compared the patients of the Schizophrenia cohort with age-comparable control persons from the Netherlands Study of Depression and Anxiety (NESDA), an ongoing longitudinal cohort study in the Netherlands11. We used data from 179 individuals, selected randomly from a large sample of control volunteers in the NESDA study without psychiatric disorder, of whom ECG and cardiovascular measurements were made (cardiovascular subsample)12. Since several studies suggested that schizophrenic patients have higher biologic age relative to their calendar age with commensurately increased prevalence of cardiovascular and metabolic disorders13,14, we studied a second control cohort ~20 years older than the study cohort: the Hoorn Study cohort, in which participants, selected randomly from the population registry of the town of Hoorn, have been followed since 1989 15. After excluding 7 participants whose medication data were missing, and 4 participants who used antipsychotics, we studied ECGs of 1168 participants (Hoorn cohort).

Brugada-ECG according to the Brugada Syndrome (BrS) consensus criteria (Figure 431-450 msec, female 451-470 msec) or severe (male >450 msec, female >470 msec)16.

The diagnosis BrS requires typical ST-segment elevations in right precordial ECG leads (type-1 Brugada-ECG) and events suggestive of cardiac arrhythmia or a family history of BrS or SCD. In most BrS patients, the baseline ECG is only suspicious for provocation testing with a sodium channel-blocking drug (Figure 1)10. Accordingly, schizophrenia patients with baseline type-2/3 Brugada-ECG were invited for ajmaline testing in the Academic Medical Center. All patients with type-1 Brugada-ECG (at baseline or after ajmaline testing) underwent DNA screening of SCN5A17. SCN5A Moreover, family screening was offered. In the NESDA and Hoorn cohorts, ajmaline testing could not be performed. Therefore, the prevalences of baseline Brugada-ECG (type-1 or type-2/3) were compared between cohorts.

36 Chapter 3

A B

V1 V1

V2 V2

10.

Risk factors for cardiovascular disease and SCD (previous myocardial infarction, hypertension, hypercholesterolemia, diabetes mellitus, body mass index) were derived (control cohorts)12,15. Drug use during ECG recording or ajmaline testing was derived from patient records (Schizophrenia cohort), questionnaire (Hoorn cohort), or drug container inspection (NESDA cohort). We assessed use of sodium-channel blockers19, QTc-prolonging drugs20, FGA, SGA, antidepressants, cardiovascular drugs (nitrates,

To analyze differences between cohorts in prevalence of co-morbidities and medication variables and analyses of variance for continuous variables. We performed multivariate logistic regression analyses to assess differences between cohorts in Brugada-ECG, Multivariate linear and logistic regression analyses were performed to investigate differences in quantitative ECG parameters, correcting for sex and factors that were sodium-channel blocker use and ECG outcomes, we compared schizophrenic patients

Hoorn (N=275) (N=179) (N=1168) % % % Male gender 195 70.9 66 36.9 517 44.3 Mean age, years (mean, SD) 44.8 9.9 47.7 12.5 66.4 6.7 † Mean BMI (mean, SD) 27.6 5.5 25.4 4.5 26.6 3.5 Smoking 181 65.8 n.a. n.a. 4 1.5 2 1.1 61 5.9 Hypertension 39 14.5 24 13.4 472 40.4 45 16.7 22 12.3 439 37.6 Diabetes mellitus 22 8.2 3 1.7 116 10.1 77 28.0 1 0.6 75 6.4 QT-interval prolonging drugs 177 64.4 2 1.1 44 3.8 Cardiovascular drugs 15 5.5 15 8.4 271 23.2 Lipid lowering drugs 29 10.5 10 5.6 94 8.0 236 85.8 0 0.0 0 0.0 18 6.5 1 0.6 5 0.4 69 25.1 0 0.0 9 0.8 13 4.7 0 0.0 4 0.3 49 17.8 SGA only 147 53.5 40 14.5 39 14.2 Clozapine 91 33.1 47 17.1 Aripiprazol 30 10.9 Risperidon 23 8.4 16 5.8 Haloperidol 14 5.1 Pimozide 12 4.4

38 Chapter 3

with or without sodium-channel blockers, and performed separate analyses with patients from the Schizophrenia cohort who used no sodium-channel blockers during ECG recording, employing the statistical methods described above. All statistics were performed in SPSS (version 20.0 for Mac, Chicago IL, USA).

Table 1 shows patient characteristics. Compared with NESDA controls, schizophrenia patients were slightly younger and more often male. They had higher body mass index (27.6 vs. 25.4, p <0.05), higher prevalence of diabetes mellitus (8.0 vs. 1.7%, p<0.05) and a trend towards higher lipid lowering drug use (10.5 vs. 5.6% p=0.07). (tricyclic antidepressants: 6.5%, selective serotonin receptor inhibitors: 25.1%, amitriptyline (10 mg OD) for neuralgy. Higher antidepressants and antipsychotics use caused higher use of sodium-channel blockers (28.0 vs. 0.6%) and QTc-prolonging drugs (64.4 vs. 1.1%). Compared with Hoorn controls, schizophrenia patients were more often male and ~20 years younger (by design). Prevalence of diabetes mellitus was comparable (8.2 vs. 10.1%), but schizophrenia patients had lower prevalence of previous myocardial infarction, hypertension, and hypercholesterolemia (1.5, 14.2, and 16.7% vs. 5.9, 40.4, and 37.6%, all p<0.05). Accordingly, they used less often cardiovascular drugs (5.5 vs. 23.2%, p<0.05), but had comparable use of lipid lowering drugs. They used 0.8%, other: 4.7 vs. 0.3%, all p<0.05). Antipsychotics were only used by schizophrenia 14% used no antipsychotics. Sodium-channel blockers and QTc-prolonging drugs were used more frequently by schizophrenia patients (28.0 vs. 6.4% and 64.4 vs. 3.8%).

In the Schizophrenia cohort, 32 patients (11.6%) had Brugada-ECG at baseline: one had type-1 Brugada-ECG, while 31 had type-2/3 Brugada-ECG (Table 2). This was Brugada-ECGs, while 2 (1.1%) and 28 (2.4%, both p<0.05 vs. Schizophrenia cohort), respectively, had type-2/3 Brugada-ECG. Figure 2 shows Brugada-ECG prevalences in the study cohorts, and reported prevalences21. In the Schizophrenia cohort, ajmaline testing was offered to the 31 patients with type-2/3 Brugada-ECG, accepted by 23, and found positive in 10. Thus, at least 11 patients had type-1 Brugada-ECG at baseline or after ajmaline testing (4% of all schizophrenia patients, 6 men, age 48.1±10.2 years). One patient had a mutation in SCN5A (c.3956G>T). Family screening was offered to all 11 patients with type-1 Brugada-ECG, but only conducted in 5 relatives of 4 patients, had Brugada-ECG. Supplemental Table s1 shows ECG parameters, co-morbidities, and medication use of all patients with type-1 Brugada-ECG. Four schizophrenia patients with type-2/3 Brugada-ECG who declined ajmaline testing provided additional medical at age <45 years had occurred in the family, and no family members suffered from schizophrenia.

Compared to NESDA controls, schizophrenia patients had higher heart rate (80.8 vs. 61.7 beats per minute, p<0.05) and longer QTc-interval (410.9 vs. 393.1 msec, p<0.05). They also had higher proportion of mild or severe QTc-prolongation (13.1% and 5.8% vs. 3.4% and 0.0%), but not when corrected for relevant covariates (BMI, diabetes mellitus status, use of sodium-channel blockers or QTc-prolonging drugs). Compared to Hoorn controls, schizophrenia patients had higher heart rate (80.8 vs. 68.0 beats per minute, p<0.05), but shorter QRS-interval (91.6 vs. 101.8 msec, p<0.05) and PR-interval (159.8 vs. 174.0 msec, p<0.05). QTc-interval was not different (410.9 vs. 401.9 msec, p=0.251). The proportion of mild or severe QTc-prolongation was higher different when corrected for relevant covariates (BMI, cardiovascular medication use, use of sodium-channel blockers or QTc-prolonging drugs).

To study whether use of sodium-channel blockers affected prevalence of Brugada- ECG and ECG parameters in schizophrenia, we compared schizophrenia patients who used sodium-channel blockers (n=77) to those who did not (n=198) (Table 2). The groups differed in PR and QRS duration (165.8 vs. 157.4 and 94.5 vs. 90.5 msec,

40 Chapter 3

15.6%

15.0%

14.0%

13.0%

12.0%

11.0% Type 1 ECG Type 2 and 3 ECG 10.0% 9.6%

9.0%

8.0%

7.0%

6.0%

5.0%

4.0%

3.0% 2.4%

2.0% 1.3% 1.1% 1.0%

0.0% 0.0% 0.0% 0.02% 0.13% 0.0% Schizophrenia Schizophrenia NESDA Hoorn Europe with Na-channel without Na-chan- (179) (1168) (138,050) blocking drugs nel blocking drugs (77) (198)

Europe21

†§ †§ †§ †§ % 5.1 2.8 28.5 2.4 11.9 16.9 25.3 Hoorn (N=1168) 28 60 33 68.0 101.8 174.0 401.9 % 3.4 0.0 10.3 32.6 1.1 9.8 25.1 (N=179) 2 6 0 61.7 91.0 152.5 393.1 % 9.6 4.5 11.1 17.2 28.7 10.8 20.6 (N=198) 9 19 22 80.0 90.5 157.4 408.8 % 9.1 16.9 18.2 16.0 13.6 22.7 31.9 7 13 14 82.9 94.5 165.8 416.3 % 5.8 11.6 13.1 16.9 11.8 21.5 29.8 32 36 16 80.8 91.6 159.8 410.9 per minute beats Heart rate,

42 Chapter 3 both p<0.05), but were otherwise similar. Prevalence of Brugada-ECG was not different (16.9 vs. 9.6%, p=0.091). To study whether ECG differences between the Schizophrenia and control cohorts may be attributed to use of sodium-channel blockers, we compared ECG parameters between schizophrenia patients who used no sodium-channel blockers with both control cohorts. Compared with NESDA controls, this schizophrenia subset had more Brugada-ECG (9.6 vs 1.1%, p<0.05), higher heart rate (80.0 vs. 61.7, p<0.05), and longer QTc-duration (408.8 vs. 393.1, p<0.05). Mild or severe QTc-prolongation was more prevalent in this schizophrenia subset (11.1 and 4.5% vs. 3.4 and 0.0%, prolonging drugs and diabetes). Similarly, compared with Hoorn controls, this schizophrenia subset had higher prevalence of Brugada-ECG (9.6 vs. 2.4%, p<0.05), higher heart rate, and shorter QRS- and PR-interval. Mild or severe QTc-prolongation was more prevalent in this when corrected for relevant covariates (sex, BMI, QT-prolonging drugs and diabetes).

We found that Brugada-ECG has higher prevalence in schizophrenia patients than in similarly aged or ~20 years older non-schizophrenic controls. Importantly, the prevalence blocking drugs (notably antipsychotics). In contrast, while we also found, in accordance with previous studies7,22,23, that schizophrenia is associated with QTc-prolongation, QTc-prolongation was largely explained by confounding factors, including use of QTc- prolonging (antipsychotics) drugs.

As many as 4% of schizophrenia patients had type-1 Brugada-ECG compared to an estimated prevalence in the general population of 0.05%21. This suggests a higher prevalence of BrS in schizophrenia. This could partly explain the increased SCD risk in schizophrenia. Indeed, the yearly SCD incidence in our Schizophrenia cohort (19/8561 patient years = 0.2%, not shown) is higher than the incidence in the general population in the Netherlands (0.1%)24. Schizophrenia patients with BrS would be vulnerable to the arrhythmia-causing effects of sodium-channel blocking medication, which include many antipsychotics25, particularly in combination with the increased prevalence of cardiovascular risk factors that increase SCD-risk. Still, it must be noted that type-1 Brugada-ECG per se

to ascertain or obtain in schizophrenia patients. For instance, unexplained syncope (a often resulting from the blood pressure lowering effects of antipsychotics. Moreover, we had little opportunity to obtain a family history, as most patients had sparse contact with their relatives, and most available relatives declined investigation. Therefore, although the association between schizophrenia and Brugada-ECG suggested that BrS is more prevalent in schizophrenia, we could not prove this. It could be argued that use of antipsychotics may provoke a Brugada-ECG, thereby facilitating easier detection by ECG analysis. This may especially apply to sodium-channel blockers25, 26. Still, >50% of patients with Brugada-ECG used no sodium-channel blockers (Supplemental Table s1), use of sodium-channel blockers. Furthermore, it is unlikely that use of sodium-channel blockers alone results in Brugada-ECG when an innate factor is absent27. Therefore, the high prevalence of Brugada-ECG found here can probably not be solely attributed to use of sodium-channel blocking antipsychotics. Future studies are required to establish the causes for the increased prevalence of Brugada-ECG (or BrS) in schizophrenia. Emerging evidence indicates that schizophrenia and acute psychosis may impact on cardiac electrophysiology22. Accordingly, genetic studies suggest that the pathobiology of schizophrenia involves various voltage-gated ion channels28-30. Because these proteins also control cardiac electrophysiology, variants in their encoding genes (KCNH2, CACNA1C) may increase arrhythmia and SCD-risk. We did not screen KCNH2, CACNA1C in the patients with Brugada-ECG, but only SCN5A, because SCN5A is the only gene routinely screened at our institution in BrS patients17. Nevertheless, our observations lend support to the more general notion that (nonstructural) brain disease and (electrical) heart disease share common underlying pathomechanisms. For instance, in epilepsy, too, the increased incidence of SCD31 may stem from expression of the same (mutant) ion channel in brain and heart32,33. Furthermore, a recent study34 showed that Neuregulin1, related with both epilepsy and schizophrenia, is also associated with SCD. Autonomic dysregulation may also explain the association between BrS and schizophrenia, being reported in both conditions. However, while reduced vagal tone exists in schizophrenia patients (including those not using antipsychotics)35 increased vagal tone may unmask Brugada-ECG and cause SCD in BrS26.

If proven in future studies that increased prevalence of Brugada-ECG in schizophrenia of excess SCD-risk in schizophrenia patients, especially those using (antipsychotic or non-antipsychotic) medication that blocks cardiac depolarization. The risk for lethal cardiac arrhythmias in BrS is mediated by dysfunctional (impaired) depolarizing ion

44 Chapter 3 channels, notably, the cardiac sodium-channel. BrS-patients, through their innately impaired cardiac sodium-channels (reduced depolarization reserve), are particularly vulnerable to additional sodium-channel block exerted by some antipsychotics (and other drugs). BrS-patients may also be more vulnerable to the depolarization-blocking effects of concomitant conditions36. In particular, acute myocardial ischemia/infarction (more likely to occur in schizophrenic patients, given their higher prevalence of diabetes) impairs cardiac depolarization37. BrS-patients may have particularly increased risk to suffer lethal cardiac arrhythmias during acute myocardial ischemia/infarction. Thus, the combined effects of higher prevalence of Brugada-ECG and concomitant factors that impair cardiac depolarization such as drug use or (risk factors for) ischemic heart disease may partly explain the increased incidence of SCD in schizophrenia. Similarly, we found increased prevalence of QTc-prolongation in schizophrenia patients. While mostly mild and not hazardous per se, this QTc-prolongation may identify individuals at increased risk for lethal cardiac arrhythmias and SCD if concomitant factors that cause further QTc-prolongation (e.g., cardiac hypertrophy or heart failure caused by hypertension or heart disease) are also present. However, QTc- prolongation observed in schizophrenia patients was largely explained by confounding factors, suggesting that QTc-prolongation is not strongly associated with schizophrenia per se, in contrast with the occurrence of Brugada-ECG. We used Bazett’s formula for heart rate correction, since this method is most widely employed and allows for easy comparison with other studies. Although Bazett’s formula may overestimate QTc- duration at higher heart rates when compared to other rate correction methods, it is not resolved which method best captures co-variates of QT duration38.

A major strength of our study is that it involved a large group of schizophrenia patients with data on medication use and relevant co-morbidities during ECG recording. introduced yearly cardiovascular screening among schizophrenic patients enabled us to cohorts. Our study has some limitations. We were unable to perform ajmaline testing in both control cohorts, and 8 schizophrenia patients declined ajmaline testing. Furthermore, data on family history of SCD in the Schizophrenia cohort was limited due prescription.

We found a strongly elevated prevalence of Brugada-ECG in schizophrenia patients. schizophrenia patients, and prudent prescription of sodium-channel blockers, to minimize SCD-risk39.

The authors wish to express their gratitude to Jan Peetoom, internist, who performed the initial ECG analysis in the schizophrenia cohort, Remco Boerman, nurse practitioner, who recorded all ECGs in the schizophrenia cohort, and Irene Beems, who contributed to data collection. The authors greatly appreciate the contributions of Paulien Homma and Loes Bekkers for data collection and data entry, Julien Barc and Leander Beekman for DNA analysis, and thank Patrick Souverein for his help in analyzing medication data.

Vici 918.86.616), the Dutch Medicines Evaluation Board (MEB/CBG), the European Community’s Seventh Framework Programme (FP7, grant 241679, ARITMO), and Biobanking and Biomolecular Research Infrastructure The Netherlands (BBMRI-NL). The infrastructure for the NESDA study (www. nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant 10-000-1002) and supported by participating universities and mental health care organizations (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Research (NIVEL), and Netherlands Institute of Mental Health and Addiction (Trimbos Institute). Additional cardiovascular measurements were supported by the Netherlands Heart Foundation (Grant Number 2006B258). The Hoorn Study was funded by the EMGO Institute VUmc, and has received grants from the Netherlands Diabetes Research Foundation and the Netherlands Organization for Health Research and Development.

none.

46 Chapter 3

1. Tiihonen J, Lönnqvist J, Wahlbeck K, et al. 11-year follow-up of mortality in patients with 2. Saha S, Chant D, McGRath J. A systematic review of mortality in schizophrenia. Arch Gen Psychiatry. 3. De Hert M, Correll CU, Bobes J, et al. Physical illness in patients with severe mental disorders. I. 4. Appleby L, Thomas S, Ferier N, Lewis G, Shaw J, Amos T. Sudden unexplained death in psychiatric 5. Koponen H, Alaräisänen A, Saari K, et al. Schizophrenia and sudden cardiac death: a review. Nord J 6. Myerburg RJ, Castellanos A. Emerging paradigms of the epidemiology and demographics of sudden 7. Timour Q, Frassati D, Descotes J, Chevalier P, Christé G, Chahine M. Sudden death of cardiac origin 8. Ray WA, Chung CP, Murray KT, Hall K, Stein CM. Atypical antipsychotic drugs and the risk of 9. Haddad PM, Anderson IM. Antipsychotic-related QTc prolongation, torsade de pointes and sudden 10. Wilde AA, Antzelevitch C, Borggrefe M, et al. Proposed diagnostic criteria for the Brugada syndrome: of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res. 12. Seldenrijk A, van Hout HP, van Marwijk HW, et al. Carotid atherosclerosis in depression and anxiety: 13. Kirkpatrick B, Messias E, Harvey PD, Fernandez-Egea E, Bowie CR. Is schizophrenia a syndrome of 14. Jeste DV, Wolkowitz OM, Palmer BW. Divergent trajectories of physical, cognitive, and psychosocial 15. De Vegt F, Dekker JM, Jager A, et al. Relation of impaired fasting and postload glucose with incident 16. Committee for Proprietary Medicinal Products. Points to consider: the Assessment of the Potential for QT Interval Prolongation by Non-Cardiovascular Medicinal Products. London, 1997. 17. Hofman N, Tan HL, Alders M, et al. Yield of molecular and clinical testing for arrhythmia syndromes: 18. Chen Q, Kirsch GE, Zhang D, et al. Genetic basis and molecular mechanism for idiopathic ventricular 19. www.brugadadrugs.org, accessed March 2013. 20. www.azcert.org, accessed March 2013. 22. Hatta K, Takahashi T, Nakamura H, Yamashiro H, Yonezawa Y. Prolonged QT interval in acute

23. Suvisaari J, Perälä J, Saarni SI, Kattainen A, Lönnqvist J, Reunanen A. Coronary heart disease and cardiac conduction abnormalities in persons with psychotic disorders in a general population. 24. De Vreede-Swagemakers JJ, Gorgels AP, Dubois-Arbouw WI, et al. Out-of-hospital cardiac arrest in the 1990’s: a population-based study in the Maastricht area on incidence, characteristics and survival. 25. Postema PG, Wolpert C, Amin AS, et al. Drugs and Brugada syndrome patients: review of the literature, recommendations, and an up-to-date website (www.brugadadrugs.org). Heart Rhythm. 26. Meregalli PG, Wilde AAM, Tan HL. Pathophysiological mechanisms of Brugada syndrome: 28. Atalar F, Acuner TT, Cine N, et al. Two four-marker haplotypes on 7q36.1 region indicate that the potassium channel gene HERG1 (KCNH2, Kv11.1) is related to schizophrenia: a case control study. 29. Casamassima F, Hay AC, Benedetti A, Lattanzi L, Cassano GB, Perlis RH. L-type calcium channels 1390. 30. Askland K, Read C, O’Connell C, Moore JH. Ion channels and schizophrenia: a gene set-based 31. Bardai A, Lamberts RJ, Blom MT, et al. Epilepsy is a risk factor for sudden cardiac arrest in the general 32. Surges R, Thijs RD, Tan HL, Sander JW. Sudden unexpected death in epilepsy: risk factors and of a possible pathogenic link between congenital long QT syndrome and epilepsy. Neurology. 34. Huertas-Vazquez A, Teodorescu C, Reinier K, et al. A Common Missense Variant in the Neuregulin1 998. 35. Bär KJ, Wernich K, Boettger S, et al. Relationship between cardiovagal modulation and psychotic state 36. Bardai A, Amin AS, Blom MT, et al. Sudden cardiac arrest associated with use of a non-cardiac drug that reduces cardiac excitability: evidence from bench, bedside, and community. Eur Heart J. 37. Shaw RM, Rudy Y. Electrophysiologic effects of acute myocardial ischemia: a theoretical study of 38. Malik M, Hnatkova K, Kowalski D, Keirns JJ, van Gelderen EM. QT/RR Curvatures in Healthy 39. Schneeweiss S, Avorn J. Antipsychotic agents and sudden cardiac death: how should we manage the

48 Chapter 3 SCD No No No No No degree degree st degree degree st No but 1 No, mood disorder No 1 Yes, No No Yes No No No Aripiprazol, Ipratropium Clozapine Clomipramine Clozapine 172 140 163 127 144 94 99 87 114 120 399 404 405 423 424 61 81 99 80 110 45 36 41 55 61 Male Male Male Male 1 2 3 4 5

nd SCD No No Unknown No 2 Yes, degree No degree degree st No 1 Yes, No No No No Yes Yes No No No , Lorazepam, Nortriptyline, Pantoprozol, Aripiprazol, Pimozide, Aliskiren Bisoprolol, Nortriptyline Nortriptyline Hypertension, Hypertension Hypertension 208 230 216 156 200 180 98 130 142 104 116 112 426 431 432 442 449 450 71 80 69 72 70 113 59 60 56 35 41 40 Male Male 6 7 8 9 10 11

50 Chapter 3

prevalence in 7 European countries.

Type 1 Type 2 and 3 275 0.36 11.27 179 0.00 1.12 1,168 0.00 2.40 Austria 52,097 0.02 0.03 Denmark 18,974 0.00 0.07 3,021 0.00 0.60 36,309 0.03 0.20 Germany 4,149 0.00 0.00 Greece 11,488 0.02 0.20 Italy 12,012 0.02 0.26

21.

52 CHAPTER 4

M.T. Blom, A. Bardai, B.C. van Munster, M. Nieuwland, H. de Jong, D.A. van Hoeijen, A.M. Spanjaart, A. de Boer, S.E. de Rooij, H.L. Tan

PLoS One. 2011;6(9):e23728.

To evaluate changes in QT duration during low-dose haloperidol use, and determine associations between clinical variables and potentially dangerous QT prolongation.

In a retrospective cohort study in a tertiary university teaching hospital in The Netherlands, all 1788 patients receiving haloperidol between 2005 and 2007 were (QTc) was measured before, during and after haloperidol use. Clinical variables before haloperidol use and at the time of each ECG recording were retrieved from hospital charts. Mixed model analysis was used to estimate changes in QT duration. Risk factors for potentially dangerous QT prolongation were estimated by logistic regression analysis.

Patients with normal before-haloperidol QTc duration (male 430ms, female 450ms, female (male 430-450ms, female 450-470ms) or abnormal before-haloperidol QTc the borderline group, and only 9% of patients in the abnormal group obtained abnormal levels. Potentially dangerous QTc prolongation was independently

associated with surgery before haloperidol use (ORadj 34.9, p=0.009) and before-

haloperidol QTc duration (ORadj 0.94, p=0.004).

QTc duration during haloperidol use changes differentially, increasing in patients with normal before-haloperidol QTc duration, but decreasing in patients with prolonged before-haloperidol QTc duration. Shorter before-haloperidol QTc duration and surgery before haloperidol use predict potentially dangerous QTc prolongation.

54 Chapter 4

Haloperidol has for many years been a widely prescribed drug for the treatment of agitation, delirium, acute and chronic psychoses. Haloperidol is a butyrophenon antipsychotic that reduces psychomotor disturbances such as agitation and hallucinations by blocking both dopamine D2 and 1-adrenergic receptors, causing an antidopaminergic effect in the mesocortex and limbic system of the human brain. Although haloperidol is effective in clinical practice, it has been associated with QTc prolongation on the ECG.1-6 As QT prolongation is an established risk factor for potentially life-threatening cardiac ventricular arrhythmias of the type Torsade de Pointes,7-10 clinicians are advised to keep a keen eye on (cardiac) medical history and QT interval changes when treating patients with haloperidol.4,9,11 QT prolongation by haloperidol is ascribed to its blocking effects on the cardiac potassium channel hERG and was shown in various clinical studies.12-15 Most clinical studies were performed in selected healthy populations, or in sick patients with high intravenous doses.6,16 In clinical practice, however, haloperidol is mostly prescribed orally at low doses to elderly patients with multiple co-morbidities. Thus, clinicians are often faced with the need to prescribe haloperidol to patients with other potential causes of QT prolongation, including concomitant medication use and cardiac pathology (e.g., heart failure). Yet, studies on QT prolongation during low-dose haloperidol use in such high-risk patients are lacking. In this study, we examined whether there is an association between haloperidol use and QT duration changes in a common population of elderly hospitalized patients with multiple morbidities and co-medications. By analyzing the ECGs of these patients before, during and after haloperidol use, we studied whether QT duration changed during the in-hospital use of haloperidol, taking other clinical factors into account. Also, by comparing patient characteristics and (acute) medical condition before haloperidol haloperidol use. In particular, given that hospitalized elderly patients are often given haloperidol because they are agitated during an acute phase response, after surgery, or during acute hospitalization,17,18 we studied whether these factors may impact on a patient’s QTc response to haloperidol.

In this retrospective cohort study, we used a database of all 1788 in-hospital patients for whom haloperidol was prescribed between 2005 and 2007 during admission at the Academic Medical Centre, a university teaching hospital in Amsterdam. We analyzed all patients (N=237) of whom three ECG recordings were made before, during and after haloperidol prescription, i.e., i) between two weeks and one day before start of

haloperidol, ii) between one hour after start of haloperidol and 24h after the last dose of haloperidol, and iii) >one week after the last dose of haloperidol.19,20 The study was conducted according to the principles expressed in the Declaration of Helsinki. All data were retrieved from the hospital’s databases (where data of routine clinical care measures are stored) and were analysed anonymously. The medical ethics committee of the Academic Medical Center (Amsterdam) waived the need for approval and informed consent.

ECG analysis was conducted while the researchers were blinded for haloperidol status. QT durations were measured by hand and corrected for heart rate using Bazett’s formula (QTc). All three measurements (before-haloperidol ECG, during-haloperidol ECG, and after-haloperidol ECG) were analyzed in the same lead for each patient, based on the best RR intervals in the recording was used for rate correction. Patients who had ECGs with multiple ventricular extrasystoles, pacemaker beats, left or right bundle branch block, or excluded from further analysis. Of each patient, we obtained the following information from the hospital records: gender, age, medical history, medical status during hospital admission before haloperidol use (e.g., reason of admission, any surgery for which general anesthesia was given within 3 days before haloperidol use), actual administration and given dose of haloperidol, and the use of other QT prolonging drugs (CERT list 121) within the 72 hours before each ECG recording. We collected data of electrolytes measured within 48 hour before or after each ECG recording, using the measurement closest to the moment of recording, or within 24 hours of the recording moment in serum CRP level >100mg/l, or leukocyte count >100*10E9/L. To analyze whether the based on the European Society of Cardiology Guidelines [22]: 1) Normal (male 430 ms, female 450 ms), 2) Borderline (male 431-450 ms, female 451-470 ms), and 3) Abnormal (male >450 ms, female >470 ms).

Statistical Package for the Social Sciences (SPSS, version 16.0 for Mac) was used for data analysis. Changes in mean QTc duration before, during and after haloperidol use, and clinical factors. We evaluated associations with patient variables, haloperidol dose, co-medication, medical history, medical status during hospital admission, electrolyte

56 Chapter 4 levels, and ECG parameters. Factors that were univariately associated (p<0.25) with duration while accounting for relevant variables (backward selection with p<0.05). To we performed a second analysis in which we did not use the measured QTc duration as the dependent variable, but the QTc duration minus excess QRS duration (i.e., QRS width minus 110 ms). Risk factors for potentially dangerous QTc prolongation were analyzed using increase in QTc duration by >50 ms or to >500 ms, or the occurrence of Torsade de Pointes.23 Factors that were associated with a p<0.05 in the univariate analyses were entered into the multivariate models. Effect sizes were expressed in odds ratios (OR) as mean±standard deviation (SD), unless otherwise indicated.

From a total cohort of 1788 patients for whom haloperidol was prescribed between

p<0.05 level.

this yielded 237 patients (mean age 70 years, male 66%). Since the prescription of whether haloperidol was actually administered, and whether the ECG was recorded during actual haloperidol use. We excluded 81 patients of whom we could not establish this (Figure 1). We further excluded 59 patients because of ECG abnormalities that hindered a valid and reliable measurement of QT duration, yielding a study population of serum levels of sodium or potassium, we imputed the mean value. Table 1 presents baseline characteristics of the study population. Thirty to 41% of patients in the three subgroups underwent surgery and 70 – 78% had signs interval (p=0.049), but with none of the other measured conditions. Dose of haloperidol was 2,6 mg per day on average, (range: 0.5–10 mg per day), and was given orally in 75% of patients and intravenously in 25% of patients. When we studied mean QTc duration of our overall study group, we found no change in QTc duration upon haloperidol use (Figure 2) and a decrease (-12.1±48.9 ms

58 Chapter 4 4 (17.4) 3 (13.0) 7 (30.4) 3 (13.3) 3.1 (2.1) 3.9 (0.6) 21 (91.3) 10 (43.5) 14 (60.9) 18 (78.3) 67.4 (13.5) 83.1 (24.0) 99.0 (13.7) 140.0 (2.9) 487.0 (43.0) Abnormal (N=23) 0 (0) 4 (23.5) 9 (53.9) 3 (17.6) 9 (52.9) 7 (41.2) 2.3 (1.9) 4.3 (0.5) 12 (70.6) 12 (78.3) 66.1 (15.8) 98.0 (31.0) 91.8 (16.2) 140.5 (3.9) 444.4 (11.3) Borderline (N=17) Borderline 5 (8.8) 3 (5.3) (N=57) Normal 2.5 (2.3) 4.1 (0.4) 29 (50.9) 12 (21.1) 22 (38.6) 31 (54.4) 22 (38.6) 40 (70.2) 68.2 (14.5) 85.0 (23.9) 91.3 (14.9) 139.5 (3.8) 408.8 (24.6) 6 (6.2) 2.6 (2.2) 4.1 (0.5) 62 (63.9) 20 (20.6) 41 (42.3) 11 (11.3) 54 (55.7) 36 (37.1) 70 (72.2) 67.7 (14.3) 86.8 (25.6) 93.2 (15.1) 139.8 (3.6) 433.6 (43.2) mean (SD) years, Age, mean (SD) per minute, beats Heart rate, mmol/l, mean (SD) Serum sodium level, mmol/l, mean (SD) level, Serum potassium n (%) Diabetes, n (%) Hypertension,

by before-haloperidol QTc duration (normal, borderline and abnormal), we found increase 23.1±45.5 ms [p<0.001], Figure 1). Twenty-three percent of these patients rose to abnormal levels. In contrast, the subgroups with borderline and abnormal use (borderline: -14.6±26.2 ms [p=0.035], and abnormal: -45.9±55.5 ms [p=0.001]). Twenty-three percent of patients in the borderline group, and nine percent of patients in the abnormal group obtained abnormal levels. After discontinuation of haloperidol, analysis when comparing QTc durations before and during the use of haloperidol in the normal and abnormal subgroups, after adjusting for relevant covariates (signs of infection and surgery before haloperidol use). Changes in QTc duration after analysis with QRS-duration subtracted from the measured QTc duration did not yield different results. In total, 16 patients (16%) developed potentially dangerous QTc prolongation during haloperidol use, 15 (94%) of whom had a normal before-haloperidol QTc duration. In only 2 patients QTc duration increased to >500 ms. No episodes of Torsade de Pointes were found. We analyzed factors associated with dangerous QTc prolongation (Table 2). haloperidol QTc duration, heart rate, surgery before haloperidol use, and signs of Table 2. Surgery before haloperidol use was strongly associated with increased risk of potentially dangerous QTc prolongation (OR 34.9 [p=0.009]), as was male gender haloperidol QTc duration (OR 0.94 [p=0.004]), faster before-haloperidol heart rate In contrast, dose of haloperidol, mode of administration, and presence of cardiovascular risk factors were not associated with potentially dangerous QTc interval prolongation (only heart failure and ischemic heart disease increased the risk, though not statistically

60 Chapter 4 0.055 0.004 0.029 0.009 0.147 p-value 95% CI 0.8-1.0 0.0-1.1 1.0-60.5 0.91-0.98 2.4-506.2 7.6 0.9 0.1 0.94 34.9 0.046 0.380 0.682 0.001 0.053 0.004 0.090 0.860 0.876 0.387 0.673 0.315 0.548 0.008 0.268 <0.001 p-value 95% CI 1.0-1.1 0.7-1.2 0.0-1.0 0.9-1.0 1.0-1.1 0.9-1.2 0.3-2.7 0.1-2.4 0.3-2.4 0.5-9.0 0.5-4.2 0.1-0.7 Univariate 1.0-22.6 2.8-41.9 0.5-16.5 0.95-0.99 4.8 1.0 0.9 0.1 0.9 1.0 1.0 0.9 0.5 0.8 2.1 1.4 0.2 2.7 n.a. 0.97 10.9 8 (9.9) 4 (4.9) 2.6 (2.2) 4.1 (0.5) 17 (100) 48 (59.3) 42 (73.7) 22 (95.7) 14 (17.3) 35 (43.2) 44 (54.3) 23 (28.4) 63 (77.8) No (n=81) 67.1 (15.1) 90.5 (25.7) 92.0 (14.9) 139.7 (3.9) 439.8 (43.4) 0 (0.0) 1 (4.3) 2 (12.5) 6 (37.5) 3 (18.8) 7 (43.8) 2 (12.5) 2.4 (2.2) 4.1 (0.4) 14 (87.5) 15 (26.3) 10 (62.5) 13 (81.2) 70.5 (9.7) Yes (n=16) Yes 68.4 (15.3) 99.1 (14.9) 139.9 (3.2) 401.8 (24.4) Normal QTc, n (%) Normal QTc, n (%) QTc, Borderline n (%) Abnormal QTc, Male, n (%) mean (SD) years, Age per min., mean (SD) beats Heart rate, mmol/L, mean (SD) Serum sodium level, mmol/L, mean (SD) level, Serum potassium n (%) Diabetes, n (%) Hypertension,

In this real-life study among hospitalized elderly patients with multiple co-morbidities, we found that substantial QTc prolongation upon low-dose haloperidol use occurred predominantly in patients with normal before-haloperidol QTc duration. Conversely, in most patients with borderline or abnormal before-haloperidol QTc duration, an unexpected QTc shortening occurred. Importantly, 94% of patients with potentially dangerous QTc prolongation had a before-haloperidol QTc duration in the normal range. A rise in QTc duration to potentially dangerous levels was not only associated with before-haloperidol QTc duration, but also with surgery (increased risk) and cardiovascular risk factors are reported to have a higher risk of QTc prolongation,24,25 we It is unlikely that QTc shortening in patients with a prolonged QTc interval prior to haloperidol use is due to the effects of haloperidol per se. A more plausible explanation might be that changes in the underlying condition occurred during haloperidol use, and that these changes caused QTc duration to shorten, despite haloperidol use. For instance, although speculative, it is possible that the complex effects of acute illness (both cardiac and non-cardiac), acute phase response, or poor general condition (e.g., post-operative patients), contributed to QTc prolongation through as yet unknown mechanisms. In support of the view that acute illness may cause QTc prolongation, average QTc durations of all three subgroups were normal after haloperidol use, i.e., when the acute illness had subsided. Unexpectedly, potentially dangerous QTc-prolongation was associated with with a decreased hERG function (e.g., patients using QT prolonging drugs).17,26 Proposed mechanisms include the effects of circulating currents that determine QT duration, and increased drug-mediated block of hERG channels at high temperature, as demonstrated for erythromycin.27,28 It is conceivable the hERG channel, thus causing clinically dangerous QTc prolongation. Yet, we associated with potentially dangerous QTc prolongation during haloperidol use. One between the before-haloperidol state and the during-haloperidol state may have been the net result would be QT shortening (or lack of further QT prolongation).

62 Chapter 4

A limitation of our study is that the patients had different causes and stages indication for haloperidol prescription (e.g., agitation) may also be associated with QTc prolongation, independent of haloperidol use. This may be the case in post-operative patients, who, in our population, are at increased risk to develop potentially dangerous QTc prolongation. A stress response has been shown to induce QTc prolongation,29 whether the various indications for haloperidol prescription in elderly hospitalized patients are associated with changes in QTc duration, and to elucidate the underlying mechanisms of these changes. QTc duration upon haloperidol use, while taking co-morbidities that play an important role in a real-life clinical setting into account. A major limitation of our study, however, is its retrospective design. By limiting our study to patients of whom ECG-recordings were available before, during and after haloperidol use, we excluded patients who were discharged or died during haloperidol use. Moreover, we were biased towards patients in whom multiple ECG recordings were made, thereby probably introducing an overrepresentation of patients with cardiac disease. This might explain the large proportion of male patients in our study population. who receive in-hospital low-dose haloperidol may not be necessary, as QTc prolongation occurred mostly in patients with normal before-haloperidol QTc duration, while QTc duration generally shortened in those with before-haloperidol QTc prolongation. Overall, potentially dangerous QTc prolongation occurred only in few patients, and of our study (in particular, the differential effects between subgroups) as indication that the real-life clinical effects of haloperidol are not well established. We therefore conclude that more research (larger prospective studies) is needed to allow for rational clinical decision-making on the cardiac safe use of haloperidol.

We thank Mrs J. de Koning-Popma and Ms P.C. Homma for their help in data acquisition and Dr M.W. Tanck for his advice on statistical matters.

Vici 918.86.616), the Dutch Medicines Evaluation Board (MEB/CBG), and the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement nr. 241679 - the ARITMO Mozaiek 017.003.084). Both grants are unrestricted. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

64 Chapter 4

1. Stepkovitch K, Heagle Bahn C, Gupta R. Low-dose haloperidol-associated QTc prolongation. J Am Geriatr Soc 2. Straus SM, Sturkenboom MC, Bleumink GS, et al. Non-cardiac QTc-prolonging drugs and the risk of sudden cardiac death. Eur Heart J 3. Ray WA, Meredith S, Thapa PB, et al. Antipsychotics and the risk of sudden cardiac death. Arch Gen Psychiatry 4. Stollberger C, Huber JO, Finsterer J. Antipsychotic drugs and QT prolongation. Int Clin Psychopharmacol. 5. Roden DM. Drug-induced prolongation of the QT interval. New Engl J Med. 6. Hatta K, Takahashi T, Nakamura H, et al. The association between intravenous haloperidol and prolonged QT interval. J Clin Psychopharmacol 7. Kirchhof P, Franz MR, Bardai A, Wilde AM. Giant T-U waves precede torsades de pointes in long QT syndrome: a systematic electrocardiographic analysis in patients with acquired and congenital QT prolongation. J Am Coll Cardiol. 8. Smits JP, Blom MT, Wilde AA, Tan HL. Cardiac sodium channels and inherited electrophysiologic disorders: a pharmacogenetic overview. Expert Opin Pharmacother. 9. De Bruin MLD, Langendijk PNJ, Koopmans RP, et al. In-hospital cardiac arrest is associated with use of non-antiarrhythmic QTc-prolonging drugs. Br J Clin Pharmacol. 10. Straus SM, Bleumink GS, Dieleman JP, et al. Antipsychotics and the risk of sudden cardiac death. Arch Intern Med 11. American Psychiatric Association, APA Practice Guidelines. APA Guidance on the Use of Antipsychotic ny.us/omhweb/advisories/adult_antipsychotic_use.html. Accessed 2011 May 17th. 12. Tan HL, Hou CJY, Lauer MR, Sung RJ. Electrophysiologic mechanisms of the Long QT interval syndromes and torsade de pointes. Intern Med 13. Sanguinetti MC, Tristani-Firouzi M. HERG potassium channels and cardiac arrhythmia. Natur.e 14. Mortl D, Agneter E, Krivanek P, Koppatz K, Todt H. Dual rate-dependent cardiac electrophysiologic effects of haloperidol: slowing of intraventricular conduction and lengthening of repolarization. J Cardiovasc Pharmacol. monophasic action potential duration in anesthetized dogs. Chest. 16. Hassaballa AH, Balk RA. Torsade de Pointes associated with the administration of intravenous haloperidol. Am J Therapeutics. 17. Amin AS, Herfst LJ, Delisle BP, et al. Fever-induced QTc prolongation and ventricular arrhythmias in individuals with type 2 congenital long QT syndrome. J Clin Invest. 18. Burashnikov A, Shimizu W, Antzelevitch C. Fever accentuates transmural dispersion of repolarization and facilitates development of early afterdepolarizations and torsade de pointes under Long-QT conditions. Circ Arrhythm Electrophysiol. 19. Brunton LL, Chabner BA, Knollmann BC. Goodman & Gilman’s The Pharmacological Basis of Therapeutics, 12th edition, online edition, 2011. 20. Desai M, Tanus-Santos JE, Li L, et al. Pharmacokinetics and QT interval pharmacodynamics of oral haloperidol in poor and extensive metabolizers of CYP2D6. Pharmacogenomics J

21. Arizona Center for Education and Research on Therapeutics website. Available: http://www.azcert. org/. Accessed 2011 January 12. 22. Committee for Proprietary Medicinal Products. The Assessment of the Potential for QT Interval Prolongation by Non-Cardiovascular Medicinal Products. London, 1997. 23. Roden, D. Drug-Induced Prolongation of the QT Interval. N Engl J Med. 24. Dekker JM, Schouten EG, Klootwijk P, Pool J, Kromhout D. Association between QT interval and coronary heart disease in middle-aged and elderly men. The Zutphen Study. Circulation. 25. Brown DW, Giles WH, Greenlund KJ, Valdez R, Croft JB. Impaired fasting glucose, diabetes mellitus, and cardiovascular disease risk factors are associated with prolonged QTc duration. Results from the Third National Health and Nutrition Examination Survey. J Cardiovasc Risk 26. Wang J, Wang H, Zhang Y, et al. Impairment of HERG K(+) channel function by tumor necrosis factor-alpha: role of reactive oxygen species as a mediator. J Biol Chem 27. Guo J, Zhan S, Lees-Miller JP, Teng G, Duff HJ. Exaggerated block of hERG (KCNH2) and prolongation of action potential duration by erythromycin at temperatures between 37 degrees C and 42 degrees C. Heart Rhythm. 28. de Rooij SE, van Munster BC, Korevaar JC, Levi M. Cytokines and acute phase repsons in delirium. J Psychosom Res. 29. Andrássy G, Szabo A, Ferencz G, et al. Mental stress may induce QT-interval prolongation and T-wave notching. Ann Noninvasive Electrocardiol.

66 CHAPTER 5

M. T. Blom*, S. Jansen*, A. de Jonghe, B.C. van Munster, A. de Boer, S.E. de Rooij, H.L. Tan, N. van der Velde * These authors contributed equally

Accepted with revision in Journal of Nutrition, Health and Aging, 2014

Haloperidol may prolong ECG QTc-duration but is often prescribed perioperatively to hip-fracture patients. We aimed to determine (1) how QTc-duration changes (3) which clinical variables are associated with potentially dangerous perioperative

Prospective cohort study in tertiary university teaching hospital. Participants were patients enrolled in a randomized controlled clinical trial of melatonin versus placebo on occurrence of delirium in hip-fracture patients.

Data from ECGs made before and after hip surgery (1-3 days and/or 4-6 days post-surgery) was analyzed. QTc-duration was measured by hand, blinded for haloperidol and pre/post-surgery status. Clinical variables were measured at baseline. Mixed model analysis was used to estimate changes in QTc-duration. Risk-factors for PD-QTc were estimated by logistic regression analysis.

haloperidol. Patients with normal pre-surgery QTc-duration (male 430 ms, female pre-surgery QTc-duration (male >450ms, female >470 ms). Haloperidol-use did (n=8) was not associated with any of the measured risk-factors.

QTc-duration changed differentially, increasing in patients with normal, but decreasing in patients with abnormal baseline QTc-duration. PD-QTc was not associated with haloperidol-use or other risk-factors. Low-dose oral haloperidol did not affect perioperative QTc-interval.

68 Chapter 5

Haloperidol has for many years been a widely prescribed drug for the treatment of agitation, delirium, and psychoses. Although it is highly effective for these conditions, its use is hampered by concerns that it may cause lethal cardiac arrhythmias (Torsade de Pointes, TdP).1-4 Such arrhythmias are caused by haloperidol’s ability to block the 5-8 and heralded by QTc-prolongation on the ECG.9-14 Accordingly, clinicians are advised to monitor QTc-duration during haloperidol use.3,12,15 is relevant. Some risk factors are well-known, e.g., concomitant QT-prolonging drug use, and hypokalemia. We recently found indications in a cohort of older, hospitalized patients that other lesser-known stressors may have such great impact on QTc-duration that they mask the potential QTc prolonging effects of haloperidol.16 In that cohort with multiple morbidities and co-medications, surgery was the factor most strongly associated with post-haloperidol QTc-prolongation. It is clinically relevant to establish in detail how surgery affects QTc-duration in this older, comorbid patient category, and how haloperidol use impacts on the course of perioperative QTc-duration, because these patients often require haloperidol for delirium control.17 In the present follow-up study, we therefore aimed to address the following questions: (1) how does QTc-duration change perioperatively? (2) does low-dose potentially dangerous QTc-prolongation?

For the current study we used data of patients from a randomized trial (N=452) of melatonin versus placebo on the occurrence of delirium in hip fracture patients (Trial registration number NTR1576).18 This trial was carried out between November 2008 and May 2012 in one academic hospital and one non-academic hospital. Patients were selected for the present study if a pre-operative and at least one post-operative ECG were made. In all trial participants, a pre-operative ECG (T0) was performed. Prospective gathering of post-operative ECGs started in December 2011 at 1-3 days (T1) and/or 4-6 days (T2). To enlarge our study population, enrolled patients from the academic hospital before December 2011 were also included (retrospectively) if clinical post-operative ECGs had been performed. The study was conducted according to the principles expressed in the Declaration of Helsinki. All participating patients were asked for written informed consent if additional investigations were made, and all data were analyzed anonymously.

The medical ethics committee of the Academic Medical Center (Amsterdam, The Netherlands) approved the study.

All ECGs were analyzed separately by two researchers (MB and SJ), who were blinded for pre/post-surgery and haloperidol status. QT durations were measured by hand and corrected for heart rate using Bazett’s formula (QTc).19 All ECG measurements at T0, T1, and/or T2 were analyzed in the same lead for each patient, based on the best readable recording. If there was >10 msec difference in QTc-duration between the measurements of both researchers, the QT-interval was re-evaluated together. In case of was used for rate correction. Patients who had pacemaker beats or whose QTc-duration further analysis. surgery QTc-duration, based on the European Society of Cardiology Guidelines20: 1) Normal (male 430 ms, female 450 ms), 2) Borderline (male 431-450 ms, female 451-470 ms), and 3) Abnormal (male >450 ms, female >470 ms). Potentially dangerous >50 ms or to a QTc-duration of >500 ms.13

Of all patients, haloperidol status was determined at each ECG recording. Since the time interval between the ECG recordings was relatively short, and the elimination half-life of haloperidol long,21 all ECGs were considered as affected by haloperidol once haloperidol was administered (T0 and/or T1). The same applied for concomitant QT- prolonging medication.

Of each patient, we collected age, sex, Charlson comorbidity index (CCI),22 use of of delirium, assessed daily, the DSM-IV criteria were used.23 The use of other QT prolonging drugs24 at baseline, leukocyte count, and serum sodium, potassium and CRP level (measured within 72 hours before surgery) were derived from hospital records. >100mg/l, or leukocyte count >10*10E9/L.

70 Chapter 5

We used conventional ANOVA and chi-square statistics to evaluate differences per group (using Pearson and Fisher’s Exact test where appropriate). In non-normally distributed data, we used Mann-Whitney U and Kruskal-Wallis non-parametric tests. The course of perioperative QTc-duration (T0-T1-T2), and differences in this course changes in relevant clinical factors. We evaluated associations with age, sex, haloperidol delirium status, serum sodium and potassium levels, and ECG parameters. As this study was set up as a substudy of an RCT studying the effect of melatonin on the incidence of delirium in patients admitted for hip-fracture, we also evaluated whether treatment arm was associated with QTc-duration. Factors that were univariately associated (p<0.20) with before-surgery QTc- duration, perioperative change in QTc-duration (T0-T1 or T0-T2), or baseline subgroup for relevant variables (backward selection with p<0.05). To test the possibility that an analysis in which we used the QTc-duration minus excess QRS-duration (i.e., QRS width minus 110 ms) as the dependent variable. duration (T0-T1-T2), we compared patients who used no haloperidol at T0 but had used haloperidol shortly before T1, with patients who did not receive haloperidol at any time during our study period. The same methods as described above were deployed duration. Risk factors for potentially dangerous QTc-prolongation were analyzed using logistic regression. If factors were associated with a p<0.20 in the univariate analyses they were entered into a multivariate model. Effect sizes were expressed in odds ratios ±standard deviation (SD), unless otherwise indicated. All version 20.0, SPSS Inc.).

Between November 2008 and May 2012, 452 participants were enrolled in the trial (58 with prospective after-surgery ECG). In seven of these 96 participants, ECG the RCT was not associated with QTc-duration (p=0.772).

Table 1 presents baseline characteristics of the study population. Mean age was 83.8±13.5 years, and 24% was male. Thirty-seven percent of patients suffered a of patients received haloperidol in the perioperative period at median daily dose of 1 mg (Interquartile range 0.78-1.29), all administered orally. Six percent used other QTc (n=3). In our overall study population, we found no difference in QTc-duration between T0, T1 and T2 (Figure 2, panel A). However, patients with normal QTc-

72 Chapter 5 0.157 0.326 0.621 0.001 1.000 0.013 0.643 0.301 0.366 0.446 0.416 0.068 0.291 <0.001 p-value (n=27) 1 (1, 2) 4 (13.5) 8 (29.6) 3 (11.1) 4.0 (0.6) 10 (37.0) 12 (44.4) 11 (40.7) 10 (37.0) 86.5 (7.4) Abnormal 76.6 (14.2) 140.9 (4.5) 481.3 (14.9) 105.7 (24.5) 1 (5.0) (n=20) 4 (20.0) 2 (10.0) 2 (10.0) 7 (35.0) 5 (25.0) 7 (35.0) 4.0 (0.5) 1 (0.25, 2) Borderline 80.5 (24.1) 76.6 (11.7) 93.4 (17.0) 139.0 (4.4) 455.8 (12.0) 2 (4.8) 1 (2.4) (n=42) 1 (0, 2) Normal 7 (16.7) 6 (14.3) 4.1 (0.6) 20 (47.6) 17 (40.5) 21 (50.0) 83.7 (8.7) 79.6 (16.3) 87.1 (14.7) 138.3 (4.4) 416.5 (20.3) 5 (5.6) (n=89) 1 (0, 2) 4.0 (0.5) 21 (23.6) 12 (13.5) 12 (13.5) 39 (43.8) 33 (37.1) 38 (42.7) 83.8 (13.5) 78.0 (14.7) 94.2 (20.1) 139.2 (4.5) 445.0 (33.3) Male, n (%) mean (SD) years, Age, mean (SD) per minute, beats Heart rate, n (%) Haloperidol prescribed, period, n (%) Delirium during study mmol/l, mean (SD) Serum sodium level, mmol/l, mean (SD) level, Serum potassium

borderline prolonged QTc-duration at T0 exhibited no change in QTc-duration post- surgery.

Overall, after adjusting for relevant covariates (sex and QRS-duration), the interaction between baseline QTc-subgroup and time of ECG measurement (T0, T1 and T2) was the analysis with QRS-duration subtracted from the measured QTc-duration did not yield different results. Also, the course of perioperative QTc-changes was not affected by the nature of the source population (ECG made for research or clinical purposes).

Supplemental table s1 shows baseline characteristics according to haloperidol use at T1. We excluded patients who used haloperidol at T0, and those who started haloperidol use at T2 (instead of T1), resulting in n=76. Patients receiving haloperidol were older (88 vs. 81 years, p=0.046), and had, as expected, more often a delirium (70.8 vs. 19.2%, p<0.001). Mean QTc-duration did not differ per haloperidol status for ECGs made at different for patients with or without haloperidol use at T2, this difference was not interval (normal, borderline, abnormal) the pattern of perioperative QTc-change was not different for patients with or without haloperidol use. However, at T2, numbers per Mixed models analysis showed that, after adjusting for relevant covariates different between patients with or without haloperidol use (p=0.351). Use of other of QTc-duration.

Eight patients (9%) developed potentially dangerous QTc-prolongation post-surgery, QTc-duration increased to >500 ms. No TdP episodes were documented. We analyzed factors associated with dangerous QTc-prolongation (Table 2). No risk factors showed performed.

74 Chapter 5

0.450 0.493 0.395 0.701 0.571 0.476 0.804 0.330 0.909 0.666 0.979 0.221 0.617 0.417 0.766 0.663 p-value 95% CI 0.9 – 1.1 0.2 – 9.4 0.3 – 8.7 0.1 – 8.1 0.7 – 1.8 0.2 – 4.6 0.3 – 4.6 0.3 – 5.9 0.05 – 3.8 0.4 – 13.4 0.6 – 11.4 0.8 – 1.09 0.99 – 1.03 0.98 – 1.04 0.96 – 1.03 0.4 1.0 1.4 1.6 2.4 0.9 1.1 2.6 1.2 1.4 1.01 0.98 1.00 1.02 0.94 5 (6%) 1.0 (0,2) 4.0 (0.5) 20 (25%) 39 (48%) 18 (22%) 24 (30%) 10 (12%) 11 (14%) 30 (37%) 32 (40%) 34 (42%) No (n= 81) 83.5 (13.8) 78.4 (15.1) 94.4 (20.5) 139.4 (4.5) 444.0 (32.0) 0 (0%) 1 (13%) 3 (38%) 2 (25%) 3 (38%) 2 (25%) 1 (13%) 3 (38%) 5 (63%) 4 (50%) 1.0 (1,3) 4.1 (0.6) Yes (n=8) Yes 86.7 (10.0) 74.5 (10.1) 92.5 (16.2) 138.0 (5.0) 454.5 (46.2) Normal, n (%) n (%) Borderline, Abnormal, n (%) Male, n (%) mean (SD) years, Age, mean (SD) per minute, beats Heart rate, period, n (%) Delirium during study n (%) (T1 or T2) Haloperidol prescribed, drugs, n (%) (T1 or T2) QT-prolonging mmol/l, mean (SD) Serum sodium level, mmol/l, mean (SD) level, Serum potassium

76 Chapter 5

In this real-life study among old-age hospitalized patients with multiple co-morbidities undergoing hip surgery, perioperative QTc-durations changed differentially. Substantial QTc-prolongation occurred predominantly in patients with normal before-surgery QTc-duration. Conversely, in most patients with abnormal before-surgery QTc- duration, QTc-shortening occurred. Changes in perioperative QTc-duration were not

There are few studies that have analyzed QTc-intervals in the perioperative period. Nagele et al.25 found that 80% of patients undergoing non-cardiac surgery experienced 23 ms), whereas 18% of patients had a shortening of QTc-interval. However, age and gender distribution was different (with the present study concerning older patients), good comparison with our results. The results of our study are, however, in line with the results of our previous (retrospective) study in which we analyzed QTc-intervals of patients before, during and after low-dose haloperidol use.16 In that study, we found that the course of change in QTc-duration depends primarily on baseline QTc-duration. Surgery and baseline QTc- interval were independently associated with potentially dangerous QTc-prolongation. In the present prospective study, too, we found that (perioperative) QTc-intervals changed differentially, increasing in patients with normal pre-surgery QTc-intervals and decreasing in patients with abnormal pre-surgery QTc-intervals. A possible explanation for these observations is the ‘regression-to-the-mean’ phenomenon,26 as, in the course of time, shorter QTc-intervals increased and longer intervals normalized. This phenomenon is known to occur in randomly distributed data. However, since the QTc-interval is generally not regarded as a random value, we do not expect that our results can be solely attributed to regression-to-the-mean, although we do expect intrinsic variability in QTc-interval to play an important role at different times during the day should be used when studying QTc-changes.27 In clinical practice, however, decisions regarding the QTc interval are usually based on a measurement from a single ECG recording. Arguably, the importance of intrinsic QTc- within observational study designs.

In the present study population, use of low-dose oral haloperidol was not associated with perioperative QTc-interval prolongation. Although in line with our previous results, this contradicts results of other studies, which found that QTc-interval increases upon haloperidol administration. However, most of these studies were carried out with high doses of intravenous haloperidol,14,28 whereas current guidelines of delirium treatment advise to use haloperidol at the lowest possible dose.29 Previous studies that healthy volunteers or psychiatric patients, and show contradicting evidence. Desai et increase of 13 ms) upon administration of a single oral dose of haloperidol (10 mg) than upon placebo.30 Miceli et al. found that QTc-interval in haloperidol-treated patients increased with dose in patients with schizophrenia or schizoaffective disorder. 31 However, maximum increase of QTc-interval (7.2 ms) was found at oral doses of 30 mg/day, which is considerably higher than the dose of haloperidol that our study subjects received. In none of the study subjects did QTc-duration rise to >450 ms during haloperidol use. Furthermore, although these studies show that QTc-duration increases upon (increasing doses of) oral haloperidol, maximum increase was <15 ms. The clinical implication of this increase is disputable. By contrast, Fulop et al. found daily) in forty patients with Tourette’s syndrome.32 Interestingly, the differential pattern was still visible when patients without haloperidol use were compared with those with smaller than of other factors that determine the observed pattern.

In the present study that addresses an older population, potentially dangerous QTc- prolongation was rare. Also, among those who showed potentially dangerous QTc- previously found surgery before haloperidol use to be a strong risk factor for potentially risk factors33 to be associated with a rise in QTc-duration to potentially dangerous levels. Remarkably, neither was the use of haloperidol or other QTc-prolonging medication. Baseline increased QTc-interval is generally regarded as a contra-indication for the administration of QTc-prolonging drugs, such as haloperidol. The results of our studies do not support this recommendation for low dose oral prescriptions of haloperidol.

78 Chapter 5

The present study has several strengths. Our study population was a representative sample of prospectively included older hip-fracture patients with several co-morbidities. This allowed for a realistic assessment of risk of QTc-prolongation in this frail population, in a situation where haloperidol is often prescribed. Patient inclusion was part of a clinical trial, which allowed for comprehensive and reliable ascertainment of clinical information. A broad spectrum of clinical parameters was analyzed along with medication use. All QTc-intervals were measured manually by two independent researchers who were blinded for pre/post surgery and haloperidol status, which ensured reliable outcome measurements. Some limitations must be mentioned, however. The observational design of our study limits the interpretation of our results. As clinicians are currently discouraged to prescribe QTc-prolonging drugs to patients with a prolonged QTc-interval at baseline, a possible inclusion bias may have occurred. However, we observed no baseline differences in QTc duration between patients who were or were not prescribed haloperidol. Also, our patient group consisted of older persons in need of a traumatic hip-surgery, and our results may not be representative for other (older) patient groups. Lastly, we included 38 patients of whom the post-operative ECGs were made on indication instead of for not different in any of the studied variables, and source population did not affect results in our logistic regression and mixed model analyses.

In this old-age in-hospital population, perioperative QTc-durations changed differentially, increasing in patients with normal, but decreasing in patients with abnormal baseline QTc-duration. Changes in perioperative QTc-duration were not not appear to pose a risk for QTc-prolongation in this population. Nonetheless, these can be made to omit pre-haloperidol ECG recording as a standard measure in hospital when prescribing haloperidol.

We thank Mrs J. de Koning-Popma for her help in data acquisition.

918.86.616), the Dutch Medicines Evaluation Board (MEB/CBG), the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement nr. 241679 - the ARITMO project, and for ‘The effects of melatonin versus placebo on delirium in hip fracture patients’ trial (MAPLE): ZonMw, no. 311020301

role: none

All authors were involved in study concept and design, analysis and interpretation of data and preparation of manuscript. SJ and MB were involved in acquisition of data.

80 Chapter 5

1. Kirchhof P, Franz MR, Bardai A, Wilde AM. Giant T-U waves precede torsades de pointes in long QT syndrome: A systematic electrocardiographic analysis in patients with acquired and congenital QT prolongation. J Am Coll Cardiol 2. Smits JP, Blom MT, Wilde AA, Tan HL. Cardiac sodium channels and inherited electrophysiologic disorders: A pharmacogenetic overview. Expert Opin Pharmacother 3. De Bruin ML, Langendijk PN, Koopmans RP, et al. In-hospital cardiac arrest is associated with use of non-antiarrhythmic QTc-prolonging drugs. Br J Clin Pharmacol 4. Straus SM, Bleumink GS, Dieleman JP, et al. Antipsychotics and the risk of sudden cardiac death. Arch Intern Med 5. Tan HL, Hou CJ, Lauer MR, Sung RJ. Electrophysiologic mechanisms of the long QT interval syndromes and torsade de pointes. Ann Intern Med 6. Sanguinetti MC, Tristani-Firouzi M. hERG potassium channels and cardiac arrhythmia. Nature 7. Mortl D, Agneter E, Krivanek P, Koppatz K, Todt H. Dual rate-dependent cardiac electrophysiologic effects of haloperidol: Slowing of intraventricular conduction and lengthening of repolarization. J Cardiovasc Pharmacol monophasic action potential duration in anesthetized dogs. Chest 9. Stepkovitch K, Heagle Bahn C, Gupta R. Low-dose haloperidol-associated QTc prolongation. J Am Geriatr Soc 10. Straus SM, Sturkenboom MC, Bleumink GS, et al. Non-cardiac QTc-prolonging drugs and the risk of sudden cardiac death. Eur Heart J 11. Ray WA, Meredith S, Thapa PB, et al. Antipsychotics and the risk of sudden cardiac death. Arch Gen Psychiatry 12. Stollberger C, Huber JO, Finsterer J. (2005) Antipsychotic drugs and QT prolongation. Int Clin Psychopharmacol 13. Roden DM. Drug-induced prolongation of the QT interval. N Engl J Med 14. Hatta K, Takahashi T, Nakamura H, et al. The association between intravenous haloperidol and prolonged QT interval. J Clin Psychopharmacol 15. American Psychiatric Association. APA guidance on the use of antipsychotic drugs and cardiac antipsychotic_use_attachement.html. 15 March 2013. 16. Blom MT, Bardai A, van Munster BC, Nieuwland MI, de Jong H, et al. Differential changes in QTc duration during in-hospital haloperidol use. PLoS One 17. Gustafson Y, Berggren D, Brannstrom B, Bucht G, Norberg A, et al. Acute confusional states in elderly patients treated for femoral neck fracture. J Am Geriatr Soc 18. De Jonghe A, van Munster BC, van Oosten HE, et al. The effects of melatonin versus placebo on delirium in hip fracture patients: Study protocol of a randomised, placebo-controlled, double blind trial. BMC Geriatr 19. Rautaharju PM, Surawicz B, Gettes LS. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: Part IV: The ST segment, T and U waves, and the QT

rhythm society: Endorsed by the international society for computerized electrocardiology. Circulation 20. Committee for proprietary medicinal products. The assessment of the potential for QT interval prolongation by non-cardiovascular medicinal products, 1997. 21. Kudo S, Ishizaki T. Pharmacokinetics of haloperidol: An update. Clin Pharmacokinet 456. 22. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 23. American Psychiatric Association, Task Force on DSM-IV. Diagnostic and statistical manual of mental disorders: DSM IV-TR.: American Psychiatric Publishing, Inc. 2000 24. Arizona center for education and research on therapeutics website. list one. 2012. 25. Nagele P, Pal S, Brown F, et al. Postoperative QT interval prolongation in patients undergoing noncardiac surgery under general anesthesia. Anesthesiology 26. Barnett AG, van der Pols JC, Dobson AJ. Regression to the mean: What it is and how to deal with it. Int J Epidemiol 27. Harris RI, Steare SE. A meta-analysis of ECG data from healthy male volunteers: Diurnal and intra- subject variability, and implications for planning ECG assessments and statistical analysis in clinical pharmacology studies. Eur J Clin Pharmacol 28. Hassaballa HA, Balk RA. Torsade de pointes associated with the administration of intravenous haloperidol. Am J Ther 29. Young J, Anderson D, George J. Delirium: Diagnosis, prevention and management. clinical guideline 103. commissioned by the national institute for health and clinical excellence. 2010. 30. Desai M, Tanus-Santos JE, Li L, Gorski JC, et al. Pharmacokinetics and QT interval pharmacodynamics of oral haloperidol in poor and extensive metabolizers of CYP2D6. Pharmacogenomics J 113. 31. Miceli JJ, Tensfeldt TG, Shiovitz T, et al. Effects of oral ziprasidone and oral haloperidol on QTc interval in patients with schizophrenia or schizoaffective disorder. Pharmacotherapy 32. Fulop G, Phillips RA, Shapiro AK, et al. ECG changes during haloperidol and pimozide treatment of tourette’s disorder. Am J Psychiatry 33. Benoit SR, Mendelsohn AB, Nourjah P, Staffa JA, Graham DJ. Risk factors for prolonged QTc among US adults: Third national health and nutrition examination survey. Eur J Cardiovasc Prev Rehabil

82 Chapter 5

post surgery

p-value (n=24) (n=52) Male, n (%) 5 (20.8) 13 (25.0) 0.691 Age, years, mean (SD) 88.1 (5.7) 81.1 (16.3) 0.046 442.8 (34.9) 446.2 (33.4) 0.683 Baseline QTc-interval subgroup, n (%) 0.881 Normal 12 (50.0) 23 (44.2) Borderline 5 (20.8) 13 (25.0) Abnormal 7 (29.2) 16 (30.8) Heart rate, beats per minute, mean (SD) 74.3 (12.7) 77.6 (12.8) 0.304 90.0 (15.4) 94.4 (20.4) 0.356 3 (12.5) 6 (11.5) 1.000 2 (8.3) 7 (13.5) 0.711 0 (0.0) 4 (7.7) 0.301 1 (0, 2) 1 (1, 2) 0.971 Delirium during study period, n (%) 17 (70.8) 10 (19.2) <0.001 8 (33.3) 23 (44.2) 0.369 Serum sodium level, mmol/l, mean (SD) 139.3 (4.2) 138.8 (5.0) 0.689 Serum potassium level, mmol/l, mean (SD) 4.0 (0.6) 4.1 (0.6) 0.643

84 PART II

CHAPTER 6

M.T. Blom, D.A. van Hoeijen, A. Bardai, J. Berdowski, P.C. Souverein, M.L. De Bruin, R.W. Koster, A. de Boer, H.L. Tan

Open Heart. 2014 In press.

Out-of-hospital cardiac arrest (OHCA) is a major public health problem. Recognizing the complexity of the underlying causes of OHCA in the community, we aimed to establish the clinical, pharmacological, environmental and genetic factors, and their interactions, that may cause OHCA.

We set up a large-scale prospective community-based registry (AmsteRdam Resuscitation Studies, ARREST) in which we prospectively include all resuscitation attempts from OHCA in a large study region in The Netherlands in collaboration with Emergency Medical Services. Of all OHCA victims since June 2005, we prospectively collect medical history (through hospital and general practitioner), current and previous medication use (through community pharmacy). In addition, we include DNA samples from OHCA victims with documented ventricular study designs are employed to analyse the data of the ARREST registry, including case-control, cohort, case only, and case-cross over designs.

We describe the rationale, outline and potential results of the ARREST registry. The design allows for a stable and reliable collection of multiple determinants of OHCA, whilst assuring that the patient, lay-caregiver or medical professional is not hindered in any way. Such comprehensive data collection is required to unravel the complex basis of OHCA. Results will be published in peer-reviewed

88 Chapter 6

for 15–20% of all natural deaths in adults in the United States and Western Europe, and for up to 50% of all cardiovascular deaths.1 In the vast majority, it occurs in the of heart disease.2,3 Survival rates are increasing, but remain low despite intense efforts at improving treatment of OHCA victims.4-6 Prevention of OHCA is clearly desirable, and requires elucidation of the underlying disease processes and risk factors. OHCA VF). VT/VF may stem from multiple mechanisms, ultimately resulting in disrupted cardiac electrophysiology. On the level of an individual patient, multiple conditions, both inherited and acquired, interact to cause these disruptions and result in VT/VF.3 Important predictors of OHCA, including heart failure, cardiac ischemia unrecognized prior to OHCA and are thus predictive in only a minority of OHCA victims. Furthermore, additional non-cardiac risk factors for OHCA (e.g., depression, epilepsy, environmental factors) have been proposed, suggesting that OHCA in the community is multi-factorial.7-10 Moreover, various drugs have been associated with OHCA, ultimately leading to their withdrawal from the market.11,12 Apparently, the strict pre-marketing safety screening systems lack the ability to identify all drugs that increase the risk of OHCA. Of note, this also applies to non-cardiac drugs, i.e., drugs prescribed for non-cardiac disease. Such drugs may cause VT/VF because they impact on cardiac electrophysiology by modulating cardiac ion channel function. Importantly, VT/VF risk from drug use often requires the added presence of other risk factors. For instance, VT/VF risk conferred by (cardiac or non-cardiac) sodium channel blocking drugs is particularly high in patients with heart failure and/or cardiac ischemia.13,14 Genetic factors may also modulate VT/VF risk. For instance, observational studies have indicated that sudden death of a family member is a risk factor for OHCA.15-17 Genetic markers for increased OHCA risk in the community have started to be found,18,19 but the role of causative genes with large effect sizes remains to be established. Recognizing the complexity of the underlying causes of OHCA in the community, we aimed to uncover the clinical, pharmacological, environmental and genetic factors, and their interactions, that may cause OHCA. To this end, we set up a large-scale community-based registry (AmsteRdam REsuscitation STudies, ARREST) in which we prospectively include all resuscitation attempts from OHCA in a large study region in The Netherlands. Of all OHCA victims, we collect medical history, current and previous medication use, and DNA samples. The objective of this paper is to describe the design, rationale and outline of this registry, hereafter referred to as the ARREST registry.

The AmsteRdam REsuscitation STudies (ARREST) is an on-going, prospective observational registry of all OHCAs in the study region North Holland, a province of the Netherlands. ARREST was set up in June 2005 to establish the determinants of outcome of OHCA.20-22 In July 2007, its aims were extended to include insight into the genetic, clinical, pharmacological, and environmental determinants of OHCA in the community.10,18,19 To minimize selection bias, data of all resuscitation attempts with involvement of emergency medical services (EMS) in the study region are collected and stored in the ARREST database. The study region covers 2404 km2 (urban and rural communities) and has a population of 2.4 million people. All studies with the ARREST registry are conducted according to the principles expressed in the Declaration of Helsinki. Since OHCA is an emergency situation, consent cannot be asked before data collection. From all participants who survive OHCA, written informed consent for collection and use of medical and pharmacological information and DNA samples is obtained after discharge from the hospital. The Ethics Committee of the Academic Medical Center, a teaching hospital in Amsterdam, approved the study protocol, and the use of data from patients who did not survive OHCA, and of data needed to evaluate the outcome of the resuscitation attempt.

In a medical emergency, people dial the national emergency number. When the EMS dispatcher suspects OHCA, he/she dispatches two ambulances. Ambulance personnel in the event of a suspected OHCA. In addition, the placements of AEDs in public areas has been facilitated by public and private initiatives during the study period, but is not centrally controlled or directed.20 All four EMS services in the study region participate in ARREST.

After each resuscitation attempt, EMS paramedics routinely send the continuous ECG When an AED is used prior to EMS arrival, ARREST study personnel visits the AED site shortly after the OHCA, and collects the AED ECG recording. All ECGs are stored and analyzed with dedicated software (Code Stat Reviewer 7.0, Physio Control, Redmond WA, USA), allowing for analysis of heart rhythm/arrhythmia during the resuscitation

90 Chapter 6 attempt. Data items concerning the resuscitation procedure are collected according to the Utstein recommendations.23 Ambulance personnel is obliged by protocol to call the study center after every OHCA to provide additional information on the resuscitation (e.g., whether OHCA was witnessed, whether basic life support was provided before arrival of ambulance personnel, whether the patient died at the resuscitation site or was retrieve data of in-hospital resuscitation care / treatment (i.e. therapeutic hypothermia, angiography), outcome and neurological status when discharged alive. 23 All arrests are deemed to result from cardiac causes unless an unequivocal non-cardiac cause (i.e., reviewed to verify the presence or absence of a non-cardiac cause. In addition, data items concerning medication use and medical history are collected. For the sake of DNA discovery studies, the best possible ascertainment of a cardiac cause of OHCA is achieved by collecting DNA samples only of patients with documented VT/VF at any time during resuscitation. Patients in whom only asystole (but no VT/VF) is recorded, are excluded, as asystole is the end stage of any cardiac arrest, and may be due to non- cardiac causes.24

DNA samples In order to avoid interventions during the resuscitation attempt for research purposes only, DNA samples are collected as follows. Of patients who die on the scene and are not transported to the hospital, DNA is extracted from the endotracheal tube placed for ventilation during the resuscitation attempt (no blood is usually drawn before hospital admission). Endotracheal tubes are collected at ambulance posts or emergency rooms by study personnel. Of patients who reach the hospital alive, DNA is extracted from blood lymphocytes of blood that is routinely collected and analysed in the hospital’s lab for patient care. Unused blood (available in >95% of the patients) is sent to the ARREST study center or collected by study personnel, and is used for DNA analysis and to determine metabolic parameters. If future analyses of the collected DNA material reveal pathogenic mutations that require medical action, the patient is contacted through his/her general practitioner (GP) and the patient has indicated that he/she wishes to be informed when giving informed consent. Genetic counseling is offered to the patient and the patient’s relatives in such a case.

Medical information (medical history including mental health, current disease diagnosis, survival status and, if applicable and available, post mortem examination) is collected by

contacting the hospital of admission, and the patient’s GP, and conducting telephone interviews with surviving patients. In the Netherlands, every citizen has a GP who acts as gatekeeper for all medical care. GP records contain information on diagnoses from GPs/ The GPs are asked to mark in a questionnaire whether their patient was diagnosed prior to their OHCA with any medical condition. In addition, GPs are asked to send all medical correspondence with all treating medical specialists to the ARREST study center.

Information regarding medication use (current medication at the time of OHCA and in the year before OHCA) is obtained by contacting the patient’s community pharmacist. Exposure to medication is determined by reviewing dispensing information prior to the date of OHCA. Dosage and duration of medication use is recorded of all medication use and used to calculate the time during which a patient is exposed.

socioeconomic and environmental characteristics of the patient’s place of residency. Such data on a neighbourhood level are available from Statistics Netherlands, a Dutch governmental institution.

Data and DNA material collected by the ARREST registry can be analysed using various study designs. We employ case-control designs, collaborating with registries and studies that provide suitable controls for ARREST OHCA-cases. Cases are matched to controls according to age, sex and OHCA date. Controls are drawn from a registry that contains drug dispensing records from community pharmacies (PHARMO Institute for Drug Outcomes Research, Utrecht, The Netherlands. Available from: http://www.pharmo. nl/.),25,10 and from a database that contains the complete medical records of >60,000 people from a large group of GPs in the study area (HAG-net-AMC).26,9 Genetic data of ARREST OHCA-cases are compared to data registries with DNA samples of normal control populations.18,19 In a cohort study, we compare a group of ARREST OHCA-cases with a particular property to a group of cases without that property. For example, we study ARREST OHCA-cases with and without obstructive pulmonary disease, and compare their survival to hospital discharge.22

92 Chapter 6

Case only designs are employed when studying effects of interactions on OHCA OHCA.27 In a case cross-over design, properties of the ARREST OHCA-case (e.g., exposure to study drug) are compared with the same property at an earlier time point (e.g., 6 months before OHCA), thereby reducing confounding and selection bias.

Data collection of resuscitation parameters started in June 2005. In July 2007, we started collecting DNA samples. Each year, we collect data from ~1000 resuscitation attempts, ~50% of which have VT/VF documentation. Figure 1 shows the inclusion scheme of all and ambulance services participated in DNA collection. In this one-year period, we registered 1162 resuscitation attempts, of which 871 had a cardiac cause without EMS witness. In 498 cases, VT/VF was documented. Of these VT/VF cases, 23% (114) died on the scene, 24% (121) died in the emergency room before hospital admission, while 53% (263) were admitted to the hospital. DNA samples were collected in 60% of cases who died on the scene, in 83% of cases who died in the emergency room and in 94% of cases who were admitted to the hospital. Table 1 presents medication data per indication category of ARREST OHCA- cases (N=1787, mean age 66.5, 77% male) collected in the period 2005-2009, and age/sex/OHCA date-matched non-OHCA controls from the PHARMO database (N=7698). As expected, medication use for cardiovascular diseases was higher among cases than controls. Drugs used for several other categories were also more prevalent among cases, inviting further research. Figure 2 shows the inclusion rate of DNA samples in the period July 2007- January 2014. We included 3005 cases, and continue to collect DNA samples at the same rate. The average yield of DNA from blood samples was 160 μg DNA, while tubes yielded 6.9 μg DNA. Inclusion rate was stable over the past years.

The pathophysiology of OHCA is extremely complex, and its causes are highly heterogeneous. In many patients, it is not possible to single out one individual cause. Instead, cardiac arrest results from various interacting causes and circumstances and a to an enormous extent. Furthermore, since OHCA occurs in such an unpredictable

94 Chapter 6

VT/VF 1787 7698 1376 (77.0) 5939 (77.1) 66.5 (13.7) 66.4 (13.8) A - Alimentary tract and metabolism 810 (45.3) 2607 (33.9) 925 (51.8) 2401 (31.2) C - Cardiovascular system 1268 (71.0) 3903 (50.7) 221 (12.4) 911 (11.8) 181 (10.1) 563 (7.3) 410 (22.9) 1416 (18.4) 53 (3.0) 177 (2.3) M – Musculo-skeletal system 354 (19.8) 1420 (18.4) N – Nervous system 557 (31.2) 1966 (25.5) 12 (0.7) 73 (0.9) R – Respiratory system 426 (23.8) 1524 (19.8)

most notably, DNA samples. To increase the chances for discovery of relevant DNA OHCA cases for analysis, thereby increasing our ability to identify genetic risk factors. Our close collaboration with EMS services in the study region enables us to include all OHCA cases in the study region, thereby enabling us to also compare patients with or without VT/VF. Our study design thus allows us to study the full range of clinical, pharmacological, environmental and genetic risk factors of OHCA, and their interactions.

from these association studies with mechanistic studies in the experimental laboratory. This laboratory is equipped with a comprehensive range of experimental techniques, including molecular biology, molecular genetics, single-cell electrophysiology (patch- clamp studies), and tissue, organ, and in vivo electrophysiology. Moreover, our study group is also embedded in the cardiology and cardiogenetics departments. This organization in which these various domains are interwoven offers the possibility that research questions from one domain feed into the other domains. This allows for a multifaceted approach to resolve the causes of OHCA in the community.28 Finally, the ARREST infrastructure is not only suitable to study the causes of OHCA, but also its outcome. The fact that data regarding the resuscitation attempt are collected according to Utstein recommendations enables us to study survival at the various stages of post-resuscitation care. This allows for evaluation of the effects of various interventions in the chain-of-survival care after OHCA.

96 Chapter 6

We describe the rationale, outline and potential results of the ARREST database. To with OHCA aimed at identifying genetic, clinical, pharmacological and environmental determinants of OHCA with documented VT/VF. The data presented here show that our study design allows for a stable and reliable registry of OHCA.

MTB, AB, JB, PCS, RWK, AdB and HLT conceived the study. RWK and HLT obtained funding for the study and supervise it. MTB, DvH, AB and JB collected data. MTB, DvH, AB, JB and HLT wrote the manuscript. PCA, MLdB, RWK and AdB provided critical revisions of the manuscript.

The ARREST registry is supported by a grant from the Netherlands Heart Foundation, Den Haag, the Netherlands (grant 2006-B179), and by an unconditional grant from Physio Control Inc, Redmond, (NWO, grant ZonMW Vici 918.86.616 to dr Tan), the Dutch Medicines Evaluation Board (MEB/ CBG) the European Community’s Seventh Framework Programme (FP7, grant 241679, ARITMO), and Biobanking and Biomolecular Research Infrastructure The Netherlands (BBMRI-NL)

1. Myerburg RJ, Castellanos A. Cardiac arrest and sudden cardiac death. In: Libby P, Bonow RO, Mann DL, Zipes DP eds. Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. Elsevier, Oxford, UK, 2007. 933–974 2. Rea TD, Crouthamel M, Eisenberg MS, et al. Temporal patterns in long-term survival after 3. Huikuri HV, Castellanos A, Myerburg RJ. Sudden death due to cardiac arrhythmias. N. Engl. J. Med. 4. Iwami T, Nichol G, Hiraide A, et al. Continuous Improvements in ‘’Chain of Survival’’ Increased Survival After Out-of-Hospital Cardiac Arrests: A Large-Scale Population-Based Study. Circulation 5. Berdowski J, Berg RA, Tijssen JG, et al. Global incidences of out-of-hospital cardiac arrest and survival 6. Aufderheide TP, Yannopoulos D, Lick CJ, et al. Implementing the 2005 American Heart Association 62. 7. Empana JP, Jouven X, Lemaitre RN, et al. Clinical depression and risk of out-of-hospital cardiac 9. Bardai A, Lamberts RJ, Blom MT, et al. Epilepsy is a risk factor for sudden cardiac arrest in the general 10. Warnier MJ, Blom MT, Bardai A, et al. Increased risk of sudden cardiac arrest in obstructive pulmonary 11. Ray WA, Chung CP, Murray KT, et al. Atypical antipsychotic drugs and the risk of sudden cardiac 12. Qureshi ZP, Seoane-Vazquez E, Rodriguez-Monguio R, et al. Market withdrawal of new molecular entities approved in the United States from 1980 to 2009. Pharmacoepidemiol Drug Saf 635. 14. Shaw RM, Rudy Y. Electrophysiologic effects of acute myocardial ischemia: a theoretical study of 15. Friedlander Y, Siscovick DS, Weinmann S, et al. Family history as a risk factor for primary cardiac 16. Jouven, X., Desnos, M., Guerot, C. et al. Predicting sudden death in the population: the Paris 17. Dekker LR, Bezzina CR, Henriques JP, et al. Familial sudden death is an important risk factor for

98 Chapter 6

locus at 2q24.2 through genome-wide association in European ancestry individuals. PLoS Genet 21. Bardai A, Berdowski J, van der Werf C, et al. Incidence, causes, and outcomes of out-of-hospital cardiac arrest in children. A comprehensive, prospective, population-based study in the Netherlands. J 22. Blom MT, Warnier MJ, Bardai A, et al. Reduced in-hospital survival rates of out-of-hospital cardiac 23. Jacobs I, Nadkarni V, Bahr J, et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, Inter-American Heart Foundation, 25. Herings RMC. Pharmo, a record linkage system for post marketing surveillance of prescription drugs in the Netherlands. In: Thesis Department of Pharmacoepidemiology and Pharmacotherapy Utrecht. The Netherlands: Utrecht University, 1993. 27. Khoury MJ, Flanders WD. Nontraditional epidemiologic approaches in the analysis of gene- Review. 28. Bardai A, Amin AS, Blom MT, et al. Sudden cardiac arrest associated with use of a non-cardiac drug that reduces cardiac excitability: evidence from bench, bedside, and community. Eur Heart J

100 CHAPTER 7

D.A. van Hoeijen*, M.T. Blom*, H.L. Tan * These authors contributed equally

Expert Opin Pharmacother. 2014;3:1-13.

Since the recognition of inherited sodium (Na+) channel disease, the cardiac Na+ channel is extensively studied. Both loss-of-function and gain-of-function mutations of the cardiac Na+ channel are associated with cardiac arrhythmia and sudden cardiac death. Pathophysiological mechanisms that may induce arrhythmia are unravelled and include alterations in biophysical properties due to the mutation in SCN5A, drug use and circumstantial factors. Insights into the mechanisms of inherited Na+ channel disease may result in tailored therapy. However, due to the complexity of cardiac electrical activity and pathophysiological mechanisms, pharmacotherapy in cardiac Na+ channel disease remains challenging.

This review discusses various mechanisms involved in inherited Na+ channel disorders, focussing on Brugada Syndrome (BrS) and long QT syndrome type 3 (LQTS3). It aims to provide an overview of developments in pharmacotherapy, discussing both treatment and which drugs to avoid to prevent arrhythmia.

Altered biophysical properties of cardiac Na+ channels are the basis of arrhythmias in patients with inherited Na+ channel diseases such as BrS and LQTS3. The effects of such biophysical derangements are strongly modulated by concomitant factors. Tailored drug therapy is required to prevent arrhythmia and is best achieved by educating patients affected by Na+ channel disorders.

102 Chapter 7

The cardiac sodium (Na+) channel plays a pivotal role in the propagation of electrical activity through the heart. In the past decade many mutations in SCN5A, the gene encoding the pore-forming alpha-subunit of the cardiac Na+ channel, have been in SCN5A is expanding, although some questions regarding underlying mechanisms and interacting factors that induce the occurrence of cardiac arrhythmias remain unanswered. This progress has aided further understanding of effects and side effects of cardiac and non-cardiac drugs that modify the function of the cardiac Na+ channel. This review will focus on inherited electrophysiological disorders due to Na+ channel dysfunction and will give an update on pharmacotherapy.1

Cardiac electrical activity is initiated in the sinus node (SA node) in the right atrium by spontaneous excitation of pacemaker cells (Figure 1, panel A). From there the electrical signal starts activating the right and left atria and travels to the atrioventricular node (AV node). In the AV node, conduction of the excitation wave is slowed, before it left and right ventricle. The body surface electrocardiogram (ECG) represents the net electrical activity of all action potentials (APs) in the heart. At a cellular level, cardiac electrical activity is propagated through a complex interplay in which several ion channels and ions play a role.2-4 Ion channels form little pores in cardiac cell membranes enabling ions to cross these membranes. When ion channels are activated due to a change in membrane potential, the channels open, and allow an ion current across the membrane. The sequence of opening and closing of ion channels is time and voltage dependent (gating) and results in cardiac electrical activity and AP formation.

The AP in ventricular cardiomyocytes consists of different phases: phase 0-4 (Figure 1, panel B). During phase 4, ventricular cardiomyocytes maintain a stable and negative membrane potential of approximately -85 mV. Phase 0 of the AP is initiated by the depolarization of the cardiomyocyte to its threshold due to an excitation wave, which results in the opening of the voltage sensitive Na+ channels. After opening of these channels, Na+ ions quickly enter the cell following the Na+ concentration gradient. As + a consequence of this Na current (INa), the cardiomyocyte rapidly depolarizes, thereby

103

A Electrocardiogram (ECG) B Ventricular myocyte action potential

) 1 30 Phase 0: depolarization 2 Phase 1: fast repolarization

P wave: atrial tial (mV 0 Phase 2: plateau

LA depolarization en Phase 3: terminal repolarization ot -30 QRS complex: 0 3 Phase 4: resting ventricular depolarization RA ane p -60 4 4 RV LV QT interval: embr

M -90 ventricular repolarization 0 nA Sodium current de de ial no tr icular no tr inoa S en R v L-type calcium current

io 0 nA tr A P T Transient outward 0 nA Q S potassium current

Atrial Ventricular 0 nA Rapidly activating delayed action potential action potential rectifying potassium current

Slowly activating delayed 0 nA rectifying potassium current

Inward rectifying 0 nA potassium current

(repolarizing) potassium currents are pointed upwards. initiating a cascade of activation and inactivation of other voltage dependent ion channels. Early repolarization (phase 1) starts by opening and closing of potassium (K+) + channels, leading to a small repolarizing, transient outward K current (ITO). During phase 1, the K++ channels are closing and calcium (Ca2+) channels are opening. Phase 2 is characterized by a plateau in the AP as a net result of depolarizing 2+ 2+ 2+ inward Ca current (ICa-L) through L-type Ca into the cardiomyocyte enables the cardiomyocyte to couple excitation to myocardial + repolarizing K currents (IKr, IKs) predominate after the activation of the rapidly and slowly activating repolarizing K+ currents, respectively, and closing of L-type Ca2+ channels. This results in a rapid repolarization of the cardiomyocyte towards the resting membrane potential. During phase 3 and 4, the electrochemical gradient across the cell

104 Chapter 7 membrane of the cardiomyocyte is restored by the Na+-K+ pump that pumps Na+ ions into the cell and K+ out of the cell. The AP is different across various areas of the heart as the result of small regional differences in gating properties or channel expression of the various ion channels. regions of the heart. This may introduce regional heterogeneity of the AP, provoking substantial voltage gradients that evoke cardiac arrhythmias.

A DI DII DIII DIV +

NH3

Membrane Cell

+ + + S5 S5 S6 + S1 S2 S3 S4+ S6 S1 S2 S3 S4+ S1 S2 S3 S4+ S5 S6 S1 S2 S3 S4+ S5 S6 + + + +

+ -subunit + + + b Cytosol + NH3

- COO

- COO + B Na

+ NH3

+ + + + + + + + + + + -subunit

+ b Membrane Cell + + + MOG1 +

Caveolin-3 Cytosol

+ - NH3 COO

Ankyrin Cytoskeleton a1-syntrophin

- COO

105

+ Activated cardiac Na channels rapidly depolarize the cardiomyocyte. The resulting INa determines cardiac conduction velocity and cardiac excitability. Na+ channels consist of a pore-forming alpha subunit and several beta subunits that interact with the alpha subunit and regulate its gating properties or cellular localization. The alpha subunit has four homologous domains that each consists of six trans-membrane segments (Figure 2, panel A). The fourth segment of each domain determines voltage sensitivity. The segments are linked by intracellular and extracellular loops that regulate channel inactivation or determine channel selectivity for Na+. The SCN5A alpha subunit acts in a large protein complex consisting of several beta subunits and many other associated 5, 6 proteins (Figure 2, panel B). Each protein in this complex is able to affect INa and/

Na can be due to several factors, including mutations in SCN5A, mutations in beta subunits or other SCN5A associated proteins, and pharmacologic interactions between Na+ channels and Na+ channel blocking drugs.7 Malformation and dysfunction of Na+ channel protein complexes are not only the result of genetic mutations, but may also be involved in acquired cardiac disease. Consequently, cardiac Na+ channels are involved in acquired and inherited cardiac disease.8-10

Mutations in SCN5A are linked to different cardiac diseases. Mutations can change gating properties or cause abnormal expression of Na+ channels. Most mutations, either in SCN5A or in beta subunits, lead to Na+ channel dysfunction, which cause either a gain-of-function or a loss-of-function. Some mutations cause opposing effects on Na+ channel properties. For instance, the 1795insD mutation in SCN5A causes clinical characteristics that are consistent with both a gain and a loss of sodium channel function.[11] Inherited Na+ channel diseases, for instance Brugada Syndrome (BrS), are traditionally considered as monogenic diseases. However, mutations in SCN5A in BrS do not always follow a Mendelian pattern of inheritance, indicating a more complex inheritance model. Rare variants in SCN5A are not considered as disease causing mutations per se genetic variants in SCN5A may also be involved in inherited Na+ channel disease.12, 13 Due to variants in SCN5A, Na+ current may be slightly impaired, whereas additional factors may further reduce Na+ current and trigger cardiac events. For example, fever is recognized as such a factor. Although the mechanism is not fully understood, several explanations have been proposed by which fever can aggravate Na+ channel disease. First, intact cardiac Na+ channels have temperature dependent properties that may lead to disease symptoms during fever.14 Second, SCN5A genetic variants may change these

106 Chapter 7 temperature dependent properties, which lead to different activation and/or inactivation kinetics of cardiac Na+ channels.15, 16 Heart rate is another factor that may be relevant in inherited Na+ channel disease. On the one hand, Na+ channels have less time to recover from inactivation at fast heart rates. Accumulation of non-recovered Na+ channels results in fewer channels available for activation and initiation of the next AP. Such Na+ channel dysfunction may be further aggravated by the use of medication.17 On the other hand, symptoms in inherited Na+ channel diseases (e.g. arrhythmia in Brugada Syndrome and Long QT Syndrome type 3) are reported to occur often at rest or during sleep, and are thus 18 associated with slow heart rate. These observations are ascribed to increased ITO current due to slow gating kinetics at slow heart rate.19 Clearly, the functional consequences of inherited Na+ channel disease often result from the complex interplay of multiple factors.

Some mutations in SCN5A result in gain-of-function of the cardiac Na+ channel. These mutations are associated with long QT 3 syndrome (LQTS3).4 Due to an increase in net

INa the cardiac AP prolongs, resulting in a prolonged QT duration on the ECG (Figure

3, Panels A, B). Increased net INa can be due to several underlying mechanisms, but most mutations that cause LQTS3 disturb fast Na+ channel inactivation or inactivation stability, thereby leading to increased late Na+ current, considered the main pathologic mechanism in LQTS3 (Figure 3, panel C). As phase 2 and phase 3 of the AP are the result of the delicate balance between depolarizing and repolarizing ion currents, relatively small currents such as late INa affect this balance and prolong the AP and QT interval. Prolonged QT intervals may trigger early afterdepolarizations (EADs) during phase 2 or 3 due to reactivation of Ca2+ channels. EADs may trigger characteristic ventricular tachyarrhythmias (torsade de pointes, TdP). Arrhythmias in LQTS3 have been associated with bradycardia rather than with exercise or emotional stress as in LQTS type 1 or type 2. ICD implantation may be considered in those patients at highest risk for arrhythmia.18

Other mutations lead to loss-of-function of the cardiac Na+ channel. The loss of + cardiac Na channel function may lead to a reduced INa and thereby impair cardiac depolarization and excitability. These mutations are associated with Brugada Syndrome (BrS), progressive cardiac conduction disease (PCCD), and sick sinus syndrome.4 BrS is characterized by typical ST-segment elevations in the right precordial ECG leads (V1-V3) (Figure 4, Panel A). It is associated with an increased risk of sudden

107

cardiac death due to ventricular tachyarrhythmias, typically during rest or sleep, in the absence of gross structural abnormalities.20-22 mutations in SCN5A23. Since then, >350 mutations in SCN5A have been associated with BrS, accounting for ~20% of BrS cases.24, 25 In vitro studies of BrS-associated mutations in SCN5A invariably show loss of Na+ channel function.26-30 The underlying pathophysiologic mechanisms that cause the ECG abnormalities and arrhythmias in BrS are not fully resolved.31, 32 Most investigators regard BrS as the result of a disorder in cardiac depolarization or in cardiac repolarization (or combinations thereof). The repolarization disorder hypothesis states that the typical ST-segment elevation in BrS is caused by a transmural repolarization gradient in the right ventricular wall, related to regional differences in expression of ion channels (Figure 4, Panel D). In

B Ventricular myocyte action potential ) 30

tial (mV 0 en

ot -30

ane p -60 embr

M -90

C 0 nA

Late sodium current

108 Chapter 7 [87]

109

particular, the transient outward potassium current (ITO) is more prominent in the right

TO is more prominent in the epicardium than in de endocardium. Consequently, the notch in the action potential shape during phase 1 is more prominent in the epicardium, especially in the RVOT. Studies in canine wedge preparations demonstrated that a non-uniform abbreviation of the action potential in the right ventricle, related to these differences in expression of ion channels, causes the BrS ECG features.33-36 The depolarization hypothesis states that the reduction in sodium current results in conduction delay in the RVOT (Figure 4, Panel B, C).37 Arrhythmias are thus facilitated by regional slowing of conduction. BrS may not be solely attributable conspiring effects of conduction slowing and mild right and left ventricular structural abnormalities.38-40 The occurrence of arrhythmias in BrS can be triggered by a combination of (acquired) factors that reduce sodium current on top of the innate reduced function or the sodium channel. For example, a recent report described that a patient with BrS only developed cardiac arrhythmias during exercise while using nortriptyline (a sodium channel blocking antidepressant).17

Progressive cardiac conduction disease (PCCD), also called Lev-Lenègre disease, is a type of cardiac conduction disturbance characterized by progressive impairment of impulse propagation through the His-Purkinje system. It manifests as prolongation of the P wave, PR or QRS interval, and right or left bundle branch block (without ST-segment elevation or QT-prolongation). It may degenerate into complete AV block, resulting in the conduction system. The association of PCCD with loss-of-function mutations in SCN5A has been recognized since 1999.41 Sick sinus syndrome (SSS) is a disease of the sinoatrial node and is characterized by bradycardia, sinus arrest, atrial standstill, and tachycardia-bradycardia syndrome. It has been associated with loss-of-function mutations in SCN5A,[42] but mutations in other genes have been demonstrated as well.43 Mutations in SCN5A were also associated with familial dilated cardiomyopathy (DCM), a cardiac structural disease characterized by decreased systolic function and ventricular dilatation. Interestingly, these mutations were found in families with heterogeneous phenotype that included SSS and arrhythmias.44, 45 Phenotypical variants with overlapping features have been described, e.g., BrS plus LQT3, and BrS plus PCCD. Often, these features can be explained by the net

110 Chapter 7 result on INa, although the effects on various channel gating properties are sometimes subtle.

AnAnThe key principle of therapy in inherited sodium channel disease is to identify the patients at highest risk for cardiac arrhythmias. While implantation of an implantable potentially fatal arrhythmias, the cornerstone is prevention of arrhythmias by avoidance of medications. Also, situations that may evoke arrhythmias must be appropriately handled.18, 46 For example, fever should be treated, as it may trigger arrhythmias in a substantial proportion of these patients. Although a limited number of drugs have shown antiarrhythmic effects in patients with frequent arrhythmias, the most appropriate as the same drug that prevents arrhythmia in one patient may induce arrhythmia in another patient. This supports the notion that arrhythmia in inherited Na+ channel disease is a multifactorial phenomenon, and that tailored therapy must be instituted.

An up-to-date list of drugs that are generally accepted to have the properties to induce QT prolongation or TdP is available online at www.QTdrugs.org.47 QT prolongation by medication use can be achieved by either direct or indirect inhibition of repolarizing K+ currents or increase of depolarizing Na+ and/or Ca2+ current. The list contains over 160 of such drugs used for cardiac or non-cardiac disease. Patients with reduced repolarization reserve due to LQTS should avoid these drugs. BrS patients are also advised to avoid certain cardiac and non-cardiac drugs that may have pro-arrhythmic effects (Table 1). Most of these drugs reduce cardiac 2+ + INa, whereas other drugs decrease inward Ca current (ICa-L) or outward K current

(IK), directly or indirectly. An up-to-date list of these drugs is available online at www. brugadadrugs.org.48

Antiarrhythmic drugs that affect the cardiac sodium channel are used to unmask the type 1 BrS ECG during diagnostic drugs challenge.21, 22 These drugs include ajmaline, and other class 1A and class 1C antiarrhythmic drugs that reduce INa. The Cardiac Arrhythmia Suppression Trial (CAST) showed that the pro-arrhythmic effects of these drugs are not limited to inherited disease, but also exist in acquired heart disease.

111

I ( drugs Na Ajmaline I Procainamide I Cibenzoline IIb Disopyramide IIb Lidocaine IIb

INa ( I Pilsicainide I IIa

-blocking, IICa-L ( Propranolol IIb Amiodarone IIb I ( I (- Na IIb

ICa-L ( IIb

INa ( ICa-L ( Amitriptyline IIa Clomipramine IIa Desipramine IIa Nortriptyline IIa Dosulepine IIb IIb Imipramine IIb IIb I ( I ( Na Ca-L IIb IIb IIb

INa ( ICa-L (

112 Chapter 7

IIa Cyamemazine IIb IIb

INa ( ICa-L ( IIa IIb IIb IIb

INa ( ICa-L ( IIa Carbamazepine IIb Lamotrigine IIb IIb I ( I ( analgesics Na Ca-L Bupivacaine IIa IIa IIb

INa ( ICa-L ( Procaine IIa Tramadol IIb

ICa ( IIa IIb

Cocaine INa ( IIa

Ergonovine ICa ( IIb

INa ( IIb

INa ( IIb IIb Indapamide IIb Metoclopramide IIb - IIb potassium current.

113

CAST was initiated to test the hypothesis that suppression of ventricular ectopic beats after myocardial infarction with the use of class 1C antiarrhythmic drugs would discontinued as the group of patients using these drugs showed excess mortality due to arrhythmia.49 and placebo groups in the composite endpoint of sudden cardiac death and non-fatal an interaction of ischemia and exposure to class 1C antiarrhythmic drugs.50 Intracellular calcium overload, as is present in heart failure, reduces Na+ current. Therefore, class 1C antiarrhythmic drugs are also contra-indicated in heart failure patients.51 Although class 1C antiarrhythmic drugs have potential pro-arrhythmic effect in BrS patients and patients with acquired heart disease (as found in CAST), some patients with inherited Na+ phenotype.18 evoke potentially pro-arrhythmic gating effects.52 Some class 2, 3 or 4 antiarrhythmic drugs are reported to aggravate the type 1 BrS ECG. These drugs may induce ST segment elevation directly by reduction of

ICa-L or IK developed antiarrhythmic drug with INa and ITO reducing properties. Quinidine was formerly suggested as a drug that can aggravate features of BrS. However, there is accumulating evidence that quinidine is able to reduce arrhythmias in severely affected BrS patients.

Psychotropic drugs are prescribed to patients with psychiatric or neurologic disease and have an effect on the nervous system. However, drugs that affect neuronal ion channels may also affect cardiac ion currents. There is accumulating evidence that several antidepressants, antipsychotics and anti-epileptics are able to induce BrS signs on the ECG by Na+ or Ca2+ channel blockade.

Some drugs that are used during anaesthesia block INa or ICa-L, and thus may induce ST segment elevation. For example, lidocaine (a class 1 antiarrhythmic drug) is commonly used for local anaesthesia. However, when administered in low dosage and in combination with epinephrine, lidocaine has only a local effect and can thus be used safely for local anaesthesia.53 General anaesthesia can be safely performed in BrS patients when precautions are taken.54

114 Chapter 7

Drugs from other categories may also unmask Brugada ECG and evoke arrhythmia, including drugs that cause coronary vasospasm (e.g., acetylcholine, ergonovine), some antiemetic drugs and some antihistamines. Other substances that should be avoided by BrS patients are alcohol and cocaine. Cocaethylene (a metabolite of alcohol and 55, 56 cocaine) is reported to be a potent INa blocker. Although the mechanism is unclear, Increased parasympathetic activity after the intake of alcohol is suggested as the most likely pathophysiologic mechanism.57

Pro-arrhythmic drug effects may not only result from their effects to block or modify ion channel properties, but also from pharmacokinetic and pharmacogenetic effects. Inter-individual differences in metabolism and elimination of drugs are underlying these effects. Drug metabolism and elimination are dependent on the cytochrome P450 system in the liver. Genetic variation in cytochrome P450 may also decrease metabolism and elimination of pro-arrhythmic drugs. These variants do not necessarily lead to total cytochrome P450 iso-enzyme dysfunction, but may impair its function and increase the + channel properties is co-administered with another drug that is either a substrate or a blocker of the same iso-enzyme, the serum concentration of this drug may also be higher than expected. Such combination of drugs and or genetic variants may uncover BrS ECG features and/ or induce arrhythmias.58

BrS.59-61 Moreover, isoproterenol may prevent the typical ST-segment elevation in the right precordial leads in BrS patients.62 Intravenous orciprenaline may also terminate electrical storm and reduce ST-segment elevation in BrS.63 A limited number of drugs have been described to prevent long-term cardiac arrhythmias in BrS patients. Quinidine seems to be the most promising drug to prevent arrhythmia in BrS patients. Quinidine is a class 1A antiarrhythmic drug, which is thought to prevent arrhythmias by blocking ITO. It has been reported that the typical BrS type ECG resolves during treatment with quinidine.64 treatment on malignant arrhythmias has been reported in a limited number of BrS patients.59-61, 63, 65-70 Still, there is a need for more evidence that quinidine completely

115

prevent arrhythmias in all BrS patients.71 As quinidine has the potential to cause QT prolongation and arrhythmia, treatment with quinidine should be preferably only initiated in severely affected BrS patients in a safe setting in an experienced medical centre.66 A limited number of case reports have been published on treatment of ventricular arrhythmia in BrS with the phosphodiesterase inhibitor cilostazol.70, 72, 73 The ICD shocks. After the administration of cilostazol, this patient suffered no longer form

Blockade of the QT prolonging late Na+ current is crucial to reduce the risk for arrhythmias in LQTS3 patients. Several drugs are known to block INa. However, most 74 of these drugs block peak INa more strongly than late INa. Novel drugs with a high selectivity for blocking the late Na+ current have recently become available or are under development. Importantly, late Na+ current is not only increased in LQTS3, but also in common acquired cardiovascular diseases, in particular, heart failure.9, 10, 51 Thus, drugs developed to block late INa in acquired cardiac disease may also prevent arrhythmia in LQTS3. For example, ranolazine, developed for the treatment of angina pectoris, may also prevent arrhythmia in LQTS3.75, 76 Ranolazine blocks several ion currents, including INa, IK, and ICa-L, but shows high selectivity for blocking late INa in ventricular 74, 75, 77 myocytes (30-fold more than block of peak INa). Additionally, in experimental models, block of late INa increased with stimulating frequency, whereas its selectivity 78 for inhibiting late INa remained. Thus, ranolazine may exert its effect particularly at faster heart rates, and when administered in low dose at which it has limited or no effect 74, 77 on other ion currents including peak INa. Although ranolazine is contraindicated for LQTS patients in general due to its QT prolonging effect secondary to blockade of repolarizing K+ patients.74, 75, 77-80

Na blockers are being developed. In experimental models, GS-458967 has shown to prevent arrhythmias by blocking late Na+81, 82 81 late INa in isolated ventricular myocytes and to prevent arrhythmias in intact hearts. So far, no human studies of GS-458967 have been reported. Another potent experimental blocker of the late Na+ current is F15845. However, this drug was developed to block 83 late INa in angina pectoris and was only experimentally tested in models of ischemia.

116 Chapter 7

As sodium channels are also involved in atrial arrhythmia, we discuss the impact of antiarrhythmic drug therapy on atrial arrhythmias. Pharmacological cardioversion with a history of structural or ischaemic heart disease.[84, 85] [86] conduction may facilitate 1:1 conduction through the AV node and thus a rapid ventricular response, resulting in paradoxical increase in ventricular rate. This can be avoided with the addition of a beta-blocking agent.[84, 85] In patients with persistent AF, drug therapy can either be focused on rate control (e.g. by administering beta-blockers, verapamil and/or digoxin) or rhythm control (e.g., by administering class 1C antiarrhythmic drugs). However, the drugs used with high risk for ventricular arrhythmia, e.g., BrS patients or patients with ischemic heart disease.[85] In all AF patients, therapy should also be focused on the prevention of thromboembolisms by antithrombotic therapy.[84]

Since the recognition of inherited Na+ channel disease, the cardiac Na+ channel is extensively studied. Both loss-of-function and gain-of-function mutations of the cardiac Na+ channel are associated with cardiac arrhythmia and sudden cardiac death. Pathophysiological mechanisms that may induce arrhythmia are being unravelled and include alterations in biophysical properties due to the mutation in SCN5A, drug use and circumstantial factors. Insights into the mechanisms of inherited Na+ channel disease may result in tailored therapy. However, due to the complexity of cardiac electrical activity and pathophysiological mechanisms, pharmacotherapy in cardiac sodium channel disease remains challenging. In all BrS patients, pharmacotherapy is mainly focused on the avoidance of drugs that reduce peak sodium current and thereby induce BrS ECG features or ventricular from chronic drug treatment, in particular, quinidine. However, these drugs do not completely prevent arrhythmias in all BrS patients and some of these drugs have been tested in only a small number of BrS patients. Late Na+ current disturbs cardiac electrical activity and may lead to intracellular Na+ and Ca2+ overload and prolongation of the AP. These patients should avoid other drugs that can prolong the AP. Blockade of late INa is the cornerstone of pharmacotherapy + current can

117

Na are currently tested for safety and effect.

Altered biophysical properties of cardiac Na+ channels are the basis of arrhythmias in patients with inherited Na+ channel diseases such as BrS and LQTS3. The effects of such biophysical derangements are strongly modulated by concomitant factors. For instance, BrS patients, who have reduced depolarization reserve due to a loss-of-function SCN5A mutations, are particularly vulnerable to the pro-arrhythmic effects of additional net

INa reducing factors, such as drugs, concomitant disease that reduces INa (e.g., cardiac ischemia, heart failure), and other factors such as fast heart rates. Tailored therapy that takes all these modulating factors into account is therefore required. Clearly, prevention of cardiac arrhythmia is the cornerstone in the clinical management of these patients. This is achieved by avoidance of certain drugs and proper management of other arrhythmia-provoking factors. Other drugs, some presently in development, may be used to treat or prevent arrhythmia in patients at high risk for arrhythmia (recurrence). + current. Future studies must resolve whether these drugs are also effective in the treatment of patients in whom late INa is increased from common acquired causes, e.g., heart failure. In this way, insights gained from the treatment of these rare patients with inherited arrhythmia syndromes may advance treatment strategies for the much larger groups of patients with common acquired diseases. At present, tailored drug therapy is best achieved by educating patients affected by Na+ channel disorders about which drugs they should avoid. Naturally, all physicians prescribing drugs should be aware of possible side effects or the prescribed medication. However, for the general practitioner or, in fact, for any physician not acquainted with inherited arrhythmia syndromes, adverse side effects of especially non-cardiac Na+ channel medication may come unexpectedly. Therefore, patients with inherited Na+ channel disorders should be provided with written information to present to their treating physician. An example of such a letter for BrS patients is available at www. brugadadrugs.org. Many patients with inherited Na+ channel disorders may go unnoticed until an additional trigger occurs that unmasks their vulnerability to cardiac arrhythmias. When prescribing Na+ channel blocking medication the physician should, while taking the a family history of sudden death, and other events possibly related to cardiac events. In case of suspicious events, it may be useful to obtain an ECG before and during initiation of the therapy. If the patient shows signs of conduction slowing, QT prolongation

118 Chapter 7 or BrS-like ECG changes while taking Na+ channel blocking medication a specialised cardiologist and/or geneticist should be consulted for further clinical or genetic testing, including the patient’s family. During further treatment the physician should seek to avoid the combination of factors that may together facilitate the occurrence of cardiac arrhythmias.

• Both loss-of-function (Brugada Syndrome) and gain-of-function (Long QT Syndrome 3) sodium channel mutations are associated with cardiac arrhythmias. • Reduced peak sodium current is the main arrhythmia mechanism in Brugada Syndrome. • Increased late sodium current is the main arrhythmia mechanism in Long QT Syndrome 3. • Cornerstone of pharmacotherapy in inherited sodium channel disease is the avoidance of drugs that affect the cardiac sodium current. • Tailored therapy is essential, taking into account multiple sodium channel modulating factors, e.g., drug use, concomitant disease.

119

* of interest ** of considerable interest

1. Smits JPP, Blom MT, Wilde AAM, et al. Cardiac sodium channels and inherited electrophysiologic 2. Venetucci L, Denegri M, Napolitano C, et al. Inherited calcium channelopathies in the pathophysiology 3. Giudicessi JR, Ackerman MJ. Potassium-channel mutations and cardiac arrhythmias--diagnosis and 48. * Review of the mechanisms underlying SCN5A-associated arrhythmia. 5. Adsit GS, Vaidyanathan R, Galler CM, et al. Channelopathies from mutations in the cardiac sodium * Review of inherited mutations in non-pore-forming proteins of cardiac sodium channel complexes that cause cardiac arrhythmia. 6. Wilde AA, Brugada R. Phenotypical manifestations of mutations in the genes encoding subunits of the 7. Schwartz PJ, Ackerman MJ, George AL, Jr., et al. Impact of genetics on the clinical management of 8. Tan HL. Sodium channel variants in heart disease: expanding horizons. J Cardiovasc Electrophysiol 9. Amin AS, Tan HL, Wilde AA. Cardiac ion channels in health and disease. Heart Rhythm 37. 11. Remme CA, Verkerk AO, Nuyens D, et al. Overlap syndrome of cardiac sodium channel disease in 94. 12. Bezzina CR, Barc J, Mizusawa Y, et al. Common variants at SCN5A-SCN10A and HEY2 are associated with Brugada syndrome, a rare disease with high risk of sudden cardiac death. Nat Genet 13. Hoshi M, Du XX, Shinlapawittayatorn K, et al. Brugada syndrome disease phenotype explained in 14. Keller DI, Rougier JS, Kucera JP, et al. Brugada syndrome and fever: genetic and molecular 15. Dumaine R, Towbin JA, Brugada P, et al. Ionic Mechanisms Responsible for the Electrocardiographic 16. Amin AS, Meregalli PG, Bardai A, et al. Fever increases the risk for cardiac arrest in the Brugada 17. Bardai A, Amin AS, Blom MT, et al. Sudden cardiac arrest associated with use of a non-cardiac drug that reduces cardiac excitability: evidence from bench, bedside, and community. Eur Heart J

120 Chapter 7

** Study from bench to community showing the association between sudden cardiac arrest and the use of noncardiac drugs. 18. Priori SG, Wilde AA, Horie M, et al. HRS/EHRA/APHRS expert consensus statement on the diagnosis and management of patients with inherited primary arrhythmia syndromes: document endorsed by HRS, EHRA, and APHRS in May 2013 and by ACCF, AHA, PACES, and AEPC in * Consensus statement on diagnosis and management of patients with inherited arrhythmia syndromes. 19. Näbauer M, Beuckelmann D, Überfuhr P, et al. Regional differences in current density and rate- dependent properties of the transient outward current in subepicardial and subendocardial myocytes 20. Brugada P, Brugada J. Right bundle branch block, persistent ST segment elevation and sudden cardiac 21. Wilde AAM. Proposed Diagnostic Criteria for the Brugada Syndrome: Consensus Report. Circulation 22. Antzelevitch C, Brugada P, Borggrefe M, et al. Brugada syndrome: report of the second consensus conference: endorsed by the Heart Rhythm Society and the European Heart Rhythm Association. 23. Chen Q, Kirsch GE, Zhang D, et al. Genetic basis and molecular mechanism for idiopathic ventricular cardmoc/ 25. Kapplinger JD, Tester DJ, Alders M, et al. An international compendium of mutations in the SCN5A- encoded cardiac sodium channel in patients referred for Brugada syndrome genetic testing. Heart 26. Deschenes I, Baroudi G, Berthet M, et al. Electrophysiological characterization of SCN5A mutations causing long QT (E1784K) and Brugada (R1512W and R1432G) syndromes. Cardiovascular 27. Valdivia CR, Ackerman MJ, Testerb DJ, et al. A novel SCN5A arrhythmia mutation, M1766L, with 30. Pfahnl AE, Viswanathan PC, Weiss R, et al. A sodium channel pore mutation causing Brugada 31. Baranchuk A, Nguyen T, Ryu MH, et al. Brugada phenocopy: new terminology and proposed 32. Hoogendijk MG, Opthof T, Postema PG, et al. The Brugada ECG pattern: a marker of channelopathy, structural heart disease, or neither? Toward a unifying mechanism of the Brugada syndrome. Circ 33. Yan GX, Antzelevitch C. Cellular Basis for the Brugada Syndrome and Other Mechanisms of 72. 35. Wilde AA, Postema PG, Di Diego JM, et al. The pathophysiological mechanism underlying Brugada

121

* Scholarly debate on the pathophysiological mechanism underlying Brugada syndrome. 36. Szel T, Antzelevitch C. Abnormal Repolarization as the Basis for Late Potentials and Fractionated Electrograms Recorded from Epicardium in Experimental Models of Brugada Syndrome. J Am Coll Cardiol 2014. with clinical signs of Brugada syndrome: a combined electrophysiological, genetic, histopathologic, 38. Postema PG, van Dessel PF, de Bakker JM, et al. Slow and discontinuous conduction conspire in Brugada syndrome: a right ventricular mapping and stimulation study. Circ Arrhythm Electrophysiol 39. Catalano O, Antonaci S, Moro G, et al. Magnetic resonance investigations in Brugada syndrome 40. van Hoorn F, Campian ME, Spijkerboer A, et al. SCN5A mutations in Brugada syndrome are 41. Schott JJ, Alshinawi C, Kyndt F, et al. Cardiac connduction defects associate with mutations in 42. Benson DW, Wang DW, Dyment M, et al. Congenital sick sinus syndrome caused by recessive 43. Groenewegen WA. A Cardiac Sodium Channel Mutation Cosegregates With a Rare Connexin40 44. Ge J, Sun A, Paajanen V, et al. Molecular and clinical characterization of a novel SCN5A mutation associated with atrioventricular block and dilated cardiomyopathy. Circ Arrhythm Electrophysiol 45. McNair WP, Ku L, Taylor MR, et al. SCN5A mutation associated with dilated cardiomyopathy, 46. Kaufman ES. Mechanisms and clinical management of inherited channelopathies: long QT syndrome, Brugada syndrome, catecholaminergic polymorphic ventricular tachycardia, and short QT syndrome. 48. Postema PG, Wolpert C, Amin AS, et al. Drugs and Brugada syndrome patients: review of the literature, recommendations, and an up-to-date website (http://www.brugadadrugs.org). Heart ** An up-to-date list of drugs that should be avoided by Brugada patients. 51. Casini S, Verkerk AO, van Borren MM, et al. Intracellular calcium modulation of voltage-gated 52. Viswanathan PC, Bezzina CR, George AL, et al. Gating-Dependent Mechanisms for Flecainide Action 53. Theodotou N, Cillo JE, Jr. Brugada syndrome (sudden unexpected death syndrome): perioperative 5.

122 Chapter 7

54. Kloesel B, Ackerman MJ, Sprung J, et al. Anesthetic management of patients with Brugada syndrome: 55. Bebarta VS, Summers S. Brugada electrocardiographic pattern induced by cocaine toxicity. Ann 56. Xu Y-Q, jr. WJC, Clarkson CW. Cocaethylene, a metabolite of cocaine and etanol, is a potent blocker contributes importantly to drug-induced long QT syndrome. Circ Arrhythm Electrophysiol ** Experimental study showing the contribution of noncardiac drugs and drug metabolism to cardiac arrhythmia. 59. Suzuki H, Torigoe K, Numata O, et al. Infant case with a malignant form of Brugada syndrome. J 60. Watanabe A, Fukushima Kusano K, Morita H, et al. Low-dose isoproterenol for repetitive ventricular 61. Ohgo T, Okamura H, Noda T, et al. Acute and chronic management in patients with Brugada 700. 62. Miyazaki T, Mitamura H, Miyoshi S, et al. Autonomic and antiarrhythmic drug modulation of ST 63. Kyriazis K, Bahlmann E, van der Schalk H, et al. Electrical storm in Brugada syndrome successfully 64. Alings M, Dekker L, Sadee A, et al. Quinidine induced electrocardiograpic normalization in two 67. Hermida JS, Denjoy I, Clerc J, et al. Hydroquinidine therapy in Brugada syndrome. J Am Coll Cardiol 68. Sharif-Kazemi MB, Emkanjoo Z, Tavoosi A, et al. Electrical storm in Brugada syndrome during 69. Mizusawa Y, Sukarada H, Nishizaki M, et al. Effects of low-dose quinidine on ventricular 70. Nakagawa E, Takagi M, Tatsumi H, et al. Succesful radiofrequency catheter ablation for electrical 71. Viskin S, Wilde AA, Tan HL, et al. Empiric quinidine therapy for asymptomatic Brugada syndrome: oral phosphodiesterase inhibitor, in a patient with Brugada syndrome. J Cardiovasc Electrophysiol

123

74. Moreno JD, Clancy CE. Pathophysiology of the cardiac late Na current and its potential as a drug * Review on the pathophysiologic mechanisms underlying late sodium current and the tailored therapeutic strategies to alter these mechanisms to prevent triggered arrhythmia. 75. Moss AJ, Zareba W, Schwarz KQ, et al. Ranolazine shortens repolarization in patients with sustained inward sodium current due to type-3 long-QT syndrome. J Cardiovasc Electrophysiol 76. Karwatowska-Prokopczuk E, Wang W, Cheng ML, et al. The risk of sudden cardiac death in patients with non-ST elevation acute coronary syndrome and prolonged QTc interval: effect of ranolazine. 77. Antzelevitch C, Burashnikov A, Sicouri S, et al. Electrophysiologic basis for the antiarrhythmic actions 78. Rajamani S, El-Bizri N, Shryock JC, et al. Use-dependent block of cardiac late Na(+) current by 79. Shah DP, Baez-Escudero JL, Weisberg IL, et al. Ranolazine safely decreases ventricular and atrial 80. van den Berg MP, van den Heuvel F, van Tintelen JP, et al. Successful treatment of a patient with symptomatic long QT syndrome type 3 using ranolazine combined with a beta-blocker. Int J Cardiol 81. Belardinelli L, Liu G, Smith-Maxwell C, et al. A novel, potent, and selective inhibitor of cardiac late

** Experimental study showing the anti-arrhythmic potential of a novel late INa inhibitor. 82. Sicouri S, Belardinelli L, Antzelevitch C. Antiarrhythmic effects of the highly selective late sodium 83. Vacher B, Pignier C, Letienne R, et al. F 15845 inhibits persistent sodium current in the heart and 84. Fuster V, Rydén LE, Cannom DS, et al. ACC/AHA/ESC 2006 Guidelines for the Management of 8. 87. Antzelevitch C. The Brugada Syndrome: Ionic basis and arrhythmia mechanisms. J Cardiovasc

124 CHAPTER 8

A. Bardai*, A.S. Amin*, M.T. Blom*, C.R. Bezzina, J. Berdowski, P.N.J. Langendijk, L. Beekman, C.A. Klemens, P.C. Souverein, R.W. Koster, A. de Boer, H.L. Tan *These authors contributed equally

Eur Heart J. 2013;34(20):1506-16.

Non-cardiac drugs that impair cardiac repolarization (electrocardiographic QT- prolongation) are associated with increased sudden cardiac arrest (SCA) risk. Emerging evidence suggest that non-cardiac drugs that impair cardiac depolarization and excitability (electrocardiographic QRS-prolongation) also increase SCA risk. Nortriptyline, which blocks the SCN5A-encoded cardiac sodium channel, may exemplify such drugs. We aimed to study whether nortriptyline increase SCA risk, and to establish the underlying mechanisms.

We studied QRS-durations at rest/exercise in an index patient who experienced ventricular tachycardia during exercise while using nortriptyline, and compared them to those of 55 controls with/without nortriptyline and 24 controls with BrS without nortriptyline who carried an SCN5A mutation. We performed molecular- genetic (exon-trapping) and functional (patch-clamp) experiments to unravel the mechanisms of QRS-prolongation by nortriptyline and the SCN5A mutation found in the index patient. We conducted a prospective community-based study among 944 victims of ECG-documented SCA and 4354 matched controls to determine the risk for SCA associated with nortriptyline use. Multiple mechanisms may act in concert to increase SCA risk during nortriptyline use. Pharmacological (nortriptyline), genetic (loss-of-function SCN5A mutation) and/or functional (sodium channel inactivation at fast heart rates) factors conspire to reduce cardiac sodium current and increase SCA risk. Nortriptyline use in the community was associated with 4.5-fold increased SCA risk (adjusted OR 4.5 [95%CI 1.1-19.5]), particularly when other sodium channel blocking factors were present.

Nortriptyline increases SCA risk in the general population, particularly in the presence of genetic and/or non-genetic factors that decrease cardiac excitability by blocking the cardiac sodium channel.

126 Chapter 8

Sudden cardiac arrest (SCA) accounts for 50% of cardiovascular deaths in Western societies.12 and is Its survival rate is dismal (<10%).3 The best solution is to prevent SCA by recognizing causative pathophysiologic processes and identifying subjects at risk. Intensive translational research on rare monogenic inherited arrhythmia syndromes (e.g., long QT syndrome) has established the role of cardiac ion channel dysfunction (e.g., caused by very low-frequency gene variants) in SCA risk.4 These insights may be applicable to understand the causes of SCA in common conditions. For instance, the risk for VT/VF and SCA may be increased by non-cardiac drugs (drugs for non-cardiac disease) that impair cardiac repolarization, thereby prolonging the QT- interval.5 In some patients, the genetic basis of this acquired long QT syndrome was cardiac repolarization.6 In conjunction with drugs that block repolarizing currents, these variants impair repolarization, culminating in VT/VF.7 Impaired cardiac depolarization and excitability may also cause VT/VF by facilitating re-entrant excitation. This possibility has so far received less recognition. Depolarization of the sarcolemma initiates the cardiac action potential, which triggers depolarization reduces cardiac excitability and causes QRS-prolongation. This may 8-9 result from sodium current (INa) reduction due to sodium channel dysfunction, for example by mutations in SCN5A, the gene that encodes the major subunit of the cardiac sodium channel. SCN5A -related inheritable arrhythmia syndromes include Brugada 4 8 syndrome (BrS) and cardiac conduction disease. INa reduction may also be acquired by drugs, particularly in the presence of concomitant diseases which reduce cardiac excitability. This was shown in the CAST trial, where, class 1C cardiac anti-arrhythmic drugs (established cardiac sodium channel blockers) caused excess mortality in patients with increased susceptibility due to cardiac ischemia or heart failure.10 Accordingly, these drugs may also induce VT/VF in BrS patients.11 Suspicion is mounting that non-cardiac depolarization-blocking drugs may also increase susceptibility to SCA. Small studies found that some antidepressant drugs (e.g., nortriptyline) may induce BrS ECG-pattern and SCA.12-13 These non-cardiac drugs can block the sodium channel,14 thereby increasing the risk for VT/VF, and SCA, particularly when other conditions that also reduce INa (perhaps inherited) are present. To explore this new concept, we studied nortriptyline as an example of such drugs. The causative mechanisms were investigated in a patient with repeated syncope and VT during therapeutic nortriptyline use. We performed clinical, molecular-genetic, and that conspired to unleash the pro-arrhythmic potential of nortriptyline. Furthermore,

127

we conducted a prospective community-based study and determined the risk for SCA associated with nortriptyline use in the general population.

This study was approved by the local ethics committee, and conforms to the principles outlined in the Declaration of Helsinki. All patients gave informed consent for participation. An expanded Methods description is provided in the Supplemental Methods.

Study groups group, including 20 otherwise healthy men (49±1yrs) treated with 25-50mg nortriptyline including 24 men (43±2yrs) with BrS who carried an SCN5A mutation but used no nortriptyline.

ECG analysis rate and QRS-interval (leads V5 or II) were analyzed. Within one individual, the mean The index patient (group 1), healthy ECG-control men (group 2), and BrS patients (group 4) underwent a symptom limited treadmill test using the Bruce protocol. ECGs were analyzed at different time points during exercise and at peak exercise.

The coding and splice-site regions of SCN5A were analyzed in the index patient using the patient and 1740 unrelated control individuals of European descent (DNA-controls). We searched for the presence of the mutation in the 1000Genomes database,15 the NHLBI Exome Variant Server,16 and the Genome of the Netherlands Project (GoNL).17 cDNA analysis Blood samples from the index patient and two control individuals of European descent (SCN5A-controls) were collected in PAXgene tubes. Total RNA was isolated from

128 Chapter 8 blood lymphocytes. SCN5A sequenced.

A 622 base pairs SCN5A and mutant fragments were cloned and subjected to exon-trapping for interrogation Technologies). Splicing products were investigated by PCR and sequencing.

The megaprimer approach was used to generate mutant SCN5A cDNA in which the SCN5A cDNA in the pCGI plasmid backbone were used in electrophysiologic studies.

Using the patch-clamp technique, sodium currents were measured in HEK-293 cells transfected with wild-type or mutant SCN5A constructs. Cardiac action potentials were measured in left ventricular myocytes isolated from healthy rabbit hearts.18

We conducted a case-control study using the AmsteRdam REsuscitation STudies (ARREST) infrastructure. ARREST is an ongoing, prospective, community-based study that we designed to establish the genetic19 and clinical20 determinants of SCA in the general population with a particular emphasis on drug use. Accordingly, we prospectively collect complete medical and medication data of all patients who suffer out-of-hospital SCA with ECG-documented VT/VF in a contiguous region of the Netherlands (~2.4 million inhabitants, >95% coverage).19,20 Matched controls (by age, sex, and index [SCA] date) are randomly drawn from the general community, using a database of community pharmacies that contains demographic and complete medication data (>2 million individuals, PHARMO Record Linkage System).21 Since nearly all patients in the Netherlands are registered at a single community pharmacy, independent of prescriber, pharmacy records are essentially complete.22 Each case is from July 2005 to January 2008.

129

1 2

All medication prescriptions of ARREST-cases and PHARMO-controls during the year or within a maximum of 10% after the prescribed duration (to deal with carry-over effects).

For cases who used nortriptyline, we investigated whether the presence of additional factors that impair cardiac excitability by retrieving data from hospital of admission and/or general practitioner. The following factors known to impair cardiac excitability were assessed: other (than nortriptyline) drugs that block the cardiac sodium channel, exercise prior to SCA, fever, (acute) cardiac ischemia, and heart failure. The presence of SCN5A mutations was assessed in cases who used nortriptyline and of whom DNA was available.

For all cases and PHARMO-controls, the following risk factors for SCA were assessed: cardiac ischemia, heart failure, hypertension, diabetes mellitus, and hypercholesterolemia. Risk factors were derived from medication use.23 Use of QT prolonging drugs,

130 Chapter 8

antiarrhythmic drugs, cardiac glycosides, diuretics, angiotensin converting enzyme- inhibitors, angiotensin receptor-blocking agents, -adrenoceptor blockers and calcium channel blockers were considered covariates.

Data are mean±SEM. Group comparisons were made with Student’s t-test or Analysis of Variance. Differences for the ECG parameters in baseline values were tested between groups with Student’s t-test (Figures 5A and 5B). Differences for maximum upstroke velocities were tested between groups with one-way repeated measures ANOVA (Figure 8C). The relative risk for VT/VF associated with nortriptyline use was estimated by conditional logistic regression analysis. Covariates and risk factors that were univariately associated with VT/VF (p<0.1) were included in the regression analyses if they changed the point estimate of the association between nortriptyline use and VT/VF by >5%.24 All analyses were performed using SPSS for Mac (version 16.0 for Mac, Chicago IL, USA).

The index patient, a 35-year-old Caucasian man without cardiac history or family history of sudden death, presented with repeated syncope during running (he had started training

131

1 21 2

132 Chapter 8

(A) carried an nortriptyline (No nortriptyline) using Student for a marathon). His history was unremarkable, except for depression for which he had used nortriptyline 150mg OD for one year. His resting ECG while on nortriptyline displayed sinus tachycardia (102 beats per minute [bpm]) with prolonged PR (223ms) and QRS (131ms) intervals and no further abnormalities. Physical examination, serum electrolytes, echocardiography, and coronary angiography were unremarkable. During exercise testing, his QRS-interval prolonged to 180ms and his ST-segment in V1-V2 elevated from 0.17mV to 0.58mV at the maximum heart rate of 180bpm (Figure 1). At this point, he developed polymorphic VT with syncope (Figure 2). He recovered spontaneously, and his ECG-parameters returned to pre-exercise levels. Nortriptyline was discontinued and exercise testing was repeated. This time, his resting ECG displayed sinus rhythm at a normal rate (73bpm) with PR (208ms) and QRS (110ms) intervals at the upper limit of normal. At the maximum heart rate of 187bpm, his QRS-interval prolonged to 143ms and his ST-segment in V1-V2 elevated from 0.06mV to 0.55mV (Figure 3), but no arrhythmia or syncope occurred. Because the ST-segment elevation during exercise was suggestive of BrS, he underwent drug challenge with the sodium- channel blocking antiarrhythmic drug ajmaline. Ajmaline caused QRS-prolongation echocardiography and exercise tests did not indicate alternative explanations for these typical ECG-changes, e.g. ischemic heart disease, heart failure or cardiomyopathy.

133

ECG analysis In the absence of nortriptyline the baseline ECG of the index patient exhibited QRS- prolongation of similar magnitude as that of patients who used 150mg nortriptyline, or nortriptyline and in BrS patients compared to ECG controls who used no nortriptyline (P<0.001 for both comparisons). Nortriptyline induced additional QRS prolongation in the index patient (Figure 5B). The exercise testing ECG in the index patient in the absence of nortriptyline evoked QRS-prolongation, similar to BrS patients, while exercise testing in the index patient during nortriptyline use induced extreme QRS-

. (B) -control individuals

134 Chapter 8

protein.

prolongation when heart rates exceeded 150bpm, followed by VT (Figure 5C). QRS- duration exercise remained unchanged in ECG-controls.

Mutation analysis revealed a cytosine to thymine nucleotide change at 95 base pairs SCN5A, which was not found in 1740 unrelated individuals. This nucleotide change was found in his father, who tested positive for BrS during ajmaline-testing, but not his mother, who tested negative during ajmaline-testing. Other relatives declined investigation. The c.4719C>T mutation does not cause an amino acid substitution. Instead, it is expected

135

to cause aberrant mRNA splicing by creating a novel premature splice site through the generation of a sequence within exon 27 that more closely resembles the splice donor site consensus sequence than the natural splice donor site in intron 27 (Figure 6C). Analysis of SCN5A-transcripts from peripheral lymphocytes from the index patient corresponding to abnormal splicing at the novel splice site generated by the mutation (Figure 6B-C). Only the normal splicing product containing the complete exon 27 was SCN5A we constructed wild-type and mutant hybrid minigenes in an exon-trapping vector and analyzed the splicing products in HEK-293 cells. The wild-type minigene consistently generated wild-type transcripts, while the mutant minigene generated both wild-type transcripts and transcripts with the terminal 96 bases of exon 27 deleted (not shown).

Deletion of the terminal 96 bases of exon 27 causes in-frame deletion of amino acids 1574-1605 of the channel protein, thereby disrupting the transmembrane segments S2 and S3 of channel domain 4 (Figure 7). This may affect channel protein folding, SCN5A constructs displayed typical inward sodium currents, but no currents could be recorded from cells expressing the construct in which the terminal 96 base pairs of exon 27 were deleted (Figure 8A). As INa through functional cardiac sodium channels triggers the initial phase of the cardiac action potential, we measured the maximum upstroke velocity of this initial phase to probe sodium channel availability (Figure 8B).25 Exposure of ventricular myocytes to 1μM nortriptyline (equivalent to serum levels during chronic use of 150mg nortriptyline) slowed the maximum upstroke velocity, indicating decreased sodium channel availability. Repetitive stimulation of myocytes at increasing frequencies (to mimic increasing heart rates during exercise) progressively slowed maximum upstroke velocity even in the absence of nortriptyline. In the presence

To determine whether increased risk for SCA following nortriptyline use applies to the general population, we conducted a community-based study on the association of was 65 years in cases (77% male), and 65 years in PHARMO-controls (77% male). To indicate that the data acquisition system was valid, the known risk factors for SCA were associated with SCA. Use of cardiovascular medication was also associated established pro-arrhythmic potential of these drugs (Table 1). Four cases (0.4%) and

136 Chapter 8

availability.

(wild-type or c4719C>T), obtained by

137

Gender Male 729 (77.2) 3374 (77.5) 215 (22.8) 980 (22.5) Age in years, mean (SD) 64.8 (15.0) 64.7 (14.9) 365 (38.7) 931 (21.4) 2.7 (2.3–3.2) Hypertension 415 (44.0) 1273 (29.2) 2.1 (1.8–2.4) Diabetes mellitus 163 (17.3) 428 (9.8) 2.0 (1.6–2.4) 142 (15.0) 148 (3.4) 5.4 (4.2–6.9) Dyslipidemia 337 (35.7) 965 (22.2) 2.1 (1.8–2.4) QT prolonging drugs† 39 (4.1) 100 (2.3) 1.9 (1.3–2.7) 18 (1.9) 62 (1.4) 1.4 (0.8–2.3) Cardiac glycosides 86 (9.1) 104 (2.4) 4.6 (3.3–6.2) 359 (38.0) 906 (20.8) 2.7 (2.3–3.2) 288 (30.5) 603 (13.9) 2.9 (2.5–3.5) Angiotensin receptor blocking drugs 140 (14.8) 414 (9.5) 1.7 (1.4–2.1) 335 (35.5) 894 (20.5) 2.3 (1.9–2.7) 163 (17.3) 427 (9.8) 2.0 (1.6–2.4)

prolonging drugs, www.azcert.org.

OR n=944 Non–use 940 (99.58) 4350 (99.91) Current use 4 (0.42) 4 (0.09) 4.6 (1.2–18.4) p=0.03

138 Chapter 8 four PHARMO-controls (0.1%) were current users of nortriptyline on the index date. Use of nortriptyline was associated with a 4.6-fold increased risk for SCA (OR 4.6 [1.2–18.4] p=0.03, Table 2). After evaluation of the risk factors, we found no relevant confounding. In support of a multiple-hit model, in which multiple sodium channel blocking factors add up to result in disease-causing sodium channel blockage, all cases who used nortriptyline at the time of SCA had one or more additional causes for sodium channel blockage, in addition to nortriptyline use (Supplemental Table 1). While one causes for sodium-channel blockage: use of other cardiac sodium channel blocking drugs (lithium14, ropinirole26), ischemia and/or acute myocardial infarction,27 and heart failure.10SCN5A mutations were found in these patients.

We provide proof-of-concept from basic, clinical and epidemiologic studies that blockage of the cardiac sodium channel by therapeutic dosages of a non-cardiac drug (nortriptyline) may be critical and cause life-threatening arrhythmias. This applies in particular when other causes of cardiac sodium channel block are also present, e.g., genetic variants (loss-of-function mutation in SCN5A) and/or functional factors (reduction of sodium current by fast heart rates). Accordingly, use of nortriptyline in the general population is associated with a 4.6-fold increased risk for SCA. Future studies guidelines for the safe use of such drugs. Nortriptyline was initially proposed as a anti-arrhythmic drug, because its cardiac actions resemble those of Class 1A and 1C antiarrhythmic drugs.28 However, later studies found that it might be associated with SCA,13,29 similar to Class 1A and 1C antiarrhythmic drugs.10 Nevertheless, it has remained unknown why nortriptyline evokes arrhythmias at therapeutic dosages in some patients, but not in all. Cellular studies revealed that nortriptyline blocks the cardiac sodium channel.30 Ventricular 10 tachyarrhythmias may occur when INa is severely reduced. Yet, such reduction is normally only achieved with nortriptyline overdosage. Thus, when therapeutic nortriptyline dosages are used (as by the index patient), other predisposing factors are required to reduce INa occurrence. Recognizing such factors is crucial to prevent arrhythmias in individuals who use nortriptyline. In the index patient, an SCN5A loss-of-function mutation and exercise-related fast heart rates acted in concert with nortriptyline to reduce INa to a critical level. The important role of fast heart rates in causing VT in the index patient was supported by the observations that VT and syncope occurred only during exercise,

139

while they had not occurred in the year prior to this analysis, when he had already used nortriptyline, but had not trained for the marathon. Our data are supported by a recent experimental study in isolated canine right ventricular wedge preparations showing that amitriptyline (another tricyclic antidepressant) unmasked BrS ECG pattern and facilitated the development of arrhythmias only when INa was further counteracted by an increase in the repolarizing potassium current Ito, which opposes INa during the initial phase of the cardiac action potential.31

The c.4719C>T mutation abolished INa completely (Figure 8A). In the index patient, who was heterozygous for this mutation, this is likely to result in 50% loss in his resting QRS-intervals in the absence of nortriptyline were prolonged to a similar extent as in ECG-controls who used 150mg nortriptyline (Figure 5A). Fast heart rates, as mimicked by stimulation of ventricular myocytes at high frequencies, decreased the availability of cardiac sodium channels because of their intrinsic opening and closing behavior (Figure 8C). Opening and closing result from transitions of channel domains between different conformational states. Sodium channels open (activate) during the initial phase of the cardiac action potential, and inactivate within milliseconds thereafter. Before they can re-open, channels need time to re-enter their original conformational state (recovery from inactivation) during the diastolic interval that ends at the beginning of the next heart beat. At faster heart rates, this interval becomes too short for full recovery, causing reduction in the fraction of channels available for opening.32 The resulting INa maximum upstroke velocity of ventricular action potentials at higher stimulation frequencies (Figure 8C), and the lack of change in QRS-duration in ECG-controls during exercise (Figure 5C). However, when INa is already reduced at baseline (e.g., due to an SCN5A mutation), additional INa to reduce cardiac excitability, as indicated by QRS-prolongation in BrS patients, including the index patient, during exercise (Figure 5C). In addition, fast heart rates allow nortriptyline to reduce the cardiac sodium current, because nortriptyline requires repetitive opening and closing of sodium channels to act as blocker (use-dependent block),33 as it blocks these channels more effectively in their inactivated state. Thus, nortriptyline blocks cardiac sodium channels more potently at fast heart rates, when the QRS-prolongation in the index patient when heart rates exceeded 150bpm during nortriptyline use (Figure 1 and Figure 5C), and by the reduction of the maximum upstroke velocity of ventricular action potentials in the presence of nortriptyline at higher stimulation frequencies (Figure 8C). Having determined how nortriptyline may cause potentially lethal cardiac sodium channel block, we determined whether nortriptyline use increases SCA risk in the general population. We found that this risk is increased 4.6-fold, in agreement

140 Chapter 8 with previous studies that linked established cardiac sodium channel blocking drugs (Class 1A and 1C antiarrhythmic drugs) to SCA.12-14 index patient that multiple factors may be required to cause critical sodium channel blockade and potentially lethal cardiac arrhythmias, we found that all four SCA cases who used nortriptyline in the community-based study had at least one additional cause of sodium channel blockade. These causes included concomitant use of other cardiac or non-cardiac drugs that block the cardiac sodium channel,34 ischemia,27 and heart failure.10 Although none of the genotyped cases who used nortriptyline had a mutation in SCN5A, this does not rule out that genetic predisposition may have contributed to and functional properties of sodium channels may also reduce INa and render individuals more susceptible to VT/VF. Yet, these genes were not tested here.4 that additional causes of sodium channel blockade may be required for nortriptyline to cause SCA is analogous to studies into the risk for SCA of QT-prolonging drugs. In those studies, various risk factors that are associated with QT-prolongation increase the risk for SCA upon use of QT-prolonging drugs.5 the index patient, contributes to SCA risk provides support to the growing realization that susceptibility to complex disease might stem from rare variants acting in concert ways to uncover the possible disease-causing potential of mutations that are silent on the coding level.35 In conclusion, we conducted comprehensive clinical, molecular-genetic, functional, and community-based studies on the association between nortriptyline use and lethal cardiac arrhythmias. We demonstrated that nortriptyline at therapeutic dosages may block the cardiac sodium channel to a critical level and cause life-threatening arrhythmias when other genetic and/or non-genetic causes of cardiac sodium channel block are also present. Accordingly, we found that nortriptyline use increases the risk for be extremely careful in prescribing nortriptyline to subjects with QRS-prolongation at baseline, subjects suspected of BrS, or subjects using other cardiac or non-cardiac drugs known to possess the ability to block the cardiac sodium channel.34 For the latter, the website “www.brugadadrugs.org”, which we have recently initiated to ensure worldwide availability of information on safe drug use in BrS patients, may be used. An exercise before prescribing nortriptyline. However, future studies must establish which screening strategies are most effective to identify individuals at risk for excess mortality from the use of nortriptyline or other non-cardiac drugs that block the cardiac sodium channel.

141

Esther Landman, and Renate van der Meer to the data collection, data entry, and patient follow-up at the Academic Medical Center, and are greatly indebted to the dispatch centers, ambulance paramedics cooperation and support. The authors would like to thank the NHLBI GO Exome Sequencing Project and its ongoing studies which produced and provided exome variant calls for comparison: the Lung GO Sequencing Project (HL-102923), the WHI Sequencing Project (HL-102924), the Broad GO Sequencing Project (HL-102925), the Seattle GO Sequencing Project (HL-102926) and the Heart GO Sequencing Project (HL-103010). This study makes use of data generated by the Genome of the Netherlands Project. A full list of the investigators is available from http://www.dutchgenomeproject.com/. Funding for the project dated July 9, 2009 and made available as a Rainbow Project of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL). The sequencing was carried out in collaboration with the Beijing Institute for Genomics (BGI).

Vici 918.86.616), the Dutch Medicines Evaluation Board (MEB/CBG), the European Community’s Seventh Framework Programme (FP7, grant 241679, ARITMO), and Biobanking and Biomolecular Research Infrastructure The Netherlands (BBMRI-NL). Dr. Koster was supported by Physio Control and the Netherlands Heart Foundation (grant 2006B179). Dr. Bardai was supported by the Netherlands preparation, review, or approval of the manuscript.

142 Chapter 8

1. Myerburg RJ, Castellanos A. Cardiac arrest and sudden cardiac death. In: Libby P, Bonow RO, Mann DL, Zipes DP, eds. Braunwald’s Heart Disease: A textbook of cardiovascular medicine. Oxford, UK, 2. Huikuri HV, Castellanos A, Myerburg RJ. Sudden death due to cardiac arrhythmias. N Engl J Med. 126. 6. Splawski I, Timothy KW, Tateyama M, et al. Variant of SCN5A sodium channel implicated in risk of 8. Schott JJ, Alshinawi C, Kyndt F, et al. Cardiac conduction defects associate with mutations in SCN5A. 9. Fukuda K, Davies SS, Nakajima T, et al. Oxidative mediated lipid peroxidation recapitulates 10. Greenberg HM, Dwyer EM Jr, Hochman JS, Steinberg JS, Echt DS, Peters RW. Interaction of 11. Antzelevitch C, Brugada P, Borggrefe M, et al. Brugada syndrome: report of the second consensus conference: endorsed by the Heart Rhythm Society and the European Heart Rhythm Association. 12. Monteban-Kooistra WE, van den Berg MP, Tulleken JE, Ligtenberg JJ, Meertens JH, Zijlstra JG. Brugada electrocardiographic pattern elicited by cyclic antidepressants overdose. Intensive Care Med. 13. Weeke P, Jensen A, Folke F, et al. Antidepressant use and risk of out-of-hospital cardiac arrest: a 14. Darbar D, Yang T, Churchwell K, Wilde AA, Roden DM. Unmasking of Brugada syndrome by 16. Exome Variant Server, NHLBI Exome Sequencing Project (ESP), Seattle, WA.: http://evs. 18. Casini S, Verkerk AO, van Borren MM, et al. Intracellular calcium modulation of voltage-gated sodium channels in ventricular myocytes. Cardiovasc Res.-81. 20. Bardai A, Berdowski J, van der Werf C, et al. Incidence, causes and outcome of out-of-hospital cardiac arrest in children: a comprehensive, prospective, population-based study in The Netherlands. J Am http://www.pharmo.nl.

143

22. Buurma H, Bouvy ML, De Smet PA, Floor-Schreudering A, Leufkens HG, Egberts AC. Prevalence and determinants of pharmacy shopping behaviour. 23. Maitland-van der Zee AH, Klungel OH, Stricker BH, et al. Repeated nitrate prescriptions as a potential marker for angina pectoris. A comparison with medical information from the Rotterdam 24. Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health. 25. Shaw RM, Rudy Y. Ionic mechanisms of propagation in cardiac tissue. Roles of the sodium and L-type calcium currents during reduced excitability and decreased gap junction coupling. Circ Res. 26. Simkó J, Szentandrássy N, Harmati G, et al. Effects of ropinirole on action potential characteristics and the underlying ion currents in canine ventricular myocytes. Naunyn Schmiedebergs Arch Pharmacol. 27. Nattel S, Maguy A, Le Bouter S, Yeh YH. Arrhythmogenic ion-channel remodeling in the heart: heart 28. Giardina EG, Barnard T, Johnson L, Saroff AL, Bigger JT Jr, Louie M. The antiarrhythmic effect of nortriptyline in cardiac patients with ventricular premature depolarizations. J Am Coll Cardiol. 29. Sicouri S, Antzelevitch C. Sudden cardiac death secondary to antidepressant and antipsychotic drugs. 30. Sudoh Y, Cahoon EE, Gerner P, Wang GK. Tricyclic antidepressants as long-acting local anesthetics. 31. Minoura Y, Di Diego JM, Barajas-Martínez H, et al. Ionic and cellular mechanisms underlying the development of acquired Brugada syndrome in patients treated with antidepressants. J Cardiovasc 32. Casini S, Tan HL, Bhuiyan ZA, et al. Characterization of a novel SCN5A mutation associated with Brugada syndrome reveals involvement of DIIIS4-S5 linker in slow inactivation. Cardiovasc Res. 33. Ogata N, Narahashi T. Block of sodium channels by psychotropic drugs in single guinea-pig cardiac 34. Postema PG, Wolpert C, Amin AS, et al. Drugs and Brugada syndrome patients: review of the literature, recommendations, and an up-to-date website (www.brugadadrugs.org). Heart Rhythm.

144 Chapter 8

Informed consent was obtained from the index patient, and genomic DNA was extracted from peripheral (PCR). Mutation detection was performed by denaturing high performance liquid chromatography, mutation was tested in the parents of the index patient and in 1740 unrelated control individuals of European descent (DNA-controls). We also looked up the presence of the mutation in the 1000Genomes database24, the NHLBI Exome Variant Server25 and the Genome of the Netherlands Project (GoNL)26.

cDNA analysis Blood samples from the patient and two control Caucasian individuals (SCN5A-controls) were collected in PAXgene tubes. Total RNA was isolated from blood lymphocytes. SCN5A transcripts were reverse PCR products were sequenced.

as template. PCR products were subsequently cloned into the pSPL3 exon-trapping vector of the Exon DNA sequencing. Wild-type and mutant pSPL3-exon 27 plasmids were transfected into human embryonic kidney (HEK-293) cells using EscortV transfection reagent (Sigma). Forty-eight hours post-transfection, cells were harvested and total RNA was isolated. Splicing products were reverse transcribed and PCR- PCR-products were analyzed using electrophoresis and DNA sequencing.

96 base pairs of exon 27 were deleted. The megaprimer was synthesized by PCR using wild- type SCN5A cDNA as template and 5’CCATGAAGAAGCTGGGCTCCA3’ (F) and GATGATGTCCGAGAGCACAGTGCCTGTGAAGATGGCCACAAAGAG3’ (R) as primers. The 5’AGGGTGGGAAGGAAGTGGAG3’ (R) and genomic DNA as template. This PCR product was cloned into the pSP64T-SCN5A construct via BstEII and BsaAI restriction sites. Subsequently, mutant SCN5A

145

cDNA was subcloned into the expression vector pCGI for bicistronic expression of the channel protein and GFP reporter.

The effects of the mutation on sodium currents were assessed in HEK-293 cells transfected with 0·5μg of pCGI-SCN5A (wild-type or mutant) construct using Lipofectamine (Invitrogen), at 48 hours post- transfection. The effects of nortriptyline on cardiac ventricular action potentials were measured in left ventricular myocytes isolated from healthy New Zealand White rabbit hearts. Measurements were performed with the patch-clamp technique.27

We conducted a case-control study using the Amsterdam Resuscitation Studies (ARREST) infrastructure. ARREST is an ongoing prospective, community-based study aimed at establishing the genetic28,29 and clinical30 determinants of SCA in the general population in a contiguous region (urban and rural communities, ~2.4 million inhabitants) of the Netherlands. Data were retrieved in the study period July 2005 – January 2008. Details of the study design are provided elsewhere.28-30 In short, the ARREST research group prospectively collects data of all cardiopulmonary resuscitation efforts in collaboration with (consisting of personnel of dispatch centers, ambulance services and all 14 area hospitals). ARREST is staffed 24 hours per day and 7 days per week. This ensures complete coverage of the study region and inclusion of >95% of all patients with out-of-hospital SCA.28-30 ARREST includes all such patients. A data collection infrastructure is used that records all out-of-hospital SCA parameters, from ambulance dispatch to discharge from the hospital or to death. Cases are included as follows. After each suspected and circumstances of SCA. Ambulance personnel are obliged by protocol to send the ECGs to the study center by modem directly after resuscitation, and to call the study center after every out-of-hospital SCA to provide extra information on the resuscitation (e.g., whether SCA was witnessed, whether basic life support was provided before arrival of ambulance personnel, whether the patient died at the resuscitation site or user of the AED (most AEDs in the study region carry a label with the request to notify the study center whether VT/VF occurred during SCA.

documentation of VT/VF. Patients with VT/VF in whom cardiopulmonary resuscitation resulted in their

146 Chapter 8 drowning, suicide) or without VT/VF (these patients typically had asystole or pulseless electrical activity) were excluded. Matched controls (by age, sex, and index date) were randomly drawn from the general population, using a database of community pharmacies (PHARMO Record Linkage System).31 This system includes the demographic details and complete medication histories from community pharmacies for >2 date as the patient to whom they had been matched.

All medication prescriptions of ARREST cases and PHARMO-controls during the year before the index Netherlands are registered with a single community pharmacy, pharmacy records are essentially complete. Prescription durations were calculated by dividing the total number of units issued per prescription by the prescribed duration or within a maximum of 10% after the prescribed duration (to deal with carry-over effects).

For cases who used nortriptyline, it was investigated whether they had additional factors that impaired cardiac excitability by retrieving data from the hospital of admission and/or the general practitioner. The following established factors that impair cardiac excitability were assessed: other (than nortriptyline) cardiac or non-cardiac drugs that block the cardiac sodium-channel32,33, exercise prior to SCA, fever34, (acute) cardiac ischemia35, and heart failure18. The presence of mutations in SCN5A was assessed in cases who used nortriptyline, of whom DNA was available.

For all cases and PHARMO-controls, the following established risk factors for SCA were assessed: cardiac ischemia, heart failure, hypertension, diabetes mellitus, and hypercholesterolemia. Risk factors were derived from medication use.36 Use of QT- prolonging drugs, antiarrhythmic drugs, cardiac glycosides, diuretics, channel blockers were also considered as covariates.

Informed consent was obtained from cases who used nortriptyline on the index date, and SCN5A mutation analysis was conducted as in the index patient.

Data are mean±standard error of the mean (SEM). Group comparisons were made with Student’s t-test or Analysis of Variance, where appropriate. The relative risk for VT/VF associated with nortriptyline use

147

conditional logistic regression analysis. Covariates and risk factors which were univariately associated with VT/VF (at a p<0.1 level) were included in the regression analyses if they changed the point estimate of the association between nortriptyline use and VT/VF by >5%.37 All analyses were performed using SPSS (version 16.0 for Mac, Chicago IL, USA).

148 CHAPTER 9

M. J. Warnier*, M.T. Blom*, A. Bardai, J. Berdowksi, P.C. Souverein, A.W. Hoes, F.H. Rutten, A. de Boer, R.W. Koster, M.L. De Bruin, H.L. Tan. *These authors contributed equally

PLoS One. 2013;8(6):e65638.

We aimed to determine whether (1) patients with obstructive pulmonary disease (OPD) have an increased risk of sudden cardiac arrest (SCA) due to ventricular

A community-based case-control study was performed, with 1310 cases of SCA of the ARREST study and 5793 age, sex and SCA-date matched non-SCA controls from the PHARMO database. Only incident SCA cases, age older than 40 years, that resulted from unequivocal cardiac causes with electrocardiographic documentation of VT/VF were included. Conditional logistic regression analysis severity, and current use of respiratory drugs.

A higher risk of SCA was observed in patients with OPD (n= 190 cases [15%], 622 controls [11%]) than in those without OPD (OR adjusted for cardiovascular (OR 3.5 [2.7-4.4]) a higher risk of SCA was observed than in those with a low (SABA) or anticholinergics (AC) at the time of SCA (SABA OR: 3.9 [1.7-8.8], AC OR: 2.7 [1.5-4.8] compared to those without OPD).

OPD is associated with an increased risk of SCA. The most increased risk was received SABA and, possibly, those who received AC at the time of SCA.

150 Chapter 9

Sudden cardiac arrest (SCA) most often causes sudden death and is the most common direct cause of death in Western1 and developing2 societies. Given the dismal survival rate of SCA,3,4 Signals have emerged that patients with obstructive pulmonary disease (OPD: asthma and chronic obstructive pulmonary disease [COPD]) do not only have a worse outcome after SCA,5 but are also at increased risk for the occurrence of SCA.6 This may be due to an increased risk of concomitant cardiovascular disease7, as OPD and cardiovascular disease share risk factors and disease pathways, e.g., smoking (in COPD) and 8,9 Accordingly, OPD is associated with a higher risk of cardiac arrhythmias and cardiovascular mortality.6 Alternatively, increased SCA risk in OPD may stem from drugs used to treat OPD (‘respiratory drugs’).10 In particular, inhaled short-acting or 11,12 witnessed natural death <1 hour of onset of acute symptoms, or unwitnessed unexpected death of someone seen in a stable medical condition <24 hours previously.13 This may that SCA was present requires electrocardiogram (ECG) documentation of ventricular associated with an increased risk of SCA with ECG-documented VT/VF. Secondly, we sought to identify subgroups of OPD patients at greatest risk, focusing on the possible

The AmsteRdam REsuscitation Study (ARREST) was conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from all participants who survived SCA. The Ethics Committee of the Academic Medical Center Amsterdam approved the use of data from patients who did not survive SCA, and approved this study.

We performed a community-based case-control study. Cases were SCA patients from and index date (date of SCA in cases) drawn from the PHARMO record linkage system (www.PHARMO.nl).

151

the community (out-of-hospital). All individuals who suffer SCA in the North Holland province of the Netherlands (>2.4 million inhabitants) are included. The ARREST study protocol is described in detail elsewhere.14 In short, a data collection infrastructure is used to record all SCA parameters, from ambulance dispatch to discharge from the were patients older than 40 years with incident SCA, with ECG-documented VT/VF. Patients were excluded when cardiac arrest was caused by trauma, drowning, intoxication, or other unequivocal non-cardiac causes. Patients in whom only asystole (but no VT/VF) was recorded were excluded, because we could not ensure that cardiac arrest stemmed from cardiac causes, as asystole is the end stage of any cardiac arrest, and may be due to non-cardiac causes (e.g., respiratory failure).15 Of each case, complete medication history of the year before SCA was retrieved by contacting the patient’s pharmacy. Data for the current study were retrieved from July 2005 to December 2008. The PHARMO database includes drug-dispensing records from community pharmacies of >3 million community-dwelling inhabitants in the Netherlands. The catchment area covers about 12% of the total population of the Netherlands, and is representative of the total population. Since nearly all patients in the Netherlands are registered at a single community pharmacy, independent of prescriber, pharmacy records are essentially complete.16 Because it is virtually impossible to disentangle the effects of OPD from those of respiratory drugs, as nearly all OPD patients use such drugs, and those more severely affected generally use more drugs (often from multiple drug classes),17,18 OPD patients (ATC) code R03 (drugs for obstructive airway diseases, either oral or inhaled), within one year prior to the index date.

As the association between OPD and SCA may be confounded by patient and other characteristics associated with both the presence of OPD and the risk of SCA, we use of antiarrhythmic drugs or non-antiarrhythmic QTc prolonging drugs. High 6 months before index date: -adrenoreceptor blockers, calcium channel antagonists, angiotensin-converting enzyme inhibitors, diuretics, angiotensin-II receptor blockers,

152 Chapter 9

N = 2 426 097

Emergency calls with SCA suspected N = 5812

No SCA: N = 1991

SCA N = 3821

SCA from non-cardiac causes: N = 531 Trauma: N = 107 Drowning: N = 35 Respiratory failure: N = 103 SCA from Other non-cardiac causes: N = 286 presumed cardiac causes N = 3290

No VT/VF: N = 1320

SCA with VT/VF N = 1970

SCA with VT/VF, aged > 40 N = 1875 Excluded: N = 565 Consent could not be retrieved: N = 44 Consent refused: N = 3 Foreigner: N = 60 Second SCA (in database): N = 4

VT/VF SCA with complete Age, gender and index date matched controls N = 1310 N = 5793

Accessed May 15, 2010].

153

use of anti-diabetics within six months before index date. Anti-arrhythmic drugs were Vaughan-Williams class I or III antiarrhythmic drugs (supplementary Table s1)19. Non- antiarrhythmic QT prolonging drugs were class 1 or 2 QTc-prolonging drugs according to the Arizona Center for Education & Research on Therapeutics (supplementary Table date and the end of the prescription period (extended with 10% after the prescribed and 65 years. The number of different respiratory drugs used in the six-month period before index date was used as a proxy for OPD severity.17,18 OPD patients were grouped according to the current use of respiratory drugs: SABA, LABA, AC, inhaled corticosteroids (ICS), alone or in combinations (mutually exclusive categories). In the Netherlands, OPD is treated almost exclusively with inhaled respiratory medications. Therefore oral medications, such as systemic 2-adrenoreceptor agonists, xanthines, or chronic systemic corticosteroid use, were not included in the analyses. In the Netherlands, medications used to treat OPD are not available over-the-counter.

Differences in baseline characteristics were examined with chi-square tests or t-tests. Conditional logistic regression analysis was used to examine the risk for SCA in relation to OPD, with adjustment for three sets of confounders: 1) all potential confounders, 2) all covariates that were univariately associated with SCA (p<0.05), 3) all covariates the association between OPD and SCA by 5%. As logistic regression analyses were performed, odds ratios were calculated, which can be interpreted as a risk ratio in case of including the cross-product of the two factors as a variable in the model. The presence was estimated by determining the synergy index.20 Subgroup analyses were performed according to disease severity of OPD, and (combinations of) types of current use of respiratory drugs. All data were analysed using the statistical software package SPSS (SPSS for Windows, version 18.0, SPSS Inc.).

During the study period, 3821 instances of cardiac arrest were recorded, of which 1875 cases had ECG-documented VT/VF and were aged > 40 years. We excluded 565 patients

154 Chapter 9

67.1 (12.2) 67.0 (12.2) n/a Age group in years < 65 546 (42%) 2421 (42%) 764 (58%) 3372 (58%) n/a 1015 (78%) 4502 (78%) n/a 4 929 (71%) 3049 (53%) <0.001 Diabetes mellitus5 232 (18%) 621 (11%) <0.001 1 6 60 (4%) 182 (3%) 0.009 463 (35%) 1134 (20%) <0.001 21 (2%) 64 (1%) 0.134 35 (3%) 151 (3%) 0.891 190 (15%) 622 (11%) <0.001 70 (37%) 120 (19%) <0.001 1 62 (5%) 51 (0.9%) <0.001 78 (6%) 127 (2%) <0.001 78 (6%) 102 (2%) <0.001 97 (7%) 205 (4%) <0.001 2 3 (0.2%) 4 (0.1%) 0.096 2 10 (0.8%) 31 (0.5%) 0.325 3 18 (1.4%) 93 (1.6%) 0.542 1 2 3 4 5 6 (supplementary table).

155

OR1 OR2 OR (95%CI) (95%CI) (95%CI) (95%CI) 1.4 1.4 1.4 1.4 (1.2-1.7) (1.1-1.6) (1.1-1.6) (1.2-1.6) 4 2.5 2.3 2.3 2.5 (2.2-2.9) (2.0-2.7) (2.0-2.7) (2.2-2.9) Diabetes mellitus5 1.8 1.5 1.5 (1.5-2.1) (1.2-1.7) (1.2-1.7) 6 1.5 1.2 1.2 (1.1-2.0) (0.9-1.6) (0.9-1.6) 1.4 1.2 drugs class 16 (0.8-2.3) (0.7-2.0) 1.0 1.0 drugs class 26 (0.7-1.5) (0.7-1.4) 1 2 3 4 5 6 (Supplementary table s1)

of cases and controls are presented in table 1. The mean age was 67.1 (SD 12.2, range: 41-99) years, and 78% were male. OPD was more prevalent in cases (15%) than controls (11%, p<0.001). OPD was independently associated with an increased risk of SCA (crude OR 1.4 [1.2-1.7]). The three different models used to adjust for confounding resulted in similar ORs (table 2).

adj

1.8 [1.3-2.6]) than in men (ORadj 1.3 [1.03-1.6], table 3). The increase in SCA risk associated with OPD was slightly stronger in OPD patients younger than 65 (ORadj

1.6 [1.2-2.3] than in OPD patients of 65 years or older (ORadj 1.3 [1.03-1.6], table 3).

156 Chapter 9

2 58/546 (11%) 163/2421 (7%) 1.6 (1.2-2.3) 1.6 (1.2-2.3) 132/764 (17%) 459/3372 (14%) 1.3 (1.1-1.6) 1.3 (1.03-1.6) 51/295 (17%) 124/1291 (10%) 2.0 (1.3-2.8) 1.8 (1.3-2.6) 139/1015 (14%) 498/4502 (11%) 1.3 (1.04-1.6) 1.3 (1.03-1.6) Low 342 (26%) 2505 (43%) n/a risk 778 (59%) 2666 (46%) 2.5 (2.1-2.9) n/a risk Low 39 (3%) 239 (4%) 1.3 (0.9-1.9) n/a risk 151 (12%) 383 (7%) 3.5 (2.7-4.4)3 n/a risk 1 2 3

observed on a multiplicative scale, nor on an additive scale. The SCA risk increased in parallel with OPD severity: compared to patients without OPD, the SCA risk was more elevated in patients with very severe OPD (ORadj 1.8 [1.2-2.7]) than in patients with moderate OPD (ORadj 1.4 [1.1-1.7], table 4). Analysis of current use of respiratory drug revealed that increased SCA risk was associated with the use of SABA only (ORadj 3.9 [1.7-8.8]) or AC only (ORadj 2.7 [1.5-4.8]), but not ICS only, while use of LABA only was too rare to draw any

157

1120 (86%) 5171 (89%)

Mild (0 drugs) 12 (1%) 61 (1%) 0.9 (0.5-1.7) 0.9 (0.5-1.7)

Moderate (1-2 drugs) 99 (8%) 334 (6%) 1.4 (1.1-1.7) 1.4 (1.1-1.7)

Severe (3 drugs) 45 (3%) 147 (3%) 1.4 (1.01-2.0) 1.3 (0.9-1.9)

34 (3%) 80 (1%) 1.9 (1.3-2.9) 1.8 (1.2-2.7)

1120 (86%) 5171 (89%)

No SABA, AC, LABA or ICS 52 (4%) 358 (6%) 0.7 (0.5-0.9) 0.7 (0.5-0.9)

SABA only 12 (0.9%) 13 (0.2%) 4.1 (1.9-9.0) 3.9 (1.7-8.8)

LABA only 2 (0.2%) 5 (0.1%) 1.7 (0.3-9.0) 1.8 (0.3-9.2)

AC only 19 (2%) 30 (0.5%) 2.8 (1.6-5.0) 2.7 (1.5-4.8)

ICS only 11 (0.8%) 78 (1.3%) 0.6 (0.3-1.2) 0.7 (0.4-1.3)

SABA + AC 8 (0.6%) 10 (0.2%) 3.5 (1.4-8.9) 2.6 (1.02-6.7)

ICS + LABA 26 (2%) 57 (1.0%) 2.0 (1.3-3.3) 2.0 (1.2-3.2)

SABA + LABA and/or ICS 14 (1%) 11 (0.2%) 5.5 (2.5-12.1) 5.3 (2.4-12.0)

AC + LABA and/or ICS 23 (2%) 46 (0.8%) 2.3 (1.4-3.8) 2.0 (1.2-3.4)

SABA + AC + LABA and/or ICS 23 (2%) 14 (0.2%) 7.9 (3.9-16.0) 7.6 (3.7-15.6)

1 2

158 Chapter 9 conclusions (table 4). Use of SABA or AC in combination with other respiratory drugs was also associated with increased SCA risk. Patients who used both SABA and AC, in combination with LABA and/or ICS, had the highest SCA risk (ORadj 7.6 [3.7-15.6], table 4). SCA risk associated with SABA or AC use (alone or in combination with other respiratory drugs) was particularly elevated in the presence of a high cardiovascular risk-

adj 6.0 [3.8-9.5], AC: 61 cases

[5%], 80 controls [1%], ORadj 3.5 [2.4-4.9]).

as possible for confounding. These analyses consistently demonstrated a statistically measurements. The increase in SCA risk is most pronounced in OPD patients with a increased SCA risk was observed compared to patients without OPD.

The observed increased risk of SCA in OPD patients may be, in part, due to the higher prevalence of concomitant cardiovascular disease and cardiac arrhythmias, as these are current use of anti-arrhythmic drugs (as a proxy for pre-existing cardiac arrhythmias), and QT prolonging drugs (which may evoke cardiac arrhythmias) did not alter the OR for SCA risk. Still, the strongest association with SCA was observed in OPD patients with a factors for OPD and cardiovascular disease may also play a role, but we were unable to assess them. Smoking, a common cause of COPD, is clearly also associated with ischemic heart disease.21 mechanism in OPD,8,9 has emerged from epidemiologic studies as a causative factor for atherosclerosis and ischemic heart disease.22 To further support the role of OPD in SCA risk, we found that more severe OPD was more strongly associated with SCA risk than mild OPD. Possibly, chronic hypoxemia, more likely to occur in those with more severe OPD, may contribute to the development of cardiac arrhythmias by increasing resting heart rates, as increased heart rate is associated with increased mortality in both the general population as well as in patients with COPD.23-25 These cardiac arrhythmias may cause episodes of

159

cardiac ischemia, which is related to an increased SCA risk.26 Paradoxically, we found that use of -adrenoceptor blockers among OPD patients was more common in cases than controls. One would probably expect the reverse, as -adrenoceptor blockers are the only drugs proven to prevent sudden death (possibly, in part, through heart rate slowing).27-30 Moreover, these drugs have long been considered contra-indicated in COPD patients, particularly in those with severe COPD and larger SCA risk. Yet recent evidence indicates that cardio-selective -adrenoceptor blockers are well tolerated by COPD patients, in daily practice the potential disadvantages of making COPD worse by prescribing -adrenoceptor blockers and making and cardiac disease worse by refraining from prescribing these drug should be weighed to optimally decide on the use of -adrenoceptor blockers in patients with OPD.31 association between OPD and SCA in women than in men. This contrasts with the general population, where SCA risk of women is only a third of that in men.32 It may be speculated that this difference is, in part, mediated by concomitant, yet unrecognized and thus untreated, cardiovascular disease in women. The clinical presentation of ischemic heart disease in women differs from that in men. Symptoms of myocardial infarction are often labelled as “atypical” in women, as women are less likely to report the key symptom, chest pain or discomfort, than men.33

We observed that, in the studied population, patients who received SABAs at the time of SCA, especially when combined with other respiratory drugs, had a higher SCA risk than patients who received other respiratory drugs. For LABAs, this association was less clear. The association between SABA use and SCA may be explained as follows. 2- adrenoreceptor agonists act on the 2-adrenergic receptors of bronchial smooth muscle, leading to dilatation of the bronchi, which results in relief of symptomatic wheeze and dyspnoea, and improvement of lung function.34,35 However, 2-adrenergic receptors are also present in the heart. Here, their stimulation results in increased myocardial contractility and heart rate.34 An elevated heart rate is associated with an increased risk of cardiac mortality in the general population36, and in COPD patients. Moreover, 2-adrenoreceptor agonists lower serum potassium levels due to intracellular uptake of arrhythmias.34,37 Finally, inhaled 2-adrenoreceptor agonists prolong the ECG QT interval, a risk factor for mortality,38 and this effect is dose-dependent.35 Consequently, soon after their availability in the 1960s, concerns have emerged about potentially serious adverse effects of 2-adrenoreceptor agonists on the heart, meta-analysis of Salpeter et al. showed that use of 2-adrenoreceptor agonists in OPD patients increases the risk of cardiovascular events.11 In contrast, a review of Wood-Baker et al. concluded that there is no evidence of increased mortality associated with the use of

160 Chapter 9

SABAs in COPD patients,39 and Suissa et al. suggest that the use of 2-adrenoreceptor agonists only increases the risk of cardiac mortality in certain administration forms.40 An alternative explanation for the association of 2-adrenoreceptor agonist use with cardiovascular mortality is that the intensity of use, and combined use with other observation that, while elevated SCA risk is, in general, associated with SABA use, this In accordance with previous studies which showed that AC use was associated with an increased risk of cardiovascular death,12 we found that OPD patients who received AC showed an overall increased risk of SCA, although to a lesser extent than patients receiving SABA. In contrast, the UPLIFT-trial did not show an increased risk of cardiovascular mortality in COPD patients using AC (tiotropium).41 Based upon this trial, the Food and Drug Administration in 2010 concluded that current studies did not support the notion that there is an increased risk of death associated with tiotropium.42 In accordance with this conclusion, we cannot rule out that our observed than a pro-arrhythmic effect of AC per se, as AC are mainly used by COPD patients (not by patients with asthma), especially those with advanced disease. In any case, SCA risk associated with AC use was largest in OPD patients with a high cardiovascular risk-

determinants of SCA. This ensured that SCA diagnosis was accurate. SCA was validated by the presence of VT/VF on the ECG. This is especially important in OPD patients, because sudden death caused by cardiac arrest may easily be confused with sudden death caused by respiratory failure.15 are representative for the community at large, because we studied the general population, including both urban and rural areas, and captured ~90% of all SCA cases.14 We only excluded eligible cases and controls because of incompleteness of data. If such incompleteness is associated with the determinant of interest (i.e. OPD) this may lead to (selection) bias. Since the main reasons of incompleteness (e.g. consent could not be retrieved or was refused, patient’s pharmacy could not be retrieved or refused participation) were highly unlikely to be related to the presence of OPD, such bias does not play an important role in our study. A limitation is that, as this is an observational study, we were unable to completely distinguish between the effects of OPD per se and those of the respiratory drugs used to treat OPD. We addressed this problem by performing subgroup analyses

161

by the use of two prescriptions of respiratory drugs within one year before index date. exclusively for OPD, and patients with OPD who received less than two prescriptions of any respiratory drug in the past year most likely are patients with very mild disease. the risk of SCA and thus most likely results in an underestimation of the true association of medication, it was impossible to distinguish between asthma and COPD. However, be asthma patients, and the 65-years-or-older group most likely to be COPD patients. ascertain whether the patients who received a prescription for inhalation medication actually used these drugs on the index date. This may especially be true for SABA, as these are generally intermittently used as symptomatic treatment. Finally, SCA is the of SCA.43 Therefore, many important clinical measurements, such as information on smoking and alcohol use, or history of syncope or arrhythmias have never been made before the SCA episode. Smoking is an important risk factor for both COPD and cardiovascular disease, and we assume that smoking rates were higher among OPD patients than controls.

We observed that the overall risk of SCA is 40% higher in patients with OPD than in patients without OPD, and we were able to identify subgroups of OPD patients in therefore would recommend a prospective trial to evaluate the effectiveness of more integrated pulmonary and cardiovascular care (e.g., investigations to detect previously unrecognized cardiovascular disease) in these high-risk patients.

The authors greatly appreciate the contributions of Paulien Homma, Michiel Hulleman, Esther Landman and Renate van der Meer to the data collection, data entry, and patient follow-up. We are greatly indebted to all participating EMS dispatch centers and ambulance services for their cooperation and support. We thank all the students at the University of Amsterdam who helped collect the data of the onsite AEDs and patient’s pharmacies.

162 Chapter 9

Vici 918.86.616), the Dutch Medicines Evaluation Board (MEB/CBG), the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement nr. 241679 - the ARITMO 007).

163

1. Myerburg RJ, Castellanos A. Cardiac arrest and sudden cardiac death. In: Libby P, Bonow RO, Mann DL, Zipes DP, editors. Braunwald’s Heart Disease: A textbook of cardiovascular medicine. Oxford, 2. Hua W, Zhang LF, Wu YF, et al. (2009) Incidence of sudden cardiac death in china: Analysis of 4 regional populations. J Am Coll Cardiol 54: 1110-1118. 3. Berdowski J, Berg RA, Tijssen JG, Koster RW. (2010) Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies. Resuscitation 81: 1479-1487. 4. Nichol G, Thomas E, Callaway CW, et al. (2008) Regional variation in out-of-hospital cardiac arrest incidence and outcome. JAMA 300: 1423-1431. 5. Blom MT, Warnier MJ, Bardai A, et al. (2012) Reduced in-hospital survival rates of out-of-hospital cardiac arrest victims with obstructive pulmonary disease. Resuscitation 6. Sidney S, Sorel M, Quesenberry CP,Jr, et al. (2005) COPD and incident cardiovascular disease hospitalizations and mortality: Kaiser permanente medical care program. Chest 128: 2068-2075. 7. Han MK, McLaughlin VV, Criner GJ, Martinez FJ. (2007) Pulmonary diseases and the heart. Circulation 116: 2992-3005. 10.1161/CIRCULATIONAHA.106.685206. asthma: Relation with comorbidities. Proc Am Thorac Soc 6: 648-651. Chest 142: 86-93. and respiratory morbidity might be mediated by short-acting beta2-agonists. J Allergy Clin Immunol 123: 124-130.e1. 11. Salpeter SR, Ormiston TM, Salpeter EE. (2004) Cardiovascular effects of beta-agonists in patients with asthma and COPD: A meta-analysis. Chest 125: 2309-2321. 12. Singh S, Loke YK, Furberg CD. (2008) Inhaled anticholinergics and risk of major adverse cardiovascular events in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis. JAMA 300: 1439-1450. 13. Priori SG, Aliot E, Blomstrom-Lundqvist C, et al. (2001) Task force on sudden cardiac death of the european society of cardiology. Eur Heart J 22: 1374-1450. 14. Bardai A, Berdowski J, van der Werf C, et al. (2011) Incidence, causes, and outcomes of out-of-hospital cardiac arrest in children. A comprehensive, prospective, population-based study in the netherlands. J Am Coll Cardiol 57: 1822-1828. chance of survival when found in asystole out of hospital? Am J Cardiol 86: 610-614. 16. Buurma H, Bouvy ML, De Smet,P A G M., et al. (2008) Prevalence and determinants of pharmacy shopping behaviour. J Clin Pharm Ther 33: 17-23. 17. Blais L, Suissa S, Boivin JF, Ernst P. (1998) First treatment with inhaled corticosteroids and the prevention of admissions to hospital for asthma. Thorax 53: 1025-1029. 18. Velthove KJ, Souverein PC, van Solinge WW, Leufkens HG, Lammers JW. (2010) Measuring exacerbations in obstructive lung disease. Pharmacoepidemiol Drug Saf 19: 367-374. Pharmacol Ther B 1: 115-138. 20. Knol MJ, van der Tweel I, Grobbee DE, Numans ME, Geerlings MI. (2007) Estimating interaction on an additive scale between continuous determinants in a logistic regression model. Int J Epidemiol 36: 1111-1118.

164 Chapter 9

21. Reid DD, Hamilton PJ, McCartney P, et al. (1976) Smoking and other risk factors for coronary heart- disease in british civil servants. Lancet 2: 979-984. 22. Sin DD, Wu L, Man SF. (2005) The relationship between reduced lung function and cardiovascular mortality: A population-based study and a systematic review of the literature. Chest 127: 1952-1959. 23. Fox K, Borer JS, Camm AJ, et al. (2007) Resting heart rate in cardiovascular disease. J Am Coll Cardiol 50: 823-830. 24. Jensen MT, Marott JL, Lange P, et al. (2012) Resting heart rate is a predictor of mortality in chronic obstructive pulmonary disease. Eur Respir J ‘in press’. 25. Verrier RL, Tan A. (2009) Heart rate, autonomic markers, and cardiac mortality. Heart Rhythm 6: S68-75. ventricular arrhythmia is related to pulmonary function: A study from “men born in 1914,” Malmo, Sweden. Circulation 103: 3086-3091. 27. Matsue Y, Suzuki M, Nagahori W, et al. (2011) Beta-blocker prevents sudden cardiac death in patients with hemodialysis. Int J Cardiol . ‘in press’. 28. Kendall MJ, Lynch KP, Hjalmarson A, Kjekshus J. (1995) Beta-blockers and sudden cardiac death. Ann Intern Med 123: 358-367. 29. Salpeter SR. (2004) Cardiovascular safety of beta(2)-adrenoceptor agonist use in patients with obstructive airway disease: A systematic review. Drugs Aging 21: 405-414. 30. Teerlink JR, Massie BM. (2000) The role of beta-blockers in preventing sudden death in heart failure. J Card Fail 6: 25-33. 31. Salpeter S, Ormiston T, Salpeter E. (2005) Cardioselective beta-blockers for chronic obstructive pulmonary disease. Cochrane Database Syst Rev (4): CD003566. 32. Engdahl J, Holmberg M, Karlson BW, Luepker R, Herlitz J. (2002) The epidemiology of out-of- hospital ‘sudden’ cardiac arrest. Resuscitation 52: 235-245. 33. Canto JG, Goldberg RJ, Hand MM, et al. (2007) Symptom presentation of women with acute coronary syndromes: Myth vs reality. Arch Intern Med 167: 2405-2413. 34. Lipworth BJ, McDevitt DG. (1992) Inhaled beta 2-adrenoceptor agonists in asthma: Help or hindrance? Br J Clin Pharmacol 33: 129-138. and hypokalaemic effects of fenoterol, salbutamol, and terbutaline in asthma. Lancet 336: 1396-1399. 36. Kannel WB, Kannel C, Paffenbarger RS,Jr, Cupples LA. (1987) Heart rate and cardiovascular mortality: The framingham study. Am Heart J 113: 1489-1494. 37. Wong CC, Tong EK, Tsoh JY, Chen M, Hom FB. (2009) Patterns of tobacco-use behavior among chinese smokers with medical conditions. J Community Health 34: 472-479. 38. de Bruyne MC, Hoes AW, Kors JA, et al. (1999) Prolonged QT interval predicts cardiac and all-cause mortality in the elderly. the rotterdam study. Eur Heart J 20: 278-284. 39. Wood-Baker R, Cochrane B, Naughton MT. (2010) Cardiovascular mortality and morbidity in COPD: The impact of bronchodilator treatment. Intern Med J 40: 94-101. 40. Suissa S, Hemmelgarn B, Blais L, Ernst P. (1996) Bronchodilators and acute cardiac death. Am J Respir Crit Care Med 154: 1598-1602. 41. Celli B, Decramer M, Kesten S, et al. (2009) Mortality in the 4-year trial of tiotropium (UPLIFT) in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 180: 948-955. 42. Michele TM, Pinheiro S, Iyasu S. (2010) The safety of tiotropium-the FDA’s conclusions. N Engl J Med 363: 1097-1099.

165

43. Myerburg RJ. (2001) Sudden cardiac death: Exploring the limits of our knowledge. J Cardiovasc Electrophysiol 12: 369-381.

166 Chapter 9

Williams1 2011].

1 2 Quinidine Astemizole Amantadine Procainamide Bepridil Atazanavir Disopyramide Sparteine Ajmaline Cisapride Clozapine Prajmaline Citalopram Dolasetron Lorajmine Escitalopram Domperidone Lidocaine Droperidol Tocainide Aprindine Haloperidol Mesoridazine Granisetron Indapamide Lorcainide Isradipine Encainide Pentamidine Pimozide Amiodarone Probucol Bretylium tosilate Nicardipine Tedisamil Dronedarone Sotalol Moricizine Paliperidone

167

1 2 Cibenzoline Ranolazine Risperidone Tacrolimus Tizanidine 1 2

Ther B. 1: 115-138.

168 PART III

CHAPTER 10

M.T. Blom*, M.J. Warnier*, A. Bardai, J. Berdowski, R.W. Koster, P.C. Souverein, A.W. Hoes, F.H. Rutten, A. de Boer, M.L. De Bruin, H.L. Tan. *These authors contributed equally

Resuscitation. 2013;84(5):569-74.

Out-of-hospital cardiac arrest (OHCA) due to sustained ventricular tachycardia/ determine survival after OHCA, and be instrumental in post-resuscitation care, but are poorly studied. We aimed to study whether patients with obstructive pulmonary disease (OPD) have a lower survival rate after OHCA than non-OPD patients.

We performed a community-based cohort study of 1172 patients with non- traumatic OHCA with ECG-documented VT/VF between 2005 and 2008. We compared survival to Emergency Room (ER), to hospital admission, to hospital discharge, and at 30 days after OHCA, of OPD-patients and non-OPD patients, using logistic regression analysis. We also compared 30-day survival of patients who were admitted to hospital, using multivariate logistic regression analysis.

OPD patients (n=178) and non-OPD patients (n=994) had comparable survival to ER (75% vs. 78%, OR 0.9 [95% CI: 0.6-1.3]) and to hospital admission (56% vs. lower among OPD patients (21% vs. 33%, OR 0.6 [0.4-0.9]). Multivariate regression analysis among patients that were admitted to hospital (OPD: n=100, no OPD: n=561) revealed that OPD was an independent determinant of reduced 30-days survival rate (39% vs. 59%, adjusted OR 0.6 [0.4-1.0, p=0.035]).

OPD-patients had lower survival rates after OHCA than non-OPD patients. Survival to ER and to hospital admission was not different between both groups. However, among OHCA victims who survived to hospital admission, OPD was an independent determinant of reduced 30-day survival rate.

172 Chapter 10

1,2 Despite much effort, survival after OHCA remains poor, even when cardiopulmonary resuscitation (CPR) by emergency medical services (EMS) personnel is attempted. Survival rate to hospital discharge is generally low and varies greatly, ranging from 3 to 40%.3,4 This variability is largely attributable to differences in the chain of survival: 5-7 However, these factors do not entirely explain the variability in survival after OHCA. Rea et al. showed that these links in the pre-hospital chain of care (termed the Utstein measures) collectively predicted 72% of survival variability among all OHCAs, and 40% among bystander- witnessed OHCA with VF.6 This indicates that patient characteristics may also play an important role. Clearly, recognizing the role of these characteristics can have important implications for therapy strategies for OHCA. Yet, reports on the effects of patient characteristics are scarce (on comorbidities8,9) and contradicting (on age10,11 and sex12,13). chronic obstructive pulmonary disease [COPD]) may affect survival rate from OHCA. Adverse effects of ventilation and endotracheal intubation during the resuscitation efforts, and increased hypoxemia in OPD patients may negatively impact the patient’s chance on survival. Also, concomitant (yet often unrecognized) cardiac disease in OPD patients may play a role.14 Yet, systematic studies on the relation between OPD and survival rates from OHCA are lacking. The primary aim of our study was to assess whether OPD patients have a lower survival rate after OHCA than non-OPD patients. We studied in detail at which point in the course of post-resuscitation care survival rates between both groups diverge by comparing survival to emergency room (ER), survival to hospital admission, survival to hospital discharge, and 30-day survival. Secondly, we aimed to compare the duration of hospital care and the quality of outcome (neurologic outcome) between OPD patients and non-OPD patients who were discharged from hospital alive.

The AmsteRdam REsuscitation STudy (ARREST) research group prospectively collects data of all OHCA since June 2005 in the North Holland province of the Netherlands. This region covers 2404 km2 (urban and rural communities) and had a population of 2 426 097 in 2007.15 In case of a medical emergency, people dial the national emergency number. Calls are transferred to the regional EMS dispatch centre. When suspecting a

173

cardiac arrest, the EMS dispatcher sends out 2 ambulances from a single tier.16 Further details of the EMS system were described elsewhere.17

Data of all resuscitations during the study period, from arrival of EMS personnel until hospital discharge or death, were collected according to Utstein recommendations.18 To determine the survival of OHCA victims with or without OPD, a prospective cohort study was performed. This study was conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from all participants who survived OHCA. The Ethics Committee of the Academic Medical Center Amsterdam approved the study, including the use of data from patients who did not survive OHCA.

Of each patient in whom a resuscitation attempt was undertaken by EMS personnel, the ECG from the ambulance or AED was retrieved and analysed. Patients were included for the present study if they had OHCA with ECG-documented VT/VF from presumed cardiac causes. All OHCAs were considered to be from cardiac causes unless an unequivocal non-cardiac cause was documented (i.e., drowning or trauma). This emergency call – response intervals and immediate resuscitation by EMS personnel have enormous impact on survival chances6, and excluded aborted resuscitation efforts in individuals with a “do not resuscitate” status. As we aimed to perform a complete case analysis, we excluded patients of whom the medication history of the year before OHCA could not be retrieved, and those of whom data on the chain of resuscitation care were missing.

absence of signs of circulation,18 occurring out-of-hospital. Patients were considered to have OPD if they had at least two prescriptions of any medication with Anatomical airway diseases) in the year before OHCA. Data of medication use at the time of OHCA, and in the year before OHCA, were obtained from the patient’s community pharmacy. Survival was assessed at different time points: survival to emergency room (ER), survival to hospital admission, survival to hospital discharge (information retrieved from hospital records), and 30-day survival (retrieved from the civic registry). Duration of hospital care (in days) was retrieved from hospital records. Two researchers (JB and AB) reviewing hospital charts of patients who survived until hospital discharge. Category 1

174 Chapter 10

175

19 18 The following prognostic factors were considered to be potential confounding inhibitors, diuretics, angiotensin-II receptor blockers, platelet aggregation inhibitors, nitrates and/or statins within 6 months before OHCA), bystander witnessed OHCA, public location of OHCA, bystander CPR performed, use of AED, and time interval from emergency call to arrival of EMS personnel.

all OHCA patients with documented VT/VF (n=1172, Figure 1). As primary outcome measure in this analysis, we used 30-day survival, as this can be determined for all patients regardless of hospitalization status. Secondary outcome measures were survival rates at succeeding stages in the chain of care: i) survival to arrival at the ER, ii) survival to hospital admission and iii) survival to hospital discharge. We performed logistic regression analysis for survival at all stages, adjusting for age and sex. Next, we determined at which stage of the chain of care survival diverged between OPD patients and non-OPD patients. We then selected all patients who survived up to that stage, and performed multivariate logistic regression analysis, using 30-day survival as outcome measure. We applied two multivariate models: 1) with adjustment for all covariates that were univariately associated with OHCA with VT/ VF, and 2) with adjustment for all covariates that were univariately associated with OHCA with VT/VF and changed the point estimate of the association between OPD and outcome with at least 5%.20 Interaction between OPD and either older age, sex or concomitant cardiovascular disease was estimated by including the cross product of the two factors as a variable in the model. Results are presented as odds ratios (OR) and To compare duration of hospital care and quality of outcome between OPD patients and non-OPD patients who were discharged from the hospital alive, we studied duration of hospital care (in days) and neurologic status at hospital discharge of the patients who were discharged from the hospital alive. Continuous variables were described as means and standard deviations (SD), or medians and interquartile range where appropriate, and categorical variables as absolute numbers and percentages. Comparisons between groups were performed with chi- square test or analysis of variance where appropriate. All data were analysed using the statistical software package of SPSS (SPSS for Mac, version 18.0, SPSS Inc.).

176 Chapter 10 0.006 0.008 0.008 0.043 0.053 0.099 0.035 0.068 0.075 561 64 (14) 295 (53%) 439 (78%) 387 (69%) 249 (44%) 512 (91%) 436 (78%) 168 (30%) 9.0 (6.8-11.5) 100 OPD 69 (12) 67 (67%) 66 (66%) 79 (79%) 34 (34%) 86 (86%) 68 (68%) 21 (21%) 9.9 (7.4-13.1) 0.024 0.001 0.007 0.826 0.142 0.015 0.021 <0.001 <0.001 994 65 (15) 545 (55%) 785 (79%) 668 (67%) 385 (39%) 855 (86%) 734 (74%) 258 (26%) 9.6 (7.5-11.9) 178 OPD 70 (12) 50 (28%) 31 (17%) 123 (69%) 127 (71%) 142 (80%) 152 (85%) 122 (69%) 10.4 (8.0-13.1) 1 n Events, SD) (years, Mean age disease, n (%) Cardiovascular n (%) - Witnessed collapse, - AED used, n (%)

177

During the 43-month study period, there were 5812 emergency calls with EMS dispatchers suspecting OHCA. In 3821 instances, EMS personnel attempted to resuscitate. There were 3290 patients with OHCA from presumed cardiac causes, including 1970 with documented VT/VF. After exclusion of non-eligible patients (in 605 patient data unavailable, in 139 EMS-witnessed OHCA, in 54 data on circumstances of OHCA unavailable), the analysis cohort consisted of 1172 patients (Figure 1). Age and sex of included and excluded patients were not meaningfully different: age 63.5 (14.5) vs. 65.8 Baseline characteristics of OHCA patients with OPD (N=178) and without OPD (N=994) are shown in Table 1. OPD patients were older (70 [12] vs. 65 [15] years, p<0.001), less often male (71 vs. 79%, p=0.02), and more often used (any type of) cardiovascular medication (80 vs. 67%, p=0.001). In OPD patients, OHCA occurred less often at a public location (28 vs. 39%, p=0.007), AED use was less common (17 vs. 26%, p=0.02), and EMS response time was longer (10.4 vs. 9.6 minutes, p=0.02). Survival rates of OPD and non-OPD patients are shown in Figure 2 and Table 2. Thirty- day survival was lower in OPD patients than in non-OPD patients (23 vs. 34%, OR 0.7 [0.5-0.97]). However, survival to ER was comparable (75 and 78%, respectively, OR 0.9 [0.6-1.3]), as was survival to hospital admission (56 and 57%, OR 1.0 [0.7-1.4]). In contrast, survival to hospital discharge was lower in OPD patients (21 vs. 33%, OR 0.6 [0.4-0.9]).

OPD OPD 30-day survival, n (%) 40 (23) 333 (34) 0.7 (0.5-0.97) Survival to ER, n (%) 134 (75) 779 (78) 0.9 (0.6-1.3) 100 (56) 561 (57) 1.0 (0.7-1.4) 38 (21) 329 (33) 0.6 (0.4-0.9)

Since survival rates of OPD patients became lower than those of non-OPD patients only after admission to the hospital, we studied the cohort of patients who were admitted to the hospital alive (n=661, Figure 1, Table 1) to establish whether OPD was an independent determinant of lower survival rate. Within this cohort, OPD patients

178 Chapter 10 were older than non-OPD patients (69 (12) vs. 65 (14) years, p=0.006), less often male (66 vs. 78%, p=0.008), and more often received (any) cardiovascular medication (79 vs. 69%, p=0.043). While all resuscitation parameters were less favourable for OPD shows ORs for 30-day survival in this cohort, calculated with univariate and multivariate regression analysis (two models). OPD patients had a lower chance of survival (39%

[second model] concomitant cardiovascular disease and OPD was observed. Among patients who were discharged from hospital alive, duration of hospital care was not different between OPD patients and non-OPD patients (26 vs. 27 days, p= 0.825, Table 4). Also, CPC scores were similar (table 4), as was the proportion of neurologically intact survival (95% and 94%, p=0.787).

Patients with OPD had a 40% lower chance on 30-day survival after OHCA than patients without OPD. Survival rates were similar for OPD patients and non-OPD

179

3 2 4 (95%CI) 0.8 (0.5-1.2) 1.7 (1.2-2.4) 1.5 (1.0-2.2) 1.6 (1.1-2.4) 0.6 (0.4-0.95) 0.94 (0.90-0.98) 0.96 (0.95-0.97) 1 (95%CI) 1.7 (1.1-2.5) 0.8 (0.5-1.2) 1.7 (1.2-2.4) 3.1 (1.7-5.8) 1.2 (0.8-1.9) 1.7 (1.1-2.5) 0.6 (0.4-0.99) 0.96 (0.94-0.97) 0.95 (0.91-0.99) (95%CI) 0.4 (0.3-0.7) 1.9 (1.3-2.7) 0.5 (0.3-0.7) 2.3 (1.7-3.1) 3.6 (2.0-6.4) 2.1 (1.5-3.0) 2.2 (1.5-3.1) 0.95 (0.94-0.97) 0.93 (0.90-0.97) 13 ± 70 61 (21) 92 (32) 58 (20) 203 (70) 230 (79) 245 (85) 199 (69) No (N=290) 10.0 (8.0-12.7) 14 ± 62 39 (11) 302 (81) 236 (64) 191 (52) 353 (95) 305 (82) 131 (35) Yes (N=371) Yes 8.4 (6.0-10.7) , n (%) 5 disease Cardiovascular AED used, n (%) (Q1-Q3) 1 2 3 4 5

180 Chapter 10

at early stages is mostly determined by pre-hospital chain of care factors, while patient characteristics play a larger role in late (eventual) survival. et al.8 reported that the chance of survival after OHCA declines as the number of co-morbidities (including lung disease) increases. However, co-morbidities in the study of Carew were not so well ascertained as in our study because they were collected solely from EMS reports. Moreover, their analysis included ambulance witnessed arrests, which arguably could be considered as in-hospital cardiac arrests when analysing survival. Most importantly, we discovered that reduction in survival rate of OPD patients (relative to non-OPD patients) occurs be feasible to modify treatment strategies in such a way that this mortality gap can be closed (treatment strategies for pre-hospital or in-community care would be much mechanisms that underlie the lower survival rates in OPD patients. While we did not study these mechanisms, various explanations may be proposed. Firstly, OPD patients have a lower potential for oxygen uptake, and may therefore have lower ‘oxygen reserve’, and be more vulnerable to the deleterious effects of hypoxemia during cardiac arrest and resuscitation. Also, endotracheal intubation and ventilation during the resuscitation issues. Still, improved pre-hospital or in-community treatments to reduce risk of OHCA or survival from OHCA in OPD patients must also be considered. For instance, at present, -adrenoceptor blockers are the only drugs that have shown to prevent

p-value (N= 38) (N=329) 0.507 31 (82) 265 (81) Moderate cerebral disability (CPC=2), n (%) 5 (13) 43 (13) Severe cerebral disability (CPC=3), n (%) 1 (3) 19 (6) 1 (3) 2 (1) 26 (17) 27 (19) 0.825

181

sudden cardiac death in some patient categories, notably cardiomyopathy, heart failure, coronary artery disease, and hemodialysis.21-23 Traditionally, -adrenoceptor blockers have been considered contra-indicated in COPD patients, although evidence indicates that at least cardio-selective -adrenoceptor blockers are well tolerated by COPD patients.24 Interestingly, recent observational studies suggest that long-term treatment with -adrenoceptor blockers may improve survival of COPD patients, including those without known cardiovascular disease.25,26 As COPD patients have a worse prognosis after OHCA, future research must establish whether or not -adrenoceptor blockers should be given to COPD patients with an indication for these drugs.

determinants and outcomes of OHCA. This ensured that OHCA diagnosis was accurate. A cardiac cause of OHCA was validated by the presence of VT/VF on the ECG. This is especially important in patients with OPD, because sudden death caused by cardiac arrest may easily be confused with sudden death caused by respiratory failure.14 Another studied the general population, including both urban and rural areas, and captured ~90% of all OHCA cases.27 Some limitations of our study should also be discussed. Non-differential presence of the disease by the use of two prescriptions of respiratory drugs within one year before OHCA. However, these drugs are indicated exclusively for OPD, and patients with OPD who received less than two prescriptions of any respiratory drug result in underestimation of the effect. Similarly, our diagnosis of CVD was based on the use of -adrenoceptor blockers, calcium antagonists, angiotensin converting enzyme inhibitors, diuretics, angiotensin-II receptor blockers, platelet aggregation inhibitors, of antidiabetic medication, anti-arrhythmic drugs or digitalis, may lead to different misdiagnosed as OPD, due to misinterpretation of their dyspnoea or other symptoms, while in fact they had unrecognized heart failure.14 Finally, OPD treatment withdrawal subsequent to the resuscitation may explain part of the observed difference in survival, but was not assessed in our study. Future studies should establish whether in-hospital

In conclusion, we found that OPD patients have a 40% lower chance on 30-day survival after OHCA than non-OPD patients. Survival rates were similar in both groups at the

182 Chapter 10

only after admission to hospital that survival rates of OPD patients became lower than care of OPD patients who suffered OHCA should be adapted in order to close this mortality gap. We aim to raise awareness of the lower survival chances of OPD pathophysiologic basis of this difference.

The authors greatly appreciate the contributions of Paulien Homma, Michiel Hulleman, Esther Landman and Renate van der Meer to the data collection, data entry, and patient follow-up. We are greatly indebted to all participating EMS dispatch centres, ambulance services and pharmacies for their cooperation and support. We thank all the students at the University of Amsterdam who helped collect the data of the onsite AEDs and patient’s pharmacies.

None of the authors (MTB, MJW, AB, JB, RWK, PCS, AWH, FHR, MLdB, AdB and HLT) report a Vici 918.86.616), the Dutch Medicines Evaluation Board (MEB/CBG), and the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement nr. 241679 - the ARITMO Mozaiek 017.003.084). Both grants are unrestricted. The funders had no involvement in the design and The Department of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, has received unrestricted research funding from the Netherlands Organisation for Health Research and Development (ZonMW), the Dutch Health Care Insurance Board (CVZ), the Royal Dutch Pharmacists Association (KNMP), the private-public funded Top Institute Pharma (www.tipharma. nl, includes co-funding from universities, government, and industry), the EU Innovative Medicines Initiative (IMI), EU 7th Framework Program (FP7), the Dutch Medicines Evaluation Board, the Dutch

183

1. Myerburg RJ, Castellanos A. Cardiac arrest and sudden cardiac death. In: Libby P, Bonow RO, Mann DL, Zipes DP, eds. Braunwald’s Heart Disease: A textbook of cardiovascular medicine. Oxford, UK, 2. Hua W, Zhang LF, Wu YF, et al. Incidence of sudden cardiac death in China: analysis of 4 regional 3. Nichol G, Thomas E, Callaway CW, et al. Regional variation in out-of-hospital cardiac arrest incidence 4. Berdowski J, Berg RA, Tijssen JG, Koster RW. Global incidences of out-of-hospital cardiac arrest and 5. Hess EP, White RD. Optimizing survival from out-of-hospital cardiac arrest. J Cardiovasc 6. Rea TD, Cook AJ, Stiell IG, et al. Predicting survival after out-of-hospital cardiac arrest: role of the 7. Sasson C, Rogers MA, Dahl J, Kellermann AL. Predictors of survival from out-of-hospital cardiac 8. Carew HT, Zhang W, Rea TD. Chronic health conditions and survival after out-of-hospital ventricular 9. de Vreede-Swagemakers JJ, Gorgels AP, Dubois-Arbouw WI, et al. Circumstances and causes of out- 10. McNally B, Robb R, Mehta M, et al. Out-of-hospital cardiac arrest surveillance - Cardiac Arrest Registry to Enhance Survival (CARES), United States, October 1, 2005 - December 31, 2010. 11. Longstreth WT Jr, Cobb LA, Fahrenbruch CE, Copass MK. Does age affect outcomes of out-of- 12. Akahane M, Ogawa T, Koike S, et al. The effects of sex on out-of-hospital cardiac arrest outcomes. Am 13. Kim C, Fahrenbruch CE, Cobb LA, Eisenberg MS. Out-of-hospital cardiac arrest in men and women. 14. Rutten FH, Moons KG, Cramer MJ, et al. Recognising heart failure in elderly patients with stable chronic obstructive pulmonary disease in primary care: cross sectional diagnostic study. BMJ. 15. Netherlands Statistics. http://statline.cbs.nl/. Accessed May 15, 2010 description and recognition of an out-of- hospital cardiac arrest in an emergency call. Circulation. 17. Berdowski J, Blom MT, Bardai A, Tan HL, Tijssen JG, Koster RW. Impact of onsite or dispatched 18. Jacobs I, Nadkarni V, Bahr J, et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, Inter-American Heart Foundation,

184 Chapter 10

19. Herlitz J, Ekstrom L, Wennerblom B, Axelsson A, Bang A, Holmberg S. Prognosis among survivors 20. Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health 21. Bardai A, Berdowski J, van der Werf C, et al. Incidence, causes, and outcomes of out-of-hospital cardiac arrest in children. A comprehensive, prospective, population-based study in the Netherlands. J 22. Kendall MJ, Lynch KP, Hjalmarson A, Kjekshus J. Beta-blockers and sudden cardiac death. Ann 23. Teerlink JR, Massie BM. The role of beta-blockers in preventing sudden death in heart failure. J Card 24. Matsue Y, Suzuki M, Nagahori W, Ohno M, Matsumura A, Hashimoto Y. Beta-blocker prevents (PMID:21996409). 25. Salpeter S, Ormiston T, Salpeter E. Cardioselective beta-blockers for chronic obstructive pulmonary 26. Rutten FH, Zuithoff NP, Hak E, Grobbee DE, Hoes AW. Beta-blockers may reduce mortality and risk of exacerbations in patients with chronic obstructive pulmonary disease. Arch Intern Med. 27. Short PM, Lipworth SI, Elder DH, Schembri S, Lipworth BJ. Effect of beta blockers in treatment of

185

186 CHAPTER 11

M.T. Blom, J. Berdowski, A. Bardai, H.L. Tan

Submitted

less when accessing health care than men. We aimed to study whether access to and outcome of cardiopulmonary resuscitation (CPR) after out-of-hospital cardiac arrest (OHCA) differs between men and women.

medical services treated cardiac resuscitation attempts after OHCA in one contiguous region of the Netherlands in 2006-2012.

Exposure: resuscitation attempt after OHCA by emergency medical services personnel. Main outcomes: Neurologically intact survival.

resuscitation attempts by emergency medical services after cardiac OHCA (27.5% odds ratio 0.58, 95%CI 0.50-0.69, P< .001). The gender gap in survival could be explained by a lower rate of a shockable initial rhythm in women (34.4% vs. were less likely to receive bystander CPR, even when OHCA was witnessed by bystanders.

Women are less likely than men to receive CPR treatment by both EMS and bystanders in case of OHCA. When women do receive CPR, they have lower chance of survival. The survival disparity is gender related and can only partly be explained by CPR characteristics.

188 Chapter 11

less when accessing health care than men.1-3 We aimed to study whether access to and outcome after cardiopulmonary resuscitation (CPR) differs between female and male out-of-hospital cardiac arrest (OHCA) patients. OHCA is a leading cause of death in industrialized countries, affecting 300,000 individuals/year in North America.4,5 In Europe, 275,000 OHCA patients are treated annually by emergency medical services (EMS).6 In studies of CPR during OHCA, the proportion of women is generally low: fewer than 30% of OHCA victims in whom resuscitation is attempted are women.7-10 However, several studies show that the proportion of women among sudden cardiac death victims (with or without resuscitation attempt) is much higher.11-13 little chance to be successful. We conducted a comprehensive, prospective, population-based study in The of OHCA. This served three purposes: (1) to compare incidence of OHCA between women and men, (2) to evaluate whether women and men in OHCA have an equal chance of receiving EMS-conducted resuscitation attempt, and (3) to determine whether neurologically intact survival after OHCA is comparable between women and men.

This investigation was a prospective population-based study of persons 20 years old who had an OHCA in the study period January 2006 - December 2012. The study region (North-Holland, a province of The Netherlands) covers 2404 km2 (urban and rural communities) and had a mean population of 1.85 million people aged 20 years during the study period, including 903,481 women and 949,910 men. statistics of all deaths in The Netherlands (national mandatory reporting system)14, and 2) data from the AmsteRdam REscustation Studies (ARREST), an ongoing prospective community-based registry of all EMS-treated resuscitation attempts after OHCA since July 2005 in the study region. Statistics Netherlands records anonymous information on the site and causes information from the police and the public prosecutor is also used. The causes of death

189

190 Chapter 11

revision (ICD-10). ARREST was set up in cooperation with all EMS in the study region to establish the genetic, clinical, pharmacological and environmental determinants of OHCA in the general population15,16 and the determinants of outcome after OHCA.10 The Medical Ethics Review Board of the Academic Medical Center, Amsterdam, approved the study and gave a waiver for the requirement of (written) informed consent.

The population of interest consisted of all OHCA cases that occurred within the study area. Deaths registered by Statistics Netherlands were considered to be OHCA cases if unknown causes, and occurred out-of-hospital. All individuals with OHCA from cardiac causes who received an EMS-treated resuscitation attempt were included via the ARREST database. Resuscitation was considered attempted if ambulance personnel evaluated the OHCA and the victim or chest compressions by organized ambulance personnel. EMS-treated OHCAs were deemed to result from cardiac causes unless an unequivocal non-cardiac cause (e.g., trauma, drowning) was documented by EMS-rescuers, hospital physicians or coroners. EMS witnessed OHCAs were excluded.17

Advanced Life Support.18 After each CPR attempt, EMS paramedics routinely sent AED site shortly after OHCA to collect the AED ECG recording. The ECGs were stored and analyzed with dedicated software (Code Stat Reviewer 7.0, Physio Control, Redmond WA, USA). The initial rhythm was categorized as shockable (ventricular electrical activity). After each resuscitation attempt, EMS personnel called the study center to specify resuscitation characteristics (location of collapse, presence/absence of witness, use of AED, bystander CPR performed). All data were collected according to the Utstein recommendations.17

The incidence rates of OHCA were calculated per 100,000 person-years, in the total study population and per gender and age category (decades). Chance of an EMS- conducted resuscitation attempt was calculated by dividing the number of EMS-treated

191

OHCA cases by the total number of OHCAs (including those who eventually died in-hospital categories would not be reported as death after OHCA in Statistic Netherlands). Patients who survived to admission were surveyed after the resuscitation attempt by contacting the hospital department. We retrieved data on neurological outcome at discharge from the hospital charts to determine the cerebral performance category 17

Results are presented as percentage, per gender and age category. The Mantel-Haenzel method was used to calculate the pooled OR for gender and chance on a resuscitation calculated using logistic regression analysis. NI-survival after resuscitation attempt was calculated in the total OHCA population and in EMS-treated OHCA. NI-survival was regressed on gender, in the regression analysis. Next, in a multivariate logistic regression analysis we determined the association between gender and shockable initial rhythm, while adjusting for CPR characteristics. Descriptive statistics are reported as mean (standard deviation), median (interquartile range), or number (percent) as indicated. Comparisons for continuous variables were made with ANOVA. Chi-square analyses were used when discrete variables were compared across groups. All statistical tests were 2-tailed, and a P Value (version 20.0 for Mac, Chicago IL, USA).

The study region had a mean population aged were women, and 903,481 (48.7%) were men (Supplemental Table s1). In total, 23,443 and 12,387 (52.8%) in men. The yearly incidence of OHCA per 100,000 was 15% gender difference occurred across all age categories.

192 Chapter 11 P

a b

193

During the study period, 7917 resuscitation attempts for OHCA were recorded, 6128 of which had a presumed cardiac cause and were not EMS-witnessed (Figure 1). Of these, 90 were excluded from analysis, because the patients were aged <20 years (n=66) or their age was unknown (n=24), leaving a study population of 6038. In this study population, 1661 (27.5%) were women, and 4377 (72.5%) were men. The yearly incidence of EMS-treated OHCA per 100,000 was 25.0 (95%CI 23.8-26.2) in women and 69.2 (95%CI 67.2-71.3) in men: a difference of 64%. For a third of all women suffering OHCA, bystanders called the EMS (33%) as opposed to for 63.1% of all different in the 20-29 and 40-49 age groups (Figure 2).

men (Table 1). Women were older (69.3 vs. 65.7 years, P< .001), suffered their OHCA less often at a public location (16.4% vs. 34.4%, P< .001) and their OHCA was less P< .001). Also, bystander CPR was performed less P< .001), even when OHCA was witnessed by P P< .004), but the proportion of AED deployment was similar (37.5% vs. 35.9%: P< .270). The initial rhythm was less likely to be shockable in women than in men (34.4% vs. 53.7%: P< .001). In all EMS treated patients, women had a lower odds of NI-survival than men P according to resuscitation characteristic showed that the gender difference in NI- gender difference in survival), and by the location of OHCA (no difference in survival when OHCA occurred at public location). However, when OHCA occurred at a public location, women were still less likely to have a shockable initial rhythm than men (59.8% P< .001, Table 1). NI-survival was only a few percent in both women and men when OHCA was unwitnessed or no bystander CPR was performed. The observed gender disparity in NI-survival was not present when stratifying according to initial rhythm. Gender, age and all resuscitation characteristics were associated with shockable initial rhythm, in both the univariate and multivariate analyses (Supplemental Table s2). Female gender remained independently associated with a lower odds of P< .001). An overview of the female:male ratio in access to care and survival is shown in Figure 3.

194 Chapter 11 (%) 0 (0.0) 1 (0.0) 24 (0.4) 25 (0.4) 61 (1.0) 50 (1.1) 59 (3.3) 101 (1.7) 184 (3.0) 403 (6.6) Missing, No. .001 .270 .004 < .001 < .001 < .001 < .001 < .001 < .001 < .001 P Male 65.7 (13.6) 4377 (72.5) 1492 (34.3) 3297 (76.1) 3203 (74.3) 2462 (75.5) 1640 (37.5) 2274 (53.7) 1039 (72.3) 9.2 (6.7-11.9) Gender 271 (16.4) 803 (68.6) 597 (35.9) 559 (34.4) 159 (59.8) 69.3 (14.6) 1661 (27.5) 1184 (71.9) 1127 (69.2) 9.4 (7.2-12.1) Total 66.7 (14.0) 6038 (100) 1763 (29.3) 4481 (75.0) 4330 (72.9) 3265 (73.7) 2237 (37.1) 2833 (48.4) 1198 (70.3) 9.3 (6.9-11.9) a Events, No. (%) Events, AED used, No. (%) min

195

.723 .193 .096 .530 .215 < .001 < .001 < .001 < .001 < .001 < .001 P 0.58 (0.50-0.69) 0.95 (0.72-1.25) 0.67 (0.54-0.83) 0.60 (0.50-0.71) 0.71 (0.42-1.19) 0.58 (0.49-0.70) 0.73 (0.50-1.06) 0.52 (0.41-0.66) 0.65 (0.52-0.80) 0.94 (0.77-1.14) 0.72 (0.43-1.21) Male 62/1034 (6.0) 53/1955 (2.7) 887/4347 (20.4) 512/1470 (34.8) 368/2858 (12.9) 818/3267 (25.0) 761/3175 (24.0) 118/1105 (10.7) 437/1619 (27.0) 450/2727 (16.5) 816/2246 (36.3) Neurologically intact survival per gender survival intact Neurologically 20/462 (4.3) 40/500 (8.0) 90/267 (33.7) 95/591 (16.1) 21/1062 (2.0) 125/1381 (9.1) 193/553 (34.9) 215/1652 (13.0) 195/1175 (16.6) 174/1119 (15.5) 120/1061 (11.3) Yes No Yes No Yes No Yes No Yes No Neurologically intact survival intact Neurologically CPR Bystander AED used Table 2. Table

196 Chapter 11

We found that the incidence of cardiac OHCA was lower in women than in men. Taking this difference into consideration, the chance of receiving resuscitation care by EMS was twice as low in women as in men. Moreover, the chance of receiving rate of only 13% in comparison to 20% in men. However, NI-survival did not differ This poorer outcome is therefore likely attributable to the observation that women were almost twice less likely to have a shockable initial rhythm than men.

Data on the incidence of OHCA depend largely on the inclusion criteria used.19 When between 1:1 and 2:3.11,12,20 Studies based on EMS data consequently show (less than) 30% female victims, a proportion of 1:3.7-10 This is in accordance with our observation

197

that women have less access to resuscitation care: OHCA victims without EMS- involvement will not be included in EMS-based studies of OHCA.

and early access to CPR is of vital importance, and depends on adequate recognition of OHCA. Only if an ambulance dispatcher suspects an OHCA, and believes the victim could still be resuscitated, the caller will be instructed to give bystander CPR and ambulances are sent. If not, the dispatcher will refer to the coroner.21 Adequate recognition may depend on biological factors. An OHCA in women could be more gradual than in men. In the present study, women were less likely to have a shockable initial rhythm, while a truly sudden OHCA is associated with VT/VF. If women retire to rest because they are not feeling well, not recognizing the urgency of their complaints themselves, chances of a witness being present while they slip into cardiac arrest decrease. This theory is further strengthened by the difference in symptoms described by men and women. When having an acute myocardial infarction (often preceding OHCA), women tend to have more indistinct complaints like fatigue, syncope, vomiting, and neck- or 22 This might also explain the lower bystander CPR rate in women in witnessed OHCAs. Lack of awareness among the public that OHCA may occur in women as well as in men may also hamper adequate recognition.23,24 However, behavioural and circumstantial factors may also play their part: in time for resuscitation efforts to be worthwhile. Since most OHCAs occur at home, persons living alone will thus have a reduced chance on timely resuscitation care. Given their higher life expectancy, women are more likely to be widowed and live alone than men.

There are several studies on differences between men and women in outcome after and a lower proportion of shockable initial rhythm in women. Survival after OHCA is strongly dependent on initial rhythm. Most existing studies focus on the observation women and man largely disappears,7,8,25,26 or turn in favour of women.9,27,28 However, the consequences of the relevant observation that women have a lower overall survival rate because they are less likely to present with a shockable initial rhythm are neglected. Furthermore, a shockable initial rhythm will quickly deteriorate into asystole when

198 Chapter 11

presence/absence of a witness of OHCA, and bystander CPR. The biological causes of the observed lower rate of shockable initial rhythm are unresolved. Chugh et al.29 showed that women are less likely to have a diagnosis of structural heart disease (severe left ventricular dysfunction or previously recognized coronary artery disease [CAD]) as a precursor to sudden cardiac arrest. Both under- recognition and under-treatment of CAD in women, and higher rates of micro- vascular CAD (as opposed to formation of an occlusive clot) among women may be responsible for this observation.30,31 Either way, CAD is most associated with sudden A lower prevalence of recognized CAD in women thus implies that women enjoy fewer preventive opportunities.

A major strength of our study is that ARREST was designed to establish the incidence, determinants and outcome of OHCA in the general population, allowing comprehensive and accurate data collection on out-of-hospital resuscitation care of all cases, from the site of cardiac arrest until discharge from the hospital, including neurologic outcome of care for all OHCA patients. The limitations of our observational study include the fact that cardiac OHCA is a diagnosis by exclusion.17 This may lead to an overestimation of cases that were considered to be due to cardiac causes, especially in patients who did not receive resuscitation care. This could cause an underestimation of the percentage of patients receiving EMS treatment. This underestimation will be largest in the oldest age groups, since a larger proportion of these deaths go unwitnessed. However, it is unlikely that this difference is more prominent in the male than in the female group. Another limitation of our study is that we did not have access to data on the patients’ reported symptoms prior to the arrest. Reported symptoms (or absence thereof) might play a role for family members to call or not call the national emergency number when OHCA occurs. We can now only speculate as to why the difference in chance of resuscitation care exists. One fact remains clear: as long as women in OHCA are treated less, their outcome will lag behind as well. We therefore consider it likely differences in resuscitation care are important and at this point an under-investigated area of research. Accepting differences in shockable initial rhythm as the cause of diverging survival rates between men and women23 does not explain the issue. We encourage observational studies focusing on symptoms prior to the OHCA to further explain the

199

gender difference in resuscitation care, and aim to raise awareness of OHCA in women in order to increase recognition and improve access to resuscitation care.

Women are less likely than men to be resuscitated by EMS and bystanders in case of OHCA. When women do receive CPR, they have lower chance of survival. The survival disparity is gender related and can only partly be explained by CPR characteristics. Further research is needed to determine how to optimize delivery of EMS and lay resuscitation care in women.

We are greatly indebted to all participating EMS dispatch centers (Amsterdam, Haarlem and Alkmaar), regional ambulance services (Ambulance Amsterdam, GGD Kennemerland, Witte Kruis and Schiphol airport and Meditaxi, for their cooperation and support. We greatly appreciate the contributions Renate van der Meer, Anne Spanjaart and Remy Stieglis to acquiring funding, data collection, data entry, and patient follow-up.

None.

200 Chapter 11

2. Johnston N, Schenck-Gustafsson K, Lagerqvist B. Are we using cardiovascular medications and coronary 3. Wenger NK. Women and coronary heart disease: a century after Herrick: understudied, underdiagnosed, 4. Myerburg RJ, Castellanos A. Cardiac arrest and sudden cardiac death. In: Libby P, Bonow RO, Mann BL, Zipes DP, editors. Braunwald’s Heart Disease: a textbook of cardiovascular medicine. Oxford, Stroke Statistics Subcommittee. Heart disease and stroke statistics-2011 update: a report from the 6. Atwood C, Eisenberg MS, Herlitz J, Rea TD. Incidence of EMS-treated out-of- hospital cardiac arrest 7. Herlitz J, Engdahl J, Svensson L, Young M, Angquist KA, Holmberg S. Is female sex associated with 8. Pell JP, Sirel J, Marsden AK, Cobbe SM. Sex differences in outcome following community-based 9. Kim C, Fahrenbruch CE, Cobb LA, Eisenberg MS. Out-of-hospital cardiac arrest in men and women. 10. Berdowski J, Blom MT, Bardai A, Tan HL, Tijssen JG, Koster RW. Impact of onsite or dispatched 11. Zheng ZJ, Croft JB, Giles WH, Mensah GA. Sudden cardiac death in the United States, 1989 to 12. Chugh SS, Jui J, Gunson K, et al. Current burden of sudden cardiac death: multiple source surveillance 13. Stecker EC, Reinier K, Marijon E, et al. Public health burden of sudden cardiac death in the United 14. Statistics Netherlands Statline database. Available at https://statline.cbs.nl/statnet/. 15. Blom MT, Van Hoeijen DA, Bardai A, et al. Genetic, clinical and pharmacological determinants of out-of-hospital cardiac arrest: rationale and outline of the Amsterdam Resuscitation Studies (ARREST) registry. Open Heart. 2014: in press. 16. Warnier MJ, Blom MT, Bardai A, et al. Increased risk of sudden cardiac arrest in obstructive pulmonary Resuscitation Outcomes. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand

201

Resuscitation Council, Heart and Stroke Foundation of Canada, Inter- American Heart Foundation, 18. Neumar RW, Otto CW, Link MS, et al. Part 8: adult advanced cardiovascular life support: 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Sex disparity in resuscitation efforts and outcomes in out-of-hospital cardiac arrest. Am J Emerg Med. 19. Berdowski J, Berg RA, Tijssen JG, Koster RW. Global incidences of out-of-hospital cardiac arrest and 20. van Noord C, Sturkenboom MC, Straus SM, Witteman JC, Stricker BH. Non-cardiovascular drugs that inhibit hERG-encoded potassium channels and risk of sudden cardiac death. Heart. description and recognition of an out-of-hospital cardiac arrest in an emergency call. Circulation. 22. Coventry LL, Finn J, Bremner AP. Sex differences in symptom presentation in acute myocardial 579. 24. Mosca L, Mochari-Greenberger H, Dolor RJ, Newby LK, Robb KJ. Twelve-year follow-up of American women’s awareness of cardiovascular disease risk and barriers to heart health. Circ Cardiovasc Qual 25. Ahn KO, Shin SD, Hwang SS. Sex disparity in resuscitation efforts and outcomes in out-of-hospital 26. Akahane M, Ogawa T, Koike S, et al. The effects of sex on out-of-hospital cardiac arrest outcomes. Am pulseless electrical activity in women compared to men: the Oregon Sudden Unexpected Death Study. 28. Wigginton JG, Pepe PE, Bedolla JP, DeTamble LA, Atkins JM. Sex-related differences in the presentation and outcome of out-of-hospital cardiopulmonary arrest: a multiyear, prospective, 29. Chugh SS, Uy-Evanado A, Teodorescu C, et al. Women have a lower prevalence of structural heart disease as a precursor to sudden cardiac arrest: The Ore-SUDS (Oregon Sudden Unexpected Death 30. Shaw LJ, Bugiardini R, Merz CN. Women and ischemic heart disease: evolving knowledge. J Am Coll Women’s Ischemia Syndrome Evaluation (WISE) Study: Part II: gender differences in presentation, diagnosis, and outcome with regard to gender-based pathophysiology of atherosclerosis and 32. Wissenberg M, Hansen CM, Folke F, et al. Survival after Out-of-Hospital Cardiac Arrest in Relation to Sex: A Nationwide Registry-based Study. Resuscitation. 2014. pii: S0300-9572(14)00597-8.

202 Chapter 11 (95%CI) (95%CI) 4.0 (2.8-5.2) 8.4 (6.7-10.2) 8.7 (7.1-10.4) 196 (192-199) 122 (116-129) 274 (262-285) 609 (586-631) 139 (131-147) 232 (218-246) 370 (342-397) 330 (250-409) 14.4 (12.3-16.5) 48.4 (44.7-52.1) 69.2 (67.2-71.3) 29.4 (26.6-23.3) 71.2 (66.3-76.1) 1913 (1850-1975) 6671 (6313-7029) 88 181 659 1391 2343 2806 3587 1335 12,390 42 66 110 401 809 693 4377 1188 1068 2859 65,863 26,789 903,481 148,885 179,711 194,592 162,422 122,360 (95%CI) (95%CI) 3.1 (2.1-4.1) 6.8 (5.4-8.2) 1.5 (0.8-2.3) 3.1 (2.1-4.0) 9.7 (8.0-11.4) 166 (163-169) 283 (269-297) 119 (108-131) 96.4 (73.2-120) 16.9 (14.7-19.2) 39.6 (36.0-43.3) 92.9 (86.5-99.3) 25.0 (23.8-26.2) 20.4 (17.7-22.9) 37.8 (33.7-41.9) 79.4 (72.0-86.8) 1232 (1195-1270) 5327 (5154-5500) 34 86 223 446 808 1583 4229 3647 11,056 17 39 66 128 229 329 444 409 1661 9780 79,904 49,022 949,910 157,059 181,068 188,105 160,724 124,248 (95%CI) (95%CI) 5.7 (4.7-6.7) 2.8 (2.1-3.5) 5.9 (5.0-6.8) 181 (178-183) 183 (176-189) 430 (417-443) 148 (141-156) 208 (195-220) 149 (124-175) 10.6 (9.3-11.8)) 32.9 (30.8-35.1) 81.2 (77.5-84.9) 46.5 (15.4-47.7) 19.7 (18.1-21.4) 45.9 (43.1-48.7) 87.9 (83.5-92.3) 1473 (1410-1505) 5631 (5475-5788) 122 267 882 1837 3151 4389 7816 4982 23,443 59 149 529 132 1038 1517 1512 1102 6,038 75,811 12,638 305,945 360,779 382,697 323,146 246,608 145,767 1,853,390 20-29 y 30-39 y 40-49 y 50-59 y 60-69 y 70-79 y 80-89 y >89 y 20-29 y 30-39 y 40-49 y 50-59 y 60-69 y 70-79 y 80-89 y >89 y Total (>19 y) Total (>19 y) Total

203

< .001 < .001 < .001 < .001 < .001 < .001 P (95%CI) a 0.55 (0.48-0.63) 0.85 (0.81-0.88) 2.55 (2.22-2.92) 3.47 (3.01-4.00) 1.71 (1.50-1.96) 0.94 (0.92-0.95) < .001 < .001 < .001 < .001 < .001 < .001 0.45 (0.40-0.51) 0.81 (0.78-0.84) 3.66 (3.24-4.13) 3.59 (3.15-4.09) 2.26 (2.00-2.54) 0.92 (0.91-0.93) N=3021 68.8 (14.1) 1957 (64.8) 5063 (16.8) 1910 (64.0) 1930 (65.3) 9.9 (7.4-12.7) N=2833 64.5 (13.4) 2274 (80.3) 1198 (42.5) 2432 (86.5) 2269 (80.9) 8.5 (6.3-11.0)

b No. (%) median (IQR), min

204 CHAPTER 12

M.T. Blom*, S.G. Beesems*, P.C. Homma, J.A. Zijlstra, M. Hulleman, D.A. van Hoeijen, A. Bardai, J.G.P. Tijssen, H.L. Tan, R.W. Koster. * These authors contributed equally

Accepted with revision in Circulation, 2014

out-of-hospital cardiac arrest (OHCA) was advocated in The Netherlands. We aimed to establish whether survival with favorable neurologic outcome after

We performed a population-based cohort study, including patients with OHCA from cardiac causes between 2006 and 2012, excluding EMS-witnessed arrests. We determined survival status at each stage (to emergency room, to admission, to discharge) and examined temporal trends using logistic regression analysis with ‘year-of-resuscitation’ as independent variable. By adding each co-variable subsequently to the regression model, we investigated their impact on the Odds Ratio (OR) of ’year-of-resuscitation’. Analyses were performed according to initial rhythm (shockable vs. non-shockable), and AED-use. Rates of survival with favorable neurologic outcome after OHCA increased for trend<0.001). In this group, survival increased at each stage, but was strongest of AED-use almost tripled during the study period (21.4 to 59.3%, P for trend connection (median 9.9 to 8.0 min, P<0.001). AED-use statistically explained increased survival with favorable neurologic outcome, by decreasing the OR of

Increased AED-use is associated with increased survival in patients with a shockable initial rhythm. We recommend continuous efforts to introduce or extend AED- programs.

206 Chapter 12

Out-of-hospital cardiac arrest (OHCA) is a leading cause of death in industrialized countries, affecting 300 000 individuals/year in North America.1,2 In Europe 275 000 OHCAs are treated annually.3 Although the majority of OHCAs ( 70%) have a cardiac 4 OHCA is lethal in most cases.5,6 Interventions aiming at improved bystander cardiopulmonary resuscitation to increase survival rates after OHCA. A recent study7 showed that national initiatives to increase bystander CPR have improved survival rates in Denmark. In the Netherlands, 8 We aimed to establish (1) whether survival with favorable neurologic outcome after OHCA in the community has increased in the Netherlands between 2006 and changes in resuscitation care may explain altered survival rates.

The Amsterdam Resuscitation Study (ARREST) is an ongoing, prospective registry of all-cause OHCA in the North Holland province of The Netherlands. ARREST was set up to establish the determinants of outcome of OHCA,8,9 and gain insight in the genetic, clinical and pharmacological determinants of OHCA in the general population.10,11 The study region covers 2404 km2 (urban and rural communities) and has a population of 2.4 million people. The present investigation covered the study period January 1, 2006 - December 31, 2012. The Medical Ethics Review Board of the Academic Medical Center, Amsterdam, approved the study and gave a waiver for obtaining (written) informed consent.

In a medical emergency, people dial the national emergency number. When the Emergency Medical Service (EMS) dispatcher suspects OHCA, two ambulances are Life Support. The placement of AEDs in public areas (‘onsite AED’) was stimulated but not centrally controlled or directed.8 Furthermore, the implementation of changed

207

CPR guidelines (from 15 compressions with two ventilations [Guidelines 2000] to 30 compressions with two ventilations [‘30:2 CPR-protocol’, Guidelines 2005] lasted well into 2007.12

After each CPR attempt, EMS paramedics routinely send the continuous ECG from dedicated software (Code Stat Reviewer 9.0, Physio-Control, Redmond WA, USA). If an AED is used, ARREST personnel visit the AED site shortly after the OHCA, and CPR-procedure (witness of OHCA, bystander CPR performed, AED-use) are collected according to Utstein recommendations,13 by means of a pre-coded set of questions that ambulance staff is required to answer, and by retrieving ambulance data of the event. Data on post-resuscitation care (therapeutic hypothermia [TH] or percutaneous coronary intervention [PCI]) were derived from hospital records. Survival was assessed at three time points: survival to transfer of care at ER (hereafter called survival to ER), survival to hospital admission, and survival to hospital discharge (information retrieved from hospital records and civic registry).

Arrests were deemed to result from cardiac causes unless an unequivocal non-cardiac cause was documented (e.g., trauma, drowning). We excluded EMS-witnessed cardiac arrests, aborted resuscitation efforts in individuals with a “do not resuscitate” status, variable, and was determined by analyzing the impedance signal in the continuous ECG Category (CPC) scale was assessed by reviewing hospital charts of patients who survived 2.13-15

To evaluate changes in baseline resuscitation characteristics, hospital care and survival rates by calendar year, we calculated a P-value for trend using Chi-square test (Linear-

208 Chapter 12 by-Linear) for dichotomous data. For continuous variables, we used linear regression Jonckheere-Terpstra test. We evaluated changes in in-hospital care (TH and/or PCI performed) in patients admitted to hospital. We assessed whether survival with favorable neurologic outcome had improved over time using logistic regression analysis, with the independent variable ‘year-of-resuscitation’ as a continuous variable. Survival with rhythm (shockable or non-shockable). Survival per stage (to ER, to hospital admission and to hospital discharge) was regressed by selecting patients who had survived to the previous stage. To establish which factor contributed to increased rates of survival with favorable with ‘year-of-resuscitation’ as the independent variable. We then analyzed which with favorable neurologic outcome. We then added such a variable to the model, to determine whether the OR of ‘year-of-resuscitation’ would be adjusted towards the 1, indicating that the effect of ‘year-of-resuscitation’ is (at least partly) explained by analysis. Hospital-care variables were evaluated in patients admitted to hospital alive. CPR-protocol was assessed for the years 2006 and 2007, since implementation of protocol change occurred in that period.12 Only these years were used when we analyzed the association between protocol and survival with favorable neurologic outcome. data sets using multivariate imputation models using information from all pre-hospital OHCA variables presented in Table 1. Since AED-use is strongly associated with other or no AED-use) before running imputation models. Missing data on outcome were not imputed. We then compared estimates from the observed data set with the estimates from the imputed data sets. Continuous variables were described as means and standard deviations (SD), or medians and interquartile range where appropriate, and categorical variables as absolute numbers analyzed using the statistical software package of SPSS (SPSS for Mac, version 20.0, SPSS Inc.).

Supplemental Table s1). The incidence of EMS treated OHCA (with/without cardiac

209

Flowchart of patient inclus ion

Persons with out-of-hospital cardiac arrest suspected N = 12511

No resuscitation attempted: No out-of-hospital cardiac arrest: N = 3213 Dead on arrival: N = 1262 ‘Do not resuscitate’ order: N = 119

Out-of-hospital cardiac arrest, resuscitation attempted N = 7917

EMS witnessed arrest: N = 672 Non-cardiac cause of the arrest: N = 1112

Study population N = 6133

Shockable initial rhythm Non-shockable initial rhythm Unknown initial rhythm N = 2858 N = 3083 N = 192

Survival with favorable Survival with favorable Survival with favorable neurologic outcome neurologic outcome neurologic outcome N = 1018 (36.0%) N = 75 (2.4%) N = 19 (10.0%) cause) did not change during the study period. Patient and resuscitation characteristics of dispatched police AEDs. There was a large increase over time in AED-use (2006: 21.4% to 2012: 59.3%, p<0.001 for trend). The largest increase concerned dispatched AEDs (16.0% to 48.7% of all resuscitations), but the use of onsite AEDs increased increase the rate of survival with favorable neurologic outcome were increased bystander (9.9 to 8.0 minutes, p<0.001), resulting in increased proportion of delivered shocks <6 minutes (9.2% to 21.7%, p<0.001). Moreover, wide application of the 30:2 CPR- protocol occurred in the period 2006-2007. Changes that may reduce the rate of survival with favorable neurologic outcome were also observed: decreased proportions to 45.1%, p<0.013). Among patients who where admitted to hospital, PCI-treatment

210 Chapter 12 n.a. 5 (0.1) 1 (0.0) 26 (0.4) 29 (0.5) 61 (1.0) 55 (2.4) 36 (1.6) 104 (1.7) 192 (3.1) 390 (6.4) 390 (22.6) 0.065 0.054 0.013 0.001 0.129 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 874 2012 97 (21.7) 632 (72.3) 243 (28.1) 623 (71.6) 699 (81.2) 518 (59.3) 392 (45.1) 238 (75.8) 123 (39.0) 67.1 (14.1) 8.0 (6.1-10.3) 900 2011 638 (71.0) 244 (27.3) 628 (70.5) 673 (76.0) 461 (51.2) 426 (49.1) 121 (24.8) 253 (74.6) 124 (35.7) 66.3 (16.0) 7.6 (5.4-10.5) 887 2010 56 (12.0) 622 (70.1) 246 (28.0) 647 (74.6) 635 (73.6) 385 (43.5) 391 (46.2) 264 (78.8) 120 (35.5) 67.6 (14.6) 9.0 (7.1-11.4) 871 298 2009 59 (12.5) 644 (73.9) 262 (30.1) 637 (73.9) 623 (73.0) 294 (33.8) 391 (46.9) 218 (75.7) 118 (40.0) 65.9 (15.0) 9.7 (7.2-12.5) 874 2008 61 (12.2) 630 (72.4) 287 (32.9) 674 (77.9) 605 (70.9) 208 (23.8) 416 (48.7) 232 (71.8) 115 (35.7) 65.1 (15.6) 9.6 (7.2-12.6) 876 2007 13.2) 49 (9.6) 10.7 (8.5- 640 (73.1) 260 (29.7) 672 (77.2) 612 (70.5) 210 (24.0) 413 (48.6) 546 (84.1) 203 (66.3) 113 (36.8) 65.1 (15.9) 851 2006 12.4) 48 (9.2) 9.9 (7.6- 96 (30.9) 639 (75.1) 256 (30.1) 662 (78.2) 557 (65.8) 182 (21.4) 429 (52.2) 170 (24.7) 215 (69.1) 65.0 (15.7) (SD) years, Mean age, AED-use, n (%) 30:2 used, n (%) Protocol n (%)

211

(n=593) either because patients were already conscious (n=369, 62.2%), or because only palliative care was given (n=138, 23.3%). Only 86 (14.5%) patients did not receive were not performed.

Table 2 shows rates of survival with favorable neurologic outcome per year. In the total neurologic outcome occurred from 16.2% in 2006 to 19.7% in 2012 (OR 1.04, 95% CI 1.01-1.07). This was largely due to improved rates among patients with shockable of survival with favorable neurologic outcome was observed among patients with non- shockable further analyses in this patient group.

212 Chapter 12 n(%) n(%) 4 (0.1) 2 (1.0) 0 (0.0) 4 (0.2) 8 (0.5) 41 (0.7) 35 (1.2) 23 (2.1) 1.04 (1.01-1.07) 1.08 (1.04-1.12) 1.02 (0.91-1.14) 1.00 (0.76-1.30) 1.11 (1.06-1.16) 1.05 (1.01-1.11) 1.06 (1.01-1.11) 1.09 (0.96-1.24) 0.021 0.752 1.000 0.028 0.016 0.167 <0.001 <0.001 2012 2012 13 (2.7) 1 (20.0) 168 (19.7) 154 (41.4) 339 (86.5) 259 (76.6) 178 (69.5) 154 (95.1) 2011 2011 2 (6.1) 11 (2.5) 172 (19.3) 159 (37.9) 351 (82.4) 278 (79.7) 174 (72.8) 159 (93.5) 2010 2010 14 (3.1) 5 (12.8) 166 (18.8) 147 (37.9) 339 (86.7) 261 (77.0) 159 (60.9) 147 (94.2) 2009 2009 7 (1.6) 2 (5.3) 150 (17.3) 141 (36.5) 324 (82.9) 238 (73.7) 150 (64.1) 141 (94.0) 2008 2008 9 (2.1) 3 (15.8) 183 (21.0) 171 (41.1) 339 (81.5) 262 (77.3) 180 (68.7) 171 (95.0) 2007 2007 9 (2.1) 5 (18.5) 135 (15.4) 121 (29.3) 332 (80.4) 233 (70.2) 130 (55.8) 121 (93.4) 2006 2006 1 (3.4) 12 (3.1) 138 (16.2) 125 (29.1) 324 (75.5) 239 (73.8) 139 (58.2) 125 (89.9) ER To

213

100%

90%

80%

70%

60%

50%

Percentage 40% No AED-use 30% Dispatched AED Local AED 20%

10%

0% 2006 2007 2008 2009 2010 2011 2012 Year

Among patients with shockable rhythm, survival rates increased at each 95% CI 1.06-1.16). Among patients who reached ER alive, the proportion of those 1.11), as did the proportion of patients discharged alive after admission to hospital neurologically intact at hospital discharge remained high throughout the study period

To explore which changes might explain the increase in survival with favorable neurologic outcome in patients with a shockable rhythm, we analyzed which variables changed the crude OR of ‘year-of-resuscitation’ towards 1 when added to the logistic regression model. Variables not included in the analyses were ‘Male sex’ and ‘PCI-treatment’ (no change), and ’30:2 CPR-protocol’ (no association16) (Supplemental Table s2). Table 3 shows the (adjusted) ORs of ‘year-of-resuscitation’ with each consecutively added variable. Only two variables decreased the OR of ‘year-of-resuscitation’ to a non- what might explain the observed increased rates of survival with favorable neurologic

214 Chapter 12 0.018 0.007 0.006 0.011 0.008 0.019 n.a. 1.07 (1.01-1.13) 1.08 (1.02-1.14) 1.08 (1.02-1.14) 1.08 (1.02-1.14) 1.08 (1.02-1.14) 1.07 (1.01-1.14) 0.867 0.743 0.905 0.663 0.871 0.790 n.a. 1.01 (0.95-1.07) 1.01 (0.95-1.07) 1.00 (0.94-1.07) 1.01 (0.95-1.08) 1.01 (0.95-1.07) 0.99 (0.93-1.06) 0.064 0.150 <0.001 <0.001 <0.001 <0.001 <0.001 1.08 (1.04-1.13) 1.09 (1.05-1.14) 1.09 (1.04-1.13) 1.09 (1.05-1.13) 1.07 (1.03-1.12) 1.04 (1.00-1.08) 1.03 (0.99-1.07)

215

this group, AED-use statistically explained the improved survival rates. Adding other variables to the model did not show relevant changes. In the patient group without ‘year-of-resuscitation’. Repeating the analysis with imputation for missing data showed comparable results (Supplemental Tables s3 and s4).

We report an increase in rates of survival with favorable neurologic outcome after OHCA (16.2% to 19.7%) in The Netherlands during a 7-year study period, though solely in patients presenting with a shockable rhythm (29.1% to 41.4%). Survival increased at each stage of the resuscitation process, but the strongest trend was found in the pre-hospital phase (OR 1.11). The proportion of patients that were neurologically intact remained high throughout the study period (89.9% to 95.1%). Rates of AED-use almost tripled during the study period (21.4% to 59.3%), thereby decreasing the time rate for a considerable part.

When compared with other countries, Dutch pre-hospital resuscitation care had already high standards at the beginning of our study period.17-19 For instance, the increased bystander CPR rate to 44.9% in 2010 in Denmark as reported by Wissenberg et al.7 is still low compared to the 65.8% of resuscitations with bystander CPR at the start of our study period in 2006. Similarly, the Danish increase in 30-day survival (to 10.8% in 2010) is much lower than the rate of survival with favorable neurologic outcome in 2006 (16.2%) in the present study. Nonetheless, even in The Netherlands, there was ample room for improvement. The survival analysis at each consecutive stage of resuscitation care showed that the largest increase was found in the pre-hospital phase, indicating that the large investments in improvement of pre-hospital care had the desired effects. The subsequent increased survival rate after hospital admission clearly shows that improving pre-hospital survival rates after OHCA is not ‘merely changing the place to die’, as some skeptics of et al.20 we also show that survival with favorable neurologic outcome among patients

216 Chapter 12 who were admitted to hospital alive was still strongly associated with pre-hospital factors (Supplemental Table s5). Nonetheless, in the patient group in which no AED was used we still observed an increase in survival that was not explained by the studied pre-

Previous studies21,22 with the implementation of police AED programs. In our study region, apart from the AED and trained in basic life support, thereby steadily increasing the number of available dispatched AEDs. Furthermore, the Netherlands Heart Foundation has launched the ‘six-minutes zone’ campaign in 2007, aiming to raise AED-awareness in the community The proportion of resuscitations with connection times <6 minutes increases when an increasing use of dispatched-AEDs8 will have a large impact at the population level. At present, new regional programs are being implemented in the study area, in which registered volunteers are alerted by text message to their mobile phone if an OHCA has occurred in their vicinity, and are directed to the OHCA victim or the closest onsite AED. The number of OHCAs with AED-use is thus likely to increase further.

First, our analyses are based on observational data. Hence, we can only calculate are explained by certain factors. Due to our study design, we cannot prove causality. Second, we did not include more detailed covariates of the resuscitation process such as the quality of given CPR and the time in recurrent VF, although these variables are of importance for survival with favorable neurologic outcome.16,23 Third, we had to accept a modest level of missing data (Table 2). However, analyses with our imputed datasets did not show relevant differences regarding our main estimates when compared with the observed datasets. Lastly, although we do acknowledge the importance of in- hospital post-resuscitation care, our study’s main focus was on pre-hospital parameters. Therefore, we were unable to provide more detailed information about in-hospital care beyond the application of TH and PCI.

217

after OHCA between 2006 and 2012 in The Netherlands in patients presenting with a shockable rhythm. Survival increased at each stage of the resuscitation process, but the strongest trend was observed in the pre-hospital phase. AED-use rates almost connection. Increased AED-use is associated with increased survival in patients with a shockable initial rhythm. We recommend continuous efforts to improve resuscitation care, with strong emphasis on introducing or extending AED programs, involving both dispatched AEDs and onsite AEDs.

We are greatly indebted to all participating EMS dispatch centers (Amsterdam, Haarlem and Alkmaar), regional ambulance services (Ambulance Amsterdam, GGD Kennemerland, Witte Kruis and Schiphol airport and Meditaxi, for their cooperation and support. We greatly appreciate the contributions of Loes Bekkers, Jocelyn Berdowski, Jeanet Glas, Roos Konings, Esther Landman, Renate van der Meer, Anne Spanjaart and Remy Stieglis to the data collection, data entry, and patient follow-up. We thank all the students of the University of Amsterdam who helped collect the AED data.

918.86.616), the Dutch Medicines Evaluation Board (MEB/CBG) the European Community’s Seventh Framework Programme (FP7, grant 241679, ARITMO), and Biobanking and Biomolecular Research Infrastructure The Netherlands (BBMRI-NL) RWK received funding for the ARREST data collection by unconditional grants from Physio-Control USA), Cardiac Science (Waukesha, WI, USA), ZonMW (#82711001) and the Dutch Heart Foundation (#2010T083). The funders had no access to the data and did not contribute to the preparation of this manuscript. Disclosures: none.

218 Chapter 12

1. Myerburg RJ, Castellanos A. Cardiac arrest and sudden cardiac death. In: Libby P, Bonow RO, Mann BL, Zipes DP, editors. Braunwald’s Heart Disease: a textbook of cardiovascular medicine. Oxford, Stroke Statistics Subcommittee. Heart disease and stroke statistics-2011 update: a report from the 3. Atwood C, Eisenberg MS, Herlitz J, Rea TD. Incidence of EMS-treated out-of- hospital cardiac arrest 4. Myerburg, R.J. Sudden cardiac death: exploring the limits of our knowledge. J. Cardiovasc. Electrophysiol. 12, 369–381 (2001). 5. Iwami T, Nichol G, Hiraide A, et al. Continuous improvements in “chain of survival” increased survival after out-of-hospital cardiac arrests: a large-scale population-based study. Circulation. 6. Aufderheide TP, Yannopoulos D, Lick CJ, Myers B, Romig LA, Stothert JC, Barnard J, Vartanian L, Pilgrim AJ, Benditt DG. Implementing the 2005 American Heart Association Guidelines improves 7. Wissenberg M, Lippert FK, Folke F, et al. Association of national initiatives to improve cardiac arrest management with rates of bystander intervention and patient survival after out-of-hospital cardiac 8. Berdowski J, Blom MT, Bardai A, Tan HL, Tijssen JG, Koster RW. Impact of onsite or dispatched 9. Bardai A, Berdowski J, van der Werf C, et al. Incidence, causes, and outcomes of out-of-hospital cardiac arrest in children. A comprehensive, prospective, population-based study in the Netherlands. J locus at 2q24.2 through genome-wide association in European ancestry individuals. PLoS Genet. 12. Berdowski J, Schmohl A, Tijssen JG, Koster RW. Time needed for a regional emergency medical system to implement resuscitation Guidelines 2005--The Netherlands experience. Resuscitation. Resuscitation Outcomes. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, Inter- American Heart Foundation, 14. Herlitz J, Ekstrom L, Wennerblom B, Axelsson A, Bång A, Holmberg S. Prognosis among survivors

219

15. Brain Resuscitation Clinical Trial I Study Group. A randomized clinical study of cardiopulmonary- 16. Berdowski J, ten Haaf M, Tijssen JG, Chapman FW, Koster RW. Time in recurrent ventricular 1431. 18. Sasson C, Rogers MA, Dahl J, Kellermann AL. Predictors of survival from out-of-hospital cardiac 19. Berdowski J, Berg RA, Tijssen JG, Koster RW. Global incidences of out-of-hospital cardiac arrest and 20. Hollenberg J, Lindqvist J, Ringh M, et al. An evaluation of post-resuscitation care as a possible 252. 22. Husain S, Eisenberg M. Police AED programs: a systematic review and meta-analysis. Resuscitation. 23. Vadeboncoeur T, Stolz U, Panchal A, et al. Chest compression depth and survival in out-of-hospital

220 Chapter 12 7917 6686 6133 12511 81 45 36 943 874 2012 2002 1102 2,463 780 77 48 37 990 900 2011 1889 1181 2,446 997 72 48 37 978 887 2010 1750 1166 2,425 508 78 48 36 958 871 2009 1878 1162 2,402 969 66 47 37 957 874 2008 1571 1114 2,384 015 77 46 37 938 876 2007 1827 1102 2,371 123 67 46 36 922 851 2006 1594 1090 2,364 768 residents Including ambulance witnessed ambulance witnessed / 100,000 residents

221

Age, decades 0.75 (0.71-0.79) <0.001 1.07 (0.88-1.29) 0.522 2.45 (2.09-2.87) <0.001 2.69 (2.06-3.51) <0.001 1.90 (1.54-2.35) <0.001 AED-use 1.77 (1.51-2.07) <0.001 Protocol 30:2 used 1.00 (0.72-1.39) 0.993 0.86 (0.84-0.88) <0.001

1.04 (1.01-1.07) 0.025 1.08 (1.04-1.13) <0.001 1.01 (0.90-1.14) 0.854 ER 1.11 (1.05-1.16) <0.001 1.06 (1.01-1.12) 0.023 1.06 (1.01-1.12) 0.015 1.09 (0.96-1.24) 0.171

222 Chapter 12 0.024 0.011 0.008 0.013 0.015 0.055 No AED-use n.a. 1.07 (1.01-1.13) 1.08 (1.02-1.14) 1.08 (1.02-1.14) 1.07 (1.02-1.13) 1.07 (1.01-1.13) 1.06 (1.00-1.12) 0.469 0.339 0.481 0.303 0.474 0.850 AED-use n.a. 1.02 (0.96-1.09) 1.03 (0.97-1.10) 1.02 (0.96-1.09) 1.03 (0.97-1.10) 1.02 (0.96-1.09) 1.01 (0.95-1.07) 0.031 0.109 <0.001 <0.001 <0.001 <0.001 <0.001 1.08 (1.04-1.13) 1.09 (1.05-1.14) 1.09 (1.04-1.13) 1.09 (1.05-1.14) 1.07 (1.03-1.12) 1.05 (1.00-1.09) 1.03 (0.99-1.08)

223

Age, decades 0.64 (0.59-0.70) <0.001 1.25 (0.96-1.61) 0.093 1.70 (1.36-2.12) <0.001 2.09 (1.46-2.96) <0.001 1.43 (1.08-1.89) 0.012 0.89 (0.86-0.92) <0.001

224 CHAPTER 13

226 Chapter 13

Sudden Cardiac Arrest (SCA) accounts for 15-20% of all natural deaths in Western Europe. SCA occurs predominantly in the general population and is often lethal. Although the largest part of out-of-hospital SCAs ( 70%) has a cardiac cause, in ~50% disorders, stressors and circumstantial triggers that eventually initiate an SCA. In order to reduce the number of deaths due to SCA, two main approaches can be distinguished: 1) identify those at risk in order to prevent SCA, and 2) optimize out- of-hospital resuscitation care and in-hospital post-resuscitation care to improve survival after SCA. The risk of the occurrence of SCA itself can be studied, but also of markers of SCA risk, such as certain abnormalities on the electrocardiogram (ECG), in order to explore proposed mechanisms involved in SCA risk. Part I of this thesis presents four studies on SCA risk markers on the ECG, whereas Part II and Part III present studies on risk factors and outcome of out-of-hospital cardiac arrest and determinants thereof.

In a recent study, our group showed that people with epilepsy are at increased risk chapter 2 we further explored this subject and determined whether ECG risk markers of SCA (abnormalities on the ECG that are associated with increased SCA risk) are more prevalent in people with epilepsy. In a cross-sectional, retrospective study, we analyzed the ECG recordings of 185 people with refractory epilepsy and 178 controls without epilepsy, and collected data on epilepsy characteristics, cardiac comorbidity, and drug use. Three markers of SCA risk were studied: QTc prolongation (male >450 ms, female >470 ms), the Brugada ECG pattern, and early repolarization pattern (ERP). QTc prolongation and ERP were ERP: 34% vs. 13%, p<0.001), while the prevalence of the Brugada ECG pattern was comparable in both groups (2% vs. 1%, p=1). After adjustment for covariates, epilepsy remained associated with ERP (ORadj 2.4, 95% CI 1.1 to 5.5) and severe QTc prolongation (ORadj by comorbidity or medication use. Chapter 3 has a similar approach in a different patient group: patients with schizophrenia. SCA risk is increased in these patients, but the underlying causes are not fully resolved. In this study, we compared ECG markers of SCA risk, in particular, the Brugada ECG pattern and QTc prolongation, between patients with schizophrenia and community-dwelling non-schizophrenia controls. In a cross-sectional study, we analyzed ECGs of a cohort of 275 patients with schizophrenia, along with medication

227

age (n=179) and, to account for assumed increased ageing rate in schizophrenia, with individuals 20 years older (n=1168). We found that the Brugada ECG pattern is in patients with schizophrenia who were not using sodium channel blocking drugs (notably antipsychotics). In contrast, although we also found a higher prevalence of QTc prolongation in schizophrenia patients (5.8% vs. 0% and 2.8%, p<0.05), QTc prolongation was largely explained by confounding factors, including the use of QTc prolonging (antipsychotics) drugs. aiming to explain why people with epilepsy and schizophrenia carry an increased risk of SCA. It is conceivable that variants in genes expressed in heart and brain might confer both a propensity for epilepsy or schizophrenia and an innate vulnerability to cardiac arrhythmias, thereby linking these diseases with ECG-markers of SCA risk. Nonetheless, although the use of sodium channel blocking or QTc prolonging drugs did not explain screening in patients with schizophrenia or epilepsy and prudent prescription of drugs that affect the sodium channel or prolong the QT-interval (epilepsy), to minimize SCA risk.

Chapter 4 presents a study on the effects of low-dose haloperidol on the QTc-interval in an in-hospital population. Haloperidol has established QT-prolonging effects on the comorbidities is less well studied. We aimed to evaluate changes in QTc duration during low-dose haloperidol use, and determined associations between clinical variables and potentially dangerous QTc prolongation. In a retrospective single-center cohort measured before, during and after haloperidol use. Clinical variables before haloperidol use (‘baseline’) and at the time of each ECG recording were retrieved from hospital records. We found that substantial QTc prolongation upon low-dose haloperidol use occurred predominantly in patients with normal baseline QTc duration. Conversely, in most patients with borderline or abnormal baseline QTc duration, an unexpected QTc shortening occurred. Importantly, 94% of patients with potentially dangerous QTc prolongation had a baseline QTc duration in the normal range. A rise in QTc duration to potentially dangerous levels (increase >50 ms or to >500 ms) was not

228 Chapter 13 only associated with baseline QTc duration, but also with surgery (increased risk) and cardiovascular risk factors are reported to have a higher risk of QTc prolongation, we In chapter 5 we further explored this subject in a study among old-aged hospitalized patients with multiple co-morbidities undergoing hip surgery. Haloperidol is often prescribed perioperatively to hip fracture patients. Since the study presented to determine (1) how QTc duration changes perioperatively, (2) whether low-dose with potentially dangerous perioperative QTc prolongation. We included 89 patients (mean age 84 years, 24% male), 39 of whom were treated with haloperidol, and analyzed their ECGs before, during and after hip surgery. Here, too, we found that perioperative QTc durations changed differentially, observing the same pattern as in chapter 4, with substantial QTc prolongation occurring predominantly in patients with normal baseline (before-surgery) QTc duration, and QTc shortening in most patients with abnormal baseline QTc duration. Changes in perioperative QTc duration were not We consider it unlikely that QTc shortening in patients with a prolonged QTc interval prior to haloperidol use is due to the effects of haloperidol per se. A more plausible explanation might be that changes in the underlying condition occurred during haloperidol use, and that these changes caused QTc duration to shorten, despite ECG recording as a standard measure in hospital care. We still recommend studying

Chapter 7 presents a review of the cardiac sodium channel and inherited electrophysiological disorders, and provides an overview on pharmacotherapy. The cardiac sodium channel plays a pivotal role in the propagation of electrical activity

229

conditions, e.g., the use of drugs prescribed for the treatment of non-cardiac disease, such as antidepressants and anti-epileptics.

Chapter 8 presents a study aiming to determine whether nortriptyline increases the risk for SCA, and to establish the underlying mechanisms. To this end, we studied QRS durations during rest/exercise in an index patient who experienced ventricular tachycardia during exercise while using nortriptyline, and compared them with those of 55 controls with/without nortriptyline and 24 controls with Brugada syndrome (BrS) without nortriptyline, who carried an SCN5A mutation. We performed molecular- genetic (exon-trapping) and functional (patch-clamp) experiments to unravel the mechanisms of QRS prolongation by nortriptyline and the SCN5A mutation found in the index patient. We conducted a prospective community-based study among 944 database including drug-dispensing records from community pharmacies of >3 million community-dwelling inhabitants in the Netherlands) to determine the risk for SCA associated with nortriptyline use. We showed that pharmacological (nortriptyline), genetic (loss-of-function SCN5A mutation), and/or functional (sodium channel inactivation at fast heart rates) factors conspire to reduce the cardiac sodium current and increase the risk for SCA. Nortriptyline use in the community was associated with a 4.5- fold increase in the risk for SCA [adjusted OR: 4.5 (95% CI: 1.1-19.5)]. We conclude that the arrhythmia causing combination of mechanisms blocking the cardiac sodium the risk for SCA, particularly in the presence of genetic and/or non-genetic factors that decrease cardiac excitability. When prescribing or using a sodium channel blocking drug like nortriptyline we recommend to be conscious of other factors that may affect the sodium current (e.g., prolonged baseline QRS duration on the ECG, co-medication, ischemia or heart failure, high heart rates), analogous to the recommendations for QT prolonging medication.

Chapter 9 describes a community-based case-control study that aimed to determine whether patients with obstructive pulmonary disease (OPD) have an increased risk of and/or respiratory drug use. We included 1310 SCA cases (ARREST) older than 40 years, and 5793 matched controls (PHARMO), and observed a higher risk of SCA in patients with OPD (n = 190 cases [15%], 622 controls [11%]) than in those without

SCA risk was highest among OPD patients who received short-acting 2-adrenoreceptor agonists (SABA) or anticholinergics (AC) at the time of SCA (SABA OR: 3.9 [1.7–8.8],

230 Chapter 13

integrated pulmonary and cardiovascular care in patients with OPD.

outcome of resuscitation attempts after OHCA, and (non-cardiac) determinants thereof. Variability in survival rates is largely attributable to differences in the chain of survival: care. However, these factors do not entirely explain the variability in survival after Chapter 10 describes a community-based cohort study of 1172 patients with OHCA due to VT/VF between 2005 and 2008 that aimed to study whether patients with OPD have a lower survival rate after OHCA than non-OPD patients. We compared survival to Emergency Room (ER), to hospital admission, to hospital discharge, and at 30 days after OHCA, of OPD- patients and non-OPD patients. OPD patients (n = 178) and non-OPD patients (n = 994) had comparable survival to ER (75% vs. 78%, OR 0.9 [95% CI: 0.6–1.3]) and to hospital admission (56% vs. 57%, OR 1.0 [0.7–1.4]). However, survival to hospital 0.9]). In those patients who were admitted to hospital after OHCA (OPD: n = 100, no OPD: n = 561) OPD was an independent determinant of reduced 30-day survival in-hospital post-resuscitation care of OPD patients who suffered OHCA may need adaptation in order to close this mortality gap. We aim to raise awareness of the lower provide insight into the pathophysiologic basis of this difference.

Chapter 11 addresses another aspect of survival after OHCA. We aimed to study whether access to and outcome after CPR differs between female and male OHCA patients. We performed a population-based cohort study of all cardiac OHCA patients and all emergency medical services-performed cardiac resuscitation attempts after 22,443 OHCAs of cardiac cause (47.2% female), and recorded 6,038 resuscitation attempts by emergency medical services after cardiac OHCA (27.5% female). Women women than in men (13.0% vs. 20.4%, odds ratio 0.58, 95%CI 0.50-0.69, P< 0.001).

231

The gender gap in survival was caused by a lower rate of a shockable initial rhythm in or other CPR characteristics. Also, female OHCAs were less likely to receive bystander CPR, even when OHCA was witnessed by bystanders. We conclude that women are less likely than men to receive CPR treatment by both EMS and bystanders in case of OHCA. When women do receive CPR, they have lower chance of survival. The survival disparity is gender related and can only partly be explained by CPR characteristics. We recommend raising awareness of SCA in women in order to increase recognition and improve access to resuscitation care.

Finally, in Chapter 12 we examine whether temporal trends can be distinguished in neurologically intact survival after OHCA, and, if so, whether a change in survival is attributable to increased AED use. Interventions aiming at improved bystander order to increase survival rates after OHCA. In the Netherlands, national initiatives available AEDs. We performed a population-based cohort study in the Netherlands, including all patients with OHCA from cardiac causes in the Dutch province of North Holland between 2006 and 2012, excluding EMS-witnessed arrests. Survival rates with favorable neurologic outcome after OHCA increased (16.2% to 19.7%, p for trend 0.021), though solely in patients presenting with a shockable rhythm (29.1% to 41.4%, p for trend <0.001). Survival rates increased at each stage of the resuscitation surviving patients with favorable neurological outcome remained high throughout the study period (89.9% to 95.1%). Rates of AED-use almost tripled during the study period (21.4 to 59.3%, P for trend <0.001), thereby decreasing time from emergency- in increased proportion of delivered shocks <6 minutes (9.2% to 21.7%, p<0.001). AED-use statistically explained increased survival with favorable neurologic outcome, that increased AED-use is associated with increased survival in patients with a shockable initial rhythm. We recommend continuous efforts to improve resuscitation care, with strong emphasis on introducing or extending AED programs, involving both dispatched AEDs and onsite AEDs.

232 Chapter 13

disease entity. In both the general population and in hospitalized patients, SCA, or markers thereof, results from a myriad of classically established indicators of high risk cardiac diseases, common genetic variants, environmental determinants). Because of the critically.

Both the ECG studies and the ARREST studies presented in this thesis are based on observational data. This introduced challenges when aiming to separate the effects of the studied disease (obstructive pulmonary disease, schizophrenia, epilepsy) from the effects of the medication used to treat the diseases. Also, although we have attempted to address confounding in various ways, residual bias due to measured and unmeasured confounding cannot be ruled out. The problem of confounding holds for most if not all observational studies. In contrast, randomized clinical trials are generally considered the gold standard for identifying causal associations. However, randomized clinical trials in SCA are hard to set up due to the relatively rare occurrence of SCA (1:1000 person years on average in the general population): impractically large numbers of participants would be needed. Also, some SCA-risk factors (e.g. genitic factors, chronic disease) are not easily randomized. Although observational studies have disadvantages, they may yield more representative, real-life data than other study designs. Many real-life interactions between co-morbidities, drugs and other triggers may not occur in randomized trials, and hence cannot be studied.

For the future, much work remains to be done to identify risk indicators and to develop risk scores that are either applicable in the general population or in selected patient groups. This is a challenging task as the majority of SCA occurs in the general population general population, their interactions and ultimately the mechanisms from which they arise is needed. Detection of risk factors is most likely to yield effective patient tailored prevention tools when the underlying mechanism is uncovered and targeted. To accomplish this, large registries with large numbers of well-phenotyped SCA cases (describing a multitude of possible risk factors and SCA triggers) are essential. This is especially so in the study of genetic risk factors and their interactions with drugs and acquired disease. ARREST aims to continue its registration of out-of-hospital SCA cases, nowadays in collaboration with other SCA cohorts worldwide.

233

Perhaps, at present, prevention of SCA is best achieved by raising awareness among physicians and patients that SCA is the result of a combination of factors that might be avoided. General risk scores help to identify people that need further treatment, or

Not only prevention of SCA, but also adequate resuscitation upon OHCA will remain the study period covered in this thesis. Adequate recognition and early access to early In this thesis, we have demonstrated the importance of AEDs to increase survival after OHCA in the general population. Furthermore, we have shown that patient We recommend continuous effort to improve resuscitation care, with strong emphasis on introducing or extending AED programs, involving both dispatched AEDs and onsite AEDs. The lay public will have an increasing role in timely recognizing OHCA and adequately providing resuscitation care.

234 CV

In West Europa wordt 15-20% van alle sterfgevallen met een natuurlijke oorzaak veroorzaakt door een plotselinge hartstilstand. Plotselinge hartstilstand komt een plotselinge hartstilstand meestal een cardiale oorzaak heeft (70%), is in ongeveer 50% van deze gevallen een hartstilstand het eerste teken van cardiovasculaire ziekte. Plotselinge hartstilstand in de algemene bevolking is het gevolg van een combinatie omstandigheden. Om het aantal sterfgevallen door een plotselinge hartstilstand te diegenen die risico lopen, om daarmee een plotselinge hartstilstand te voorkomen, en 2) optimaliseer de keten van zorg na een reanimatie buiten het ziekenhuis om de overleving na een plotselinge hartstilstand te verbeteren. Het risico van het optreden van een plotselinge hartstilstand zelf kan bestudeerd worden, maar ook indicatoren van risico op plotselinge hartstilstand kunnen onderwerp van studie zijn. Zo’n indicator is bijvoorbeeld een afwijking op het elektrocardiogram (ECG). Deel I van dit proefschrift beschrijft vier studies van risico-indicatoren op het ECG, terwijl deel II en deel III studies van risicofactoren voor het optreden van en overleving na een plotselinge hartstilstand buiten het ziekenhuis beschrijven.

In een recente studie hebben wij aangetoond dat mensen met epilepsie een verhoogd risico hebben op plotselinge hartstilstand buiten het ziekenhuis door een ventriculaire hoofdstuk 2 hebben we dit onderwerp verder onderzocht en bekeken of afwijkingen op het ECG die geassocieerd zijn met een verhoogd risico op plotselinge hartstilstand vaker voorkomen bij mensen met epilepsie. In een cross-sectionele, retrospectieve studie analyseerden we de ECGs van 185 mensen met refractaire epilepsie en van 178 controles zonder epilepsie, waarbij we gegevens verzamelden over epilepsie, cardiale co-morbiditeit, en medicatiegebruik. Drie risico- indicatoren van plotselinge hartstilstand werden bestudeerd: QTc-verlenging, het Brugada ECG patroon, en ‘early repolarisation pattern’ (ERP). QTc-verlenging en ERP 34% vs. 13%), terwijl de prevalentie van het Brugada ECG patroon vergelijkbaar was in beide groepen (2% vs. 1%). Na correctie voor co-variaten bleef epilepsie geassocieerd met ERP (ORadj 2,4) en ernstige QTc-verlenging (ORadj 9,9). Onze bevindingen werden niet verklaard door co-morbiditeit of medicijngebruik.

Hoofdstuk 3 beschrijft een vergelijkbare aanpak in patiënten met schizofrenie. Het risico op plotselinge hartstilstand is verhoogd bij deze patiënten, maar de onderliggende oorzaken daarvan zijn niet volledig duidelijk. In deze studie vergeleken we ECG-indicatoren van verhoogd risico op plotselinge hartstilstand, (in het bijzonder het Brugada ECG patroon en QTc-verlenging) tussen patiënten met schizofrenie en twee controle groepen zonder schizofrenie uit de algemene bevolking. In een cross-sectionele studie analyseerden we ECGs van een cohort van 275 patiënten met schizofrenie, in samenhang met medicatiegebruik. We vergeleken de resultaten met niet-schizofrene individuen van vergelijkbare leeftijd (n=179) en, om rekening te houden met de veronderstelde versnelde veroudering bij schizofrenie, met individuen die gemiddeld vaker voorkwam bij schizofrenie patiënten (11,6%) dan bij de controle cohorten (1,1% en 2,4%). De prevalentie was eveneens verhoogd bij patiënten met schizofrenie die geen medicatie gebruiken welke het cardiale natriumkanaal blokkeert (zoals sommige antipsychotica). We vonden ook een hogere prevalentie van QTc-verlenging bij schizofrenie patiënten (5,8% vs. 0% en 2,8%), maar deze werd grotendeels verklaard door andere factoren, zoals het gebruik van het QTc-verlengende (antipsychotische) medicatie. De bevindingen uit hoofdstuk 2 en 3 kunnen richting geven aan verder onderzoek naar verhoogd risico op plotselinge hartstilstand bij mensen met epilepsie of schizofrenie. Het is denkbaar dat varianten in genen die zowel in het hart als in de hersenen tot expressie komen kunnen leiden tot enerzijds epilepsie of schizofrenie en anderzijds tot een aangeboren gevoeligheid voor cardiale hartritmestoornissen. Dit zou mogelijk de associatie van deze ziekten met afwijkingen op het ECG en een verhoogd risico op plotselinge hartdood kunnen verklaren. Hoewel het gebruik van de natriumkanaal blokkerende of QTc-verlengende geneesmiddelen onze ECG- bevindingen niet verklaren, achten we het wel zeer waarschijnlijk dat het gebruik van medicatie en (cardiale) co-morbiditeit het risico op plotselinge hartdood in beide patiëntengroepen aanzienlijk kan beïnvloeden. Onze bevindingen suggereren dat het maken van een ECG als onderdeel van een periodieke cardiovasculaire screening bij te zijn met het voorschrijven van medicatie die van invloed is op het natriumkanaal of het QTc-interval (epilepsie) om het risico op plotselinge hartdood te minimaliseren.

Hoofdstuk 4 presenteert een studie naar de effecten van laag gedoseerde haloperidol op het QTc-interval in een ziekenhuispopulatie. Haloperidol heeft QTc-interval verlengende effecten door blokkade van het cardiale kaliumkanaal. Het effect van haloperidol in patiënten met multipele co-morbiditeit is minder goed bekend. Het doel van deze studie was om de veranderingen in QTc-duur tijdens een lage dosis haloperidol te in kaart te brengen, en om associaties tussen klinische variabelen en potentieel gevaarlijke QTc-verlenging te evalueren. In een retrospectief cohortonderzoek en 2007 haloperidol kregen toegediend. Voor deze studie hebben we de patienten geincludieerd waarvoor ECGs van voor, tijdens en na haloperidolgebruik beschikbaar waren (n=97). QTc-duur werd voor, tijdens en na haloperidolgebruik gemeten. Klinische variabelen voor haloperidolgebruik (nulmeting) en tijdens elke volgende ECG registratie werden verkregen uit de patiëntendossiers. We vonden dat QTc-verlenging bij een lage dosering haloperidol vooral voor kwam bij patiënten met een normale QTc-duur bij de nulmeting. Omgekeerd zagen we een onverwachte QTc-verkorting optreden bij de meeste patiënten met een borderline of abnormale QTc-duur tijdens de nulmeting. Bovendien had 94% van de patiënten met een potentieel gevaarlijke QTc- duur tijdens haloperidolgebruik bij aanvang een QTc-duur binnen normale waarden. Een toename in QTc-duur tot potentieel gevaarlijk niveau (stijging >50 ms of >500 ms) was naast QTc-duur bij nulmeting ook geassocieerd met een operatie en tekenen van ontsteking voor haloperidolgebruik, respectievelijk een verhoogd en een verlaagd risico. Hoewel patiënten met cardiovasculaire risicofactoren een hoger risico op QTc- met deze variabelen. In hoofdstuk 5 hebben we dit onderwerp verder onderzocht in oudere patiënten met meerdere co-morbiditeiten die in het ziekenhuis waren opgenomen voor een heupoperatie. Haloperidol wordt vaak perioperatief voorgeschreven aan patiënten met een heupfractuur. Aangezien de studie gepresenteerd in hoofdstuk 4 een operatie getracht om vast te stellen (1) hoe QTc-duur perioperatief verandert, (2) of een lage dosis haloperidol invloed heeft op deze veranderingen, en (3) welke klinische variabelen worden geassocieerd met potentieel gevaarlijke perioperatieve QTc-verlenging. In deze studie zijn 89 patiënten geïncludeerd (gemiddelde leeftijd 84 jaar, 24% mannen), waarvan 39 werden behandeld met haloperidol. Van deze patiënten zijn de ECGs vóór, tijdens en na heupoperatie geanalyseerd. Ook hier vonden we dat de (perioperatieve) QTc- duur gedifferentieerd verandert. We observeerden hetzelfde patroon als beschreven in hoofdstuk 4, met aanzienlijke QTc-verlenging voornamelijk bij patiënten met normale QTc-duur tijdens nulmeting (preoperatief), en QTc-verkorting bij de meeste patiënten met abnormale QTc-duur tijdens nulmeting. Veranderingen in de perioperatieve QTc- duur werden niet beïnvloed door haloperidolgebruik of één van de andere gemeten risicofactoren. Vanzelfsprekend achten we het onwaarschijnlijk dat QTc-verkorting bij patiënten met een verlengd QTc-interval vóór haloperidolgebruik veroorzaakt worden door de effecten van haloperidol zelf. Een meer plausibele verklaring zou kunnen zijn dat veranderingen in de onderliggende aandoening is opgetreden tijdens haloperidolgebruik, en dat deze veranderingen de QTc-duur verkorten, ondanks het haloperidolgebruik. De

geruststellende resultaten gepresenteerd in deze studies dienen eerst bevestigd te worden in andere populaties, voordat de aanbeveling om voorafgaand aan haloperidolgebruik een ECG te maken als standaard kan worden weggelaten in de ziekenhuiszorg. We adviseren steeds het patiëntendossier te raadplegen voor mogelijke risicofactoren voor QTc-verlenging bij het voorschrijven van haloperidol.

Het tweede deel van dit proefschrift beschrijft studies die zijn uitgevoerd met gegevens van de ARREST studie, en richt zich op niet-cardiale risicofactoren voor plotselinge hartstilstand in de algemene bevolking. Hoofdstuk 6 beschrijft de rationale en opzet van het deel van ARREST dat werd opgericht om genetische, klinische en farmacologische determinanten van plotselinge hartstilstand buiten het ziekenhuis te bestuderen, met voorbeelden van studies die met deze gegevens mogelijk zijn.

Hoofdstuk 7 geeft een overzicht van het cardiale natriumkanaal en daaraan gerelateerde erfelijke elektrofysiologische stoornissen, en geeft een overzicht van de betreffende farmacotherapie. Het cardiale natriumkanaal speelt een centrale rol in de voortgeleiding van de elektrische activiteit door het hart. De functie van dit ionkanaal kan door van geneesmiddelen die voor niet-cardiale ziekten worden voorgeschreven, zoals antidepressiva en anti-epileptica.

Hoofdstuk 8 beschrijft een studie naar de associatie van nortriptyline-gebruik en het risico op plotselinge hartstilstand, en mogelijke onderliggende mechanismen. In deze studie bestudeerden we de QRS-duur op het ECG tijdens rust/training in een patiënt met een ventriculaire tachycardie tijdens het sporten terwijl hij nortriptyline gebruikte. We vergeleken deze met de QRS-duur van 55 controles met/zonder nortriptyline en 24 controles met Brugada syndroom zonder nortriptyline, die een SCN5A-mutatie hebben. Moleculair-genetische (exon-trapping) en functionele (patch-clamp) experimenten lieten de mechanismen van de QRS-verlenging door nortriptyline en de SCN5A-mutatie van de patiënt zien. Tenslotte voerden we een prospectief onderzoek uit onder 944 patiënten met plotselinge hartstilstand buiten het ziekenhuis ten gevolge van VT/VF (ARREST) en 4354 gematchte controles uit de algemene bevolking (PHARMO) om het risico van nortriptyline-gebruik voor plotselinge hartstilstand buiten het ziekenhuis vast te stellen. We laten zien dat farmacologische (nortriptyline), genetische (loss-of-function SCN5A- mutatie), en/of functionele (natriumkanaal inactivatie bij hoge hartslag) factoren samen de cardiale natriumstroom beperken en daarmee het risico voor plotselinge hartstilstand verhogen. Nortriptyline-gebruik was geassocieerd met een 4,5-voudige toename van het risico voor plotselinge hartstilstand buiten het ziekenhuis. Nortriptyline verhoogt het risico op plotselinge hartstilstand, vooral in de aanwezigheid van factoren die cardiale exciteerbaarheid verlagen. Bij het voorschrijven van natriumkanaal blokkerende geneesmiddelen als nortriptyline wordt aanbevolen om rekening te houden met andere factoren die op de natriumstroom van invloed kunnen zijn (bijvoorbeeld verlengde QRS-duur op het ECG, comedicatie, ischemie en hartfalen, hoge hartslag), analoog aan de aanbevelingen voor QT-verlengende medicatie.

Hoofdstuk 9 beschrijft een case-control studie in de algemene bevolking met als doel OPD) een verhoogd risico op plotselinge hartstilstand buiten het ziekenhuis als gevolg van VT/VF hebben, en of dit risico wordt gemedieerd door een verhoogd cardiovasculaire met plotselinge hartstilstand (ARREST) ouder dan 40 jaar, en 5793 gematchte controles (PHARMO), en vonden een hoger risico van plotselinge hartstilstand bij patiënten met

OPD dan in die zonder OPD (15% bij cases, 11% bij controles, ORadj 1,4). Bij OPD- met plotselinge hartstilstand (OR 3,5) dan bij patiënten met een laag cardiovasculair voorgeschreven (ORSABAAC 2,7, vergeleken met patiënten zonder OPD). Onze patiënten. Wij raden een goed geïntegreerde pulmonale en cardiovasculaire zorg aan bij patiënten met OPD.

Het laatste deel van dit proefschrift presenteert drie cohort studies naar de uitkomst van reanimaties na een plotselinge hartstilstand buiten het ziekenhuis, en (niet-cardiale) determinanten van die uitkomst. De variabiliteit in de overlevingskansen na een reanimatie is grotendeels toe te schrijven aan verschillen in de zogenaamde keten van overleving: locatie van de hartstilstand, patiëntkarakteristieken kunnen ook een belangrijke rol spelen. Hoofdstuk 10 beschrijft een cohortstudie in de algemene bevolking van 1172 patiënten met een plotselinge hartstilstand buiten het ziekenhuis als gevolg van VT/VF tussen 2005 en 2008. We hebben onderzocht of patiënten met OPD een lagere overlevingskans na een plotselinge harststilstand hebben dan patiënten zonder OPD. We vergeleken de overleving tot

spoedeisende hulp (SEH), tot ziekenhuisopname, tot ontslag uit het ziekenhuis, en op 30 dagen na de reanimatie tussen OPD-patiënten en patiënten zonder OPD. OPD- patiënten (n=178) en patiënten zonder OPD (n=994) hadden vergelijkbare overleving tot SEH (75% vs. 78%) en tot ziekenhuisopname (56% vs. 57%). Echter, de overleving Onder patiënten die in het ziekenhuis werden opgenomen na de reanimatie was OPD een onafhankelijke determinant van verminderde 30-daagse overleving (39% vs. 59%,

ORadj 0,6). Onze resultaten suggereren dat de postreanimatiezorg in het ziekenhuis bij OPD-patiënten mogelijk aanpassing behoeft. Nauwkeurige monitoring van deze patiënten kan inzicht geven in de pathofysiologische basis van het geobserveerde verschil in overlevingskansen.

Hoofdstuk 11 beschrijft een studie naar verschillen in de toegang tot reanimatiezorg en naar de uitkomst van een reanimatiepoging na een plotselinge hartstilstand buiten het ziekenhuis tussen vrouwen en mannen. We voerden een cohortstudie uit onder de algemene bevolking ouder dan 19 jaar in Noord-Holland tussen 2006-2012 en cardiale oorzaak buiten het ziekenhuis (via Centraal Bureau Statistiek), en alle door spoedeisende hulpdiensten uitgevoerde cardiale reanimatiepogingen na plotselinge plotselinge hartstilstand (47,2% vrouwen), en registreerden 6.038 reanimatiepogingen lagere kans op een ondernomen reanimatiepoging dan mannen (OR 0,6). Neurologisch OR 0,6). Het verschil in overleving tussen mannen en vrouwen kan worden verklaard 0,6). Dit verschil in initieel ritme wordt niet verklaard door een langer tijdsinterval of door andere reanimatiekenmerken. Daarnaast hadden vrouwen minder kans op omstanderreanimatie, zelfs als er een getuige was van de plotselinge hartstilstand. We concluderen dat vrouwen minder kans op een reanimatiepoging hebben dan mannen, door zowel omstanders als door spoedeisende hulpdiensten. Wanneer bij vrouwen een reanimatiepoging wordt ondernomen, hebben ze een lagere kans om te overleven. Dit verschil is geslacht gerelateerd en kan slechts ten dele worden verklaard door reanimatiekenmerken. We adviseren een betere bewustwording van plotselinge hartstilstand onder vrouwen, om een goede herkenning te verbeteren en de toegang tot reanimatiezorg te verhogen.

In hoofdstuk 12 onderzoeken we of er temporele trends kunnen worden onderscheiden in de neurologisch intacte overleving na een plotselinge hartstilstand buiten het ziekenhuis, en, zo ja, of een verandering in de overleving is toe te schrijven aan toegenomen gebruik van de AED. In verschillende gebieden zijn interventies geïntroduceerd ter verbetering een plotselinge hartstilstand te verhogen. In Nederland is het beleid in de eerste plaats gericht op het verminderen van de tijd tot de eerste schok door meer AED gebruik publiekelijk toegankelijk zijn. We deden een cohortstudie in Noord-Holland tussen 2006 en 2012, en includeerden alle patiënten met een plotselinge hartstilstand buiten het ziekenhuis met cardiale oorzaak. De kans op (neurologisch intacte) overleving is schokbaar initieel ritme (29,1% tot 41,4%). De kans op overleving is gestegen in elke percentage patiënten met gunstige neurologische uitkomst bleef hoog gedurende de onderzoeksperiode (89,9% tot 95,1%). Het AED-gebruik is bijna een verdrievoudigd tijdens de onderzoeksperiode (21,4% tot 59,3%), waardoor de tijd tussen alarmering (9,2% tot 21,7). De toename in AED-gebruik verklaard statistisch de verhoogde neurologisch intacte overleving. We concluderen dat toegenomen AED-gebruik is geassocieerd met gestegen overleving van patiënten met een schokbaar initieel ritme. We adviseren blijvende aandacht voor verbeteringen in reanimatiezorg, met een sterke leken ter plaatse.

Dankwoord

Promoveren doe je niet alleen:

Beste Arthur, dank voor je hulp bij het afronden van mijn proefschrift, en je steeds zo snelle reacties. Beste Ton, dank voor al je inbreng in de opstartfase van ARREST 11, en je steeds beschikbare raad en advies tijdens het hele proces dat daarop volgde.

Beste Hanno, dank voor alle inspiratie en vertrouwen. Van begin af aan gaf je me alle kansen en alle ruimte die ik me maar kon wensen. De grote mate van zelfstandigheid die jij je aio’s gunt waardeer ik zeer.

Prof. dr. A.P.M. Gorgels, prof. dr. L. de Haan, prof. dr. H.G.M. Leufkens, prof. dr. S.E.J.A. de Rooij, prof. dr. M.C.J.M. Sturkenboom, prof. dr. A.H. Zwinderman: dank voor jullie bereidheid zitting te nemen in mijn commissie, en voor de tijd die jullie steken in het beoordelen van dit proefschrift.

Abdennasser en Jocelyn, allerbeste (oud)collega-ARREST-aio’s: zonder jullie was ik niet aan ARREST begonnen, zonder jullie was dit proefschrift niet geschreven. Dank voor de baan, de super samenwerking, de vriendschap. Ik hoop nog veel in team B, B & B verband te ondernemen!

Dr. R.W. Koster, beste Ruud: dank voor de goede samenwerking met de ARREST 11 tak binnen alle andere ARREST projecten. Je bezielende leiding aan ARREST is heel bijzonder. Dank (oud)datamanagers: Loes Bekkers, Esther Landman, Renate van der Meer, Remy Stieglis. Met zo’n team lukt alles. Jullie inzet is van immens belang. Dank Anne Spanjaart, Jolande Zijlstra. Samen onderzoek doen is zoveel leuker dan alleen! En natuurlijk dank, Paulien Homma, voor de bewonderingswaardige manier waarop je de dataverzameling van ARREST in goede banen weet te leiden.

Dank aan alle medewerkers van de meldkamers Amsterdam, Haarlem en Noord- Holland Noord en Utrecht. Dank aan de ambulancediensten en het ambulance personeel van Ambulance Amsterdam, GGD Kennemerland, RAVU, Veiligheidsregio Noord-Holland Noord Ambulancezorg en Witte Kruis. Zonder deze mensen geen ARREST-onderzoek. Dank ook aan alle medewerkers van spoedeisende hulp, afdeling cardiologie en intensive care, en klinische labs van de participerende ziekenhuizen (AMC, Boven IJ Ziekenhuis, Gemini Ziekenhuis, Kennemer Gasthuis, Meander Medisch Centrum, Medisch Centrum Alkmaar, OLVG, Rode Kruis Ziekenhuis, Sint Lucas Andreas Ziekenhuis, Slotervaart Ziekenhuis, Spaarne Ziekenhuis, UMC Utrecht, VUmc, Waterland Ziekenhuis, Westfriesgasthuis, Zaans Medisch Centrum). Dank aan alle politie- en brandweerdiensten die meewerken aan de verzameling van AED-gegevens. En dank aan alle apothekers en huisartsen en hun medewerkers voor de belangeloze medewerking aan het ARREST onderzoek. Dank aan Jet Bliek en Jessica Glebbeek voor al jullie werk met onze DNA-samples.

Dank aan alle studenten die hebben meegeholpen aan de dataverzameling van ARREST, als DNA-student, AED-student, of DNA/AED-student: dank voor jullie inzet en nachtrust. Dank aan mijn stagiairs die als volwaardige medewerkers meedraaiden in de verschillende Emine Eke, Paul de Jong, Helene Lichtenberg, Ashni Lo-Kim-Lin, Jelte Meulenaar, Mei-Ing Nieuwland. Ook aan alle stagiairs die ik niet persoonlijk begeleidde: dank.

Dank oude kamergenoten (Geert Boink, Mio Campian, Angela Engel, Maarten van den Hoogenband, Mark Hoogendijk, Elizabeth Lodder, Kelly Pilichou, Sameer Rana, Ingrid van Rijsingen, Brendon Scicluna, Alexander Sizarov, Jan Zegers) en huidige kamergenoten (Abdelaziz Beqqali, Hanneke van Deutekom, Daniel van Hoeijen, Joeri Jansweijer, Ralph van Oort) voor het delen in jullie kennis en de vier uur humor. alle coauteurs voor de goede samenwerking. Dank Patrick Souverein voor je hulp in de data-jungle, dank Michael Tanck en Nan van Geloven voor jullie raad en advies op methodologisch en statistisch gebied. En natuurlijk dank aan alle mensen van de afdeling experimentele cardiologie. Dankwoord

Lieve familie en vrienden, dank voor alles. Lieve Edith en Hans, dank voor jullie inspirerende aandacht en onvoorwaardelijk steun, voor mij en voor de meiden. Lieve Dick en Laury, dank voor de aangeboren aanleg voor epidemiologie, het vertrouwen dat je me hebt gegeven, en dank voor al die opa- dinsdagen. Lieve Edy, liefste zus van de wereld, of je nu in Amsterdam of in Burundi woont: je bent er altijd, dank voor alles. Lieve schoonfamilie: lieve Frank en Trees, Dick en Franca, Duco en Eke: dank voor al jullie hulp. Lieve Els, lieve Danielle, dank. Lieve Joline, lieve Lucie, dank voor alle liefde en lol. Jullie zijn het mooiste van alles. Liefste Frank, mijn lief: al mijn dank.

Blom MT*, Beesems SG*, Homma PC, Zijlstra JA, Hulleman M, van Hoeijen DA, Bardai A, Tijssen JGP, Tan HL, Koster RW. Improved survival after out-of-hospital cardiac arrest and use of Automated External Circulation. 2014 Accepted.

Blom MT*, Jansen S*, De Jonghe A, van Munster BC, de Boer A, de Rooij SE, Tan HL, van der Velde N. In-hospital Haloperidol use and perioperative changes in QTc-duration. Journal of Nutrition, Health and Aging. 2014 Accepted.

Blom MT, van Hoeijen DA, Bardai A, Berdowski J, Souverein PC, de Bruin ML, Koster RW, de Boer A, Tan HL. Genetic, clinical and pharmacological determinants of out-of-hospital cardiac arrest: rationale and outline of the Amsterdam Resuscitation Studies (ARREST) registry. Open Heart 2014 In press.

Bardai A, Blom MT, van Noord C, Verhamme, KM, Sturkenboom MCJM, Tan HL. Sudden cardiac death is associated both with epilepsy and with use of antiepileptic medications. Heart 2014 In press.

Van Hoeijen DA*, Blom MT*, Tan HL. Cardiac sodium channels and inherited electrophysiological disorders: an update on the pharmacotherapy. Expert Opin Pharmacother. 2014 Jul 3:1-13.

Lamberts RJ, Blom MT, Novy J, Belluzzo M, Seldenrijk A, Penninx BW, Sander JW, Tan HL, Thijs RD. Increased prevalence of ECG markers for sudden cardiac arrest in refractory epilepsy. J Neurol Neurosurg Psychiatry. 2014 Jun 19. pii: jnnp-2014-307772.

Blom MT, Cohen D, Seldenrijk A, Penninx BW, Nijpels G, Stehouwer CD, Dekker JM, Tan HL. Brugada syndrome ECG is highly prevalent in schizophrenia. Circ Arrhythm Electrophysiol.

Berdowski J, de Beus MF, Blom MT, Bardai A, Bots ML, Doevendans PA, Grobbee DE, Tan HL, Tijssen JG, Koster RW, Mosterd A. Exercise-related out-of-hospital cardiac arrest in the general population: incidence and prognosis. Eur Heart J

Veerman CC*, Verkerk AO*, Blom MT, Klemens CA, Langendijk PNJ, Antoni van Ginneken CG, Wilders R, Tan HL. induced long QT syndrome. Circ Arrhythm Electrophysiol.

Warnier MJ*, Blom MT*, Bardai A, Berdowksi J, Souverein PC, Hoes AW, Rutten FH, de Boer A, Koster RW, De Bruin ML, Tan HL. Increased risk of sudden cardiac arrest in obstructive pulmonary disease: a case-control study. PLoS One.

Bardai A*, Amin AS*, Blom MT*, Bezzina CR, Berdowski J, Langendijk PN, Beekman L, Klemens CA, Souverein PC, Koster RW, de Boer A, Tan HL. Sudden cardiac arrest associated with use of a non-cardiac drug that reduces cardiac excitability: evidence from bench, bedside, and community. Eur Heart J. 2013

Blom MT*, Warnier MJ*, Bardai A, Berdowski J, Koster RW, Souverein PC, Hoes AW, Rutten FH, de Boer A, De Bruin ML, Tan HL. Reduced in-hospital survival rates of out-of-hospital cardiac arrest victims with obstructive pulmonary disease. Resuscitation.

Bardai A, Lamberts RJ, Blom MT, Spanjaart AM, Berdowski J, van der Staal SR, Brouwer HJ, Koster RW, Sander JW, Thijs RD, Tan HL. Epilepsy is a risk factor for sudden cardiac arrest in the general population. PLoS One. van Hoorn F, Campian ME, Spijkerboer A, Blom MT, Planken RN, van Rossum AC, de Bakker JM, Wilde AA, Groenink M, Tan HL. SCN5A mutations in Brugada syndrome are associated with increased cardiac dimensions and reduced contractility. PLoS One

Hulleman M, Berdowski J, de Groot JR, van Dessel PF, Borleffs CJ, Blom MT, Bardai A, de Cock CC, Tan HL, Tijssen JG, Koster RW. for out-of-hospital cardiac arrest caused by lethal arrhythmias. Circulation. 2012 Aug

Berdowski J, Blom MT, Bardai A, Tan HL, Tijssen JG, Koster RW. of-hospital cardiac arrest. Circulation.

Blom MT, Bardai A, van Munster BC, Nieuwland MI, de Jong H, van Hoeijen DA, Spanjaart AM, de Boer A, de Rooij SE, Tan HL. Differential changes in QTc duration during in-hospital haloperidol use. PLoS One. Arking DE*, Junttila MJ*, Goyette P*, Huertas-Vazquez A*, Eijgelsheim M*, Blom MT*, Newton-Cheh C*, Reinier K, Teodorescu C, Uy-Evanado A, Carter-Monroe N, Kaikkonen KS, Kortelainen ML, Boucher G, Lagacé C, Moes A, Zhao X, Kolodgie F, Rivadeneira F, Hofman A, Witteman JC, Uitterlinden AG, Marsman RF, Pazoki R, Bardai A, Koster RW, Dehghan A, Hwang SJ, Bhatnagar P, Post W, Hilton G, Prineas RJ, Li M, Köttgen A, Ehret G, Boerwinkle E, Coresh J, Kao WH, Psaty BM, Tomaselli GF, Sotoodehnia N, Siscovick DS, Burke GL, Marbán E, Spooner PM, Cupples LA, Jui J, Gunson K, Kesäniemi YA, Wilde AA, Tardif JC, O’Donnell CJ, Bezzina CR, Virmani R, Stricker BH, Tan HL, Albert CM, Chakravarti A, Rioux JD, Huikuri HV, Chugh SS. wide association in European ancestry individuals. PLoS Genet.

Bardai A*, Berdowski J*, van der Werf C*, Blom MT, Ceelen M, van Langen IM, Tijssen JG, Wilde AA, Koster RW, Tan HL. Incidence, causes, and outcomes of out-of-hospital cardiac arrest in children. A comprehensive, prospective, population-based study in the Netherlands. J Am Coll Cardiol

Bezzina CR*, Pazoki R*, Bardai A*, Marsman RF*, de Jong JS*, Blom MT, Scicluna BP, Jukema JW, Bindraban NR, Lichtner P, Pfeufer A, Bishopric NH, Roden DM, Meitinger T, Chugh SS, Myerburg RJ, Jouven X, Kääb S, Dekker LR, Tan HL, Tanck MW, Wilde AA. Nat Genet.

Smits JP, Blom MT, Wilde AA, Tan HL. Cardiac sodium channels and inherited electrophysiologic disorders: a pharmacogenetic overview. Expert Opin Pharmacother

Hoeijen DA, Blom MT, Bardai A, de Boer A, Souverein PC, Koster RW, Tan HL. Reduced pre-hospital survival rate after out-of-hospital cardiac arrest of patients with diabetes mellitus type 2: a prospective community-based study.

Bardai A, Blom MT, van Hoeijen DA, van Deutekom HW, Brouwer HJ, Tan HL. population-based case-control study.

Blom MT, Berdowski J, Bardai A, Tan HL. Women are less likely to be resuscitated during out-of-hospital cardiac arrest than men.

* These authors contributed equally

PhD student: M.T. Blom Co-supervisor: Dr. H.L. Tan PhD period: February 2007 – September 2014

ECTS

- 2012 2.1 - 2011 0.4 - 2010 1.1 - 2010 0.9 - Advanced Topics in Clinical Epidemiology 2009 1.1 - 2007 1.1

- Weekly department seminars 2007 - 16 2014

- 2013 0.5 - 2013 0.5 - 2012 0.5 - 2012 0.5 - 2011 0.5 - 2009 0.5 - 2008 0.5

ECTS

- 2008 - 2013 3 Medical students

- 2013 2 - 2011 2 - 2010 2 - 2010 2 - 2009 2 - 2009 2 - 2009 2 - 2009 2 - 2008 2 - 2008 2 - 2008 1 n

- 2012

Born in Senanga, Zambia on 25 October 1974.

2006 – 2011 Epidemiology, Master, VU medical Center, EMGO Institute.

1995 - 2002 Human Geography, Master, University of Amsterdam

1994 - 1995 Dutch Language and Literature, University of Amsterdam Obtained propedeuse degree

February 2007 – present Research fellow at Department of Experimental Cardiology, AMC. Subject: Sudden cardiac arrest (risk factors and outcome) Supervision: Prof A.A. Wilde, Prof A. de Boer, Dr H.L. Tan

Januar Junior Consultant at ETC Adviesgroep Nederland

January 2002 – December 2004 Management assistant at ETC International, Department of International Health.