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The Work Presented in This Thesis Was Conducted at the Department of Medical Informatics of the Erasmus Medical Center, Rotterdam, the Netherlands

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The Work Presented in This Thesis Was Conducted at the Department of Medical Informatics of the Erasmus Medical Center, Rotterdam, the Netherlands The work presented in this thesis was conducted at the department of Medical Informatics of the Erasmus Medical Center, Rotterdam, the Netherlands The contribution of all of the participants and staff of the Rotterdam Study is gratefully acknowledged. All the general practitioners and pharmacists of the Ommoord district are gratefully acknowledged for their help with data collection and validation. The work in this thesis is supported by grants from the Netherlands Organisation for Health Research and Development (ZonMw; Priority Medicines Elderly 113102005). The Rotterdam Study is supported by the Erasmus MC and Erasmus University Rotterdam; the Netherlands Organisation for Scientific Research (NWO); the Netherlands Organisation for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Netherlands Genomics Initiative (NGI); the Ministry of Education, Culture, and Science; the Ministry of Health, Welfare, and Sport; the European Commission (DG XII); and the Municipality of Rotterdam. The generation and management of GWAS genotype data for the Rotterdam Study is supported by the Netherlands Organisation for Scientific Research (NWO) Investments (nr. 175.010.2005.011, 911-03-012). This stuy is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI) / the Netherlands Organisation for Scientific Research (NWO) project nr. 050-060-810. Financial support for the publication of this thesis was kindly provided by the Department of Medical Informatics of the Erasmus Medical Center, Rotterdam, the Netherlands and the Erasmus University Medical Center Rotterdam. Cover image and lay-out: Nastasia Griffioen Printed by: GVO Drukkers & Vormgevers – www.proefschriften.nl ©Marten van den Berg, 2017 ISBN: 978-94-6332-221-8 For articles published or accepted for publication, the copyright has been transferred to the respective publisher. No part of this thesis may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the permission of the author, or, when appropriate, from the publishers of the manustript. QT Variability and Other Electrocardiographic Predictors of Sudden Cardiac Death Marten Enne van den Berg QT Variability and Other Electrocardiographic Predictors of Sudden Cardiac Death QT variabiliteit en andere electrocardiografische voorspellers van plotse hartdood Thesis to obtain the degree of Doctor from the Erasmus University Rotterdam by command of the rector magnificus Prof.dr. H.A.P. Pols and in accordance with the decision of the Doctorate Board. The public defense shall be held on Wednesday October 4, 2017 at 15:30 hours by Marten Enne van den Berg born in Leeuwarden Doctoral Committee Promotor Prof.dr. B.H.C. Stricker Other members Prof.dr. J.W. Deckers Prof.dr. O.H. Franco Duran Prof.dr. P. van der Harst Copromotors Dr. P.R. Rijnbeek Dr. M. Eijgelsheim Table of Contents Manuscripts upon which this thesis is based 7 General introduction 11 Part I Commonly used electrocardiographic markers for sudden cardiac death Chapter 1.1 Normal values of heart-rate variability in the standard 10-second 27 electrocardiogram for all ages Chapter 1.2 Antidepressants and heart-rate variability in older adults: a 47 population-based study Chapter 1.3 Assessing Prolongation of the Heart Rate Corrected QT Interval in 63 Users of Tricyclic Antidepressants: Advice to Use Fridericia Rather Than Bazett’s Correction Chapter 1.4 Discovery of novel heart rate-associated loci using the Exome Chip 79 Part II QT variability Chapter 2.1 Short-term QT variability markers for the prediction of ventricular 111 arrhythmias and sudden cardiac death: A systematic review Chapter 2.2 Validation of automatic measurement of QT interval variability 143 Chapter 2.3 Normal values of QT variability in the standard 10-second 159 electrocardiogram for all ages Chapter 2.4 QT variability as risk factor for total mortality and sudden cardiac 179 death in a population-based cohort study Chapter 2.5 Additional value of electrocardiographic markers for predicting 197 sudden cardiac death in the middle-aged and elderly Chapter 2.6 QT variability is associated with heart failure in a middle-aged and 217 elderly population-based cohort: the Rotterdam Study Chapter 2.7 Does thyroid function affect QT variability? A population-based study 235 5 Part III Risk factors for sudden cardiac death not based on the electrocardiogram Chapter 3.1 Chronic obstructive pulmonary disease and sudden cardiac death: A 261 systematic review. Chapter 3.2 Thyroid function and Sudden Cardiac Death: a Prospective 287 Population-based Cohort Study General discussion 316 Summary 329 Nederlandse Samenvatting 335 Dankwoord 343 Portfolio 347 About the author 349 6 Manuscripts upon which this Thesis is Based Chapter 1.1 Van den Berg ME, Rijnbeek PR, Niemeijer MN, Hofman A, Van Herpen G, Bots ML, Hillege H, Swenne CA, Eijgelsheim M, Stricker BH, Kors JA. Normal values of heart- rate corrected heart-rate variability in 10-second electrocardiograms for all ages. Submitted Chapter 1.2 Noordam R, Van den Berg ME, Niemeijer MN, Aarts N, Hofman A, Tiemeier H, Kors JA, Stricker BH, Eijgelsheim M, Visser LE Rijnbeek PR Antidepressants and heart-rate variability in older adults: a population-based study. Psychol Med. 2016 Apr;46(6):1239-47. Chapter 1.3 Noordam R, Van den Berg ME, Niemeijer MN, Aarts N, Leening MJ, Deckers JW, Hofman A, Rijnbeek PR, Eijgelsheim M, Kors JA, Stricker BH, Visser LE. Assessing Prolongation of the Heart Rate Corrected QT Interval in Users of Tricyclic Antidepressants: Advice to Use Fridericia Rather Than Bazett's Correction. J Clin Psychopharmacol. 2015 Jun;35(3):260-5. Chapter 1.4 Van den Berg ME, Warren HR, Cabrera CP, Verweij N, Mifsud B, Haessler J, Bihlmeyer NA, Fu YP, Weiss S, Lin HJ, Grarup N, Li-Gao R, Pistis G, Shah N, Brody JA, Müller-Nurasyid M, Lin H, Mei H, Smith AV, Lyytikäinen LP, Hall LM, van Setten J, Trompet S, Prins BP, Isaacs A, Radmanesh F, Marten J, Entwistle A, Kors JA, Silva CT, Alonso A, Bis JC, de Boer R, de Haan HG, de Mutsert R, Dedoussis G, Dominiczak AF, Doney AS, Ellinor PT, Eppinga RN, Felix SB, Guo X, Hagemeijer Y, Hansen T, Harris TB, Heckbert SR, Huang PL, Hwang SJ, Kähönen M, Kanters JK, Kolcic I, Launer LJ, Li M, Yao J, Linneberg A, Liu S, Macfarlane PW, Mangino M, Morris AD, Mulas A, Murray AD, Nelson CP, Orrú M, Padmanabhan S, Peters A, Porteous DJ, Poulter N, Psaty BM, Qi L, Raitakari OT, Rivadeneira F, Roselli C, Rudan I, Sattar N, Sever P, Sinner MF, Soliman EZ, Spector TD, Stanton AV, Stirrups KE, Taylor KD, Tobin MD, Uitterlinden A, Vaartjes I, Hoes AW, van der Meer P, Völker U, Waldenberger M, Xie Z, Zoledziewska M, Tinker A, Polasek O, Rosand J, Jamshidi Y, van Duijn CM, Zeggini E, Wouter Jukema J, Asselbergs FW, Samani NJ, Lehtimäki T, Gudnason V, Wilson J, Lubitz SA, Kääb S, Sotoodehnia N, Caulfield MJ, Palmer CN, Sanna S, Mook-Kanamori DO, Deloukas P, Pedersen O, Rotter JI, Dörr M, O'Donnell CJ, Hayward C, Arking DE, Kooperberg C, van der Harst P, Eijgelsheim M, Stricker BH, Munroe PB. Discovery of novel heart rate-associated loci using the Exome Chip. Hum Mol Genet. 2017 Apr 3. [Epub ahead of print] 7 Chapter 2.1 Niemeijer MN, Van den Berg ME, Eijgelsheim M, van Herpen G, Stricker BH, Kors JA, Rijnbeek PR. Short-term QT variability markers for the prediction of ventricular arrhythmias and sudden cardiac death: a systematic review. Heart. 2014 Dec;100(23):1831-6. Chapter 2.2 Rijnbeek PR, Van den Berg ME, van Herpen G, Ritsema van Eck HJ, Kors JA. Validation of automatic measurement of QT interval variability. Rijnbeek PR, van den Berg ME, van Herpen G, Ritsema van Eck HJ, Kors JA. PLoS One. 2017 Apr 12;12(4):e0175087. Chapter 2.3 Van den Berg ME, Kors JA, Niemeijer MN, Van Herpen G, Bots ML, Hillege H, Swenne CA, Eijgelsheim M, Stricker BH, Rijnbeek PR. Normal values of QT variability in 10- second electrocardiograms for all ages. Submitted Chapter 2.4 Van den Berg ME, Niemeijer MN, Deckers JW, Franco OH, Hofman A, Van Herpen G, Kors JA, Stricker BH, Eijgelsheim M, Rijnbeek PR. QT variability as risk factor for sudden cardiac death, cardiac mortality, and all-cause mortality. Submitted Chapter 2.5 Van den Berg ME, Niemeijer MN, Eijgelsheim M, Deckers JW, Hofman A, Nieboer D, Franco OH, Stricker BH, Kors JA, Rijnbeek PR. Additional value of electrocardiographic markers for predicting sudden cardiac death in the middle- aged and elderly. In preparation Chapter 2.6 Van den Berg ME, Niemeijer MN, Leening MJ, Deckers JW, Van Herpen G, Kors JA, Stricker BH, Eijgelsheim M, Rijnbeek PR. QT variability is associated with incident heart failure in a prospective population-based cohort study. In preparation Chapter 2.7 Van den Berg ME, Chaker L, Niemeijer MN, Kors JA, Eijgelsheim M, Rijnbeek PR, Peeters RP, Stricker BH. Does thyroid function affect QT variability? A population- based study. In preparation Chapter 3.1 Van den Berg ME, Stricker BH, Brusselle GG, Lahousse L. Chronic obstructive pulmonary disease and sudden cardiac death: A systematic review.Trends Cardiovasc Med. 2016 Oct;26(7):606-13. Chapter 3.2 Chaker L, Van den Berg ME, Niemeijer MN, Franco OH, Dehghan A, Hofman A, Rijnbeek PR, Deckers JW, Eijgelsheim M, Stricker BH, Peeters RP. Thyroid Function and Sudden Cardiac Death: A Prospective Population-Based Cohort Study. Circulation. 2016 Sep 6;134(10):713-22. 8 General Introduction 11 General Introduction Sudden cardiac death (SCD), the definition of which will be given later, comprises half of all cases of coronary heart disease (CHD) mortality,1 which in turn account for half of all deaths globally.2 Assessment of the risk of SCD in members of the general population is of great importance because about half of the cases of SCD occur in people without a previous history of cardiac disease, and rates of successful resuscitation remain low at about 8%).3 The electrocardiogram (ECG) is already being used as a tool to predict SCD,4 but it has not yet reached its full potential Further studying and combining currently used ECG markers and exploring the potential of new ones could aid in the prediction of SCD in the future.
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