Quantitative Electrocardiography for Prediction Of

Home , Afterload, Electrocardiography, Mitral valve replacement

QUANTITATIVE ELECTROCARDIOGRAPHY FOR PREDICTION OF

POSTOPERATIVE ATRIAL FIBRILLATION AFTER CARDIAC SURGERY

FLORIAN RADER, M.D.

Submitted in partial fulfillment of the requirements

For the degree of Master of Science

Clinical Research Scholars Program

CASE WESTERN RESERVE UNIVERSITY

January 2010

CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Florian Rader, M.D.

candidate for the Master of Science degree*.

(signed) Regis E. McFadden (chair of the committee)

Eugene H. Blackstone, M.D.

Ottorino Costantini, M.D.

Neal Dawson, M.D.

(date) 10-28-2009

*We also certify that written approval has been obtained for any proprietary material

contained therein.

Table of Contents

List of Tables 4

List of Figures 5

Acknowledgments 6

List of Abbreviations 7

Abstract 9

Text

Background and Significance 10

Specific aims 12

Methods 13

Results 21

Discussion 46

Limitations 54

Conclusions 54

References 55

List of Tables:

Table 1: Patient characteristics 24

Table 2: Clinical predictors of postoperative atrial fibrillation 37

Table 3: ECG predictors of postoperative atrial fibrillation 38

List of Figures:

Figure 1: Relationship of anatomic and ECG changes with POAF 11

Figure 2: Patient flow chart 22

Figure 3: Occurrence of POAF by days after surgery 33

Figure 4: Occurrence of POAF by surgery type 34

Figure 5: ROC curve of final prediction model 35

Figure 6: Calibration curve of final prediction model 36

Figure 7: Adjusted Co-plots of ECG predictors 39

Figure 8: Unadjusted and adjusted co-plot of P wave amplitude 41

Figure 9: Nomogram of final prediction model 42

Figure 10: Nomogram of prediction model with pre-op variables 43

Figure 11: Calibration curve of model without ECG predictors 45

Figure 12: Left atrial sizes by P wave amplitude in aVR 48

Figure 13: Correlation matrix (P wave amplitude, aVR, and left atrial volume) 49

Acknowledgments:

I thank Dr. Rajeswaran, staff statistician of the Department of Quantitative Health

Sciences at Cleveland Clinic for the continued support of my project. In addition, I want to thank my mentors, specifically Dr. Eugene Blackstone, Dr. Otto

Costantini and Dr. Neal Dawson for making themselves available and discussing this project in multiple hour-long sessions despite their busy schedules.

List of abbreviations:

ABDT = Angiotensin blocking drug therapy

ACE = Angiotensin converting enzyme

AF = Atrial fibrillation

ARB = Angiotensin II receptor blocker

AVR = Aortic valve replacement

BPM = beats per minute

BMI = Body mass index

BUN = Blood urea nitrogen

CA = Coronary artery

CABG = Coronary artery bypass graft surgery

CC = cubic centimeters

CM = Centimeter

COPD = Chronic obstructive pulmonary disease

CR = Creatinine

CVIR = Cardiovascular Information Registry

CVA = Cerebral vascular accident

DL = Deciliter

ECG = Electrocardiogram

FFP = Fresh frozen plasma g = gram

ICD = Implanted cardioverter-defibrillator

KG = Kilogram

LA = Left atrium

LAD = Left anterior descending coronary artery

LCX = Left circumflex coronary artery

LVH = Left ventricular hypertrophy m = meter mg = milligram ml = milliliter mmHg = millimeter mercury

µV = microvolt

MUSE = Marquette Universal System of Electrocardiography

MV = Mitral valve

MVR= Mitral valve replacement

OR = Odds ratio

POAF = Postoperative atrial fibrillation

RBC = Red blood cell

RCA = Right coronary artery

ROC = Receiver operating characteristics

Quantitative Electrocardiography for Prediction of Postoperative Atrial

Fibrillation after Cardiac Surgery

Abstract

FLORIAN RADER, M.D.

Background: Postoperative atrial fibrillation (POAF) after cardiac surgery is a common marker of poor outcomes and can be reduced with medical prophylaxis.

Methods: Quantitative ECG measurements and clinical patient characteristics predictive of POAF were identified with stepwise logistic regression and 500-fold bagging in a cohort of patients undergoing coronary artery bypass and/or valve surgery.

Results: 4762 (35%) of 13416 patients developed POAF. Independent ECG predictors were a less negative P wave amplitude in lead aVR (OR 1.46, CI 1.32-

1.61) and a larger P wave amplitude in lead V1 (OR 1.25, CI 1.16-1.36, per 0.1 mV). Greater age and left atrial volume, prior episodes of atrial arrhythmias, and valvular surgery were risk factors; black race and hypothyroidism were protective from POAF.

Conclusion: P wave amplitude in lead aVR and V1 are important predictors of postoperative atrial fibrillation after cardiac surgery and in combination with clinical predictors can guide prophylactic medication use.

Background and Significance:

Atrial fibrillation (AF) is the most common arrhythmia after cardiac surgery occurring in 16% to 53%(1,2) of patients with the highest incidence on day 2 after surgery(3). Postoperative AF (POAF) is associated with higher morbidity (i.e. acute myocardial infarction, stroke, worsening congestive heart failure, re- intubation), mortality, length of hospital stay and costs(4-7). In a study by Aranki et al. POAF after coronary bypass graft surgery (CABG) was associated with prolongation of hospital stay by 4.9 days and addition of $10,055 in hospital costs(4). Prophylactic medications such as amiodarone, beta blockers and sotalol(8-11) are available, but they bear the potential of significant adverse effects (i.e. bradycardia, hypotension) and should therefore only be used in patients at high risk.

The method of quantitative electrocardiography has been applied to predict mortality in left ventricular hypertrophy(12) and acute coronary syndrome(13,14).

Although the use of P wave analysis to predict POAF has been proposed, it remains controversial. Passman and colleagues used ECG measurements similar to ours and found P wave duration in V1 and PR interval to be predictive.

The study was limited by small sample size of 152 patients and adjustment for only few variables(15). Amar and colleagues reported P wave duration and PR interval the least discriminatory out of all independent predictors of POAF(16).

However, both studies showed a promising signal of quantitative ECG measurements being a useful component of preoperative risk assessment of postoperative supraventricular arrhythmias. Multiple studies demonstrated the

association of POAF with echocardiographic measures such as left atrial enlargement(1,17), presence of left ventricular hypertrophy(18,19) and decreased left ventricular ejection fraction(20) (Figure 1). ECG variables such as the P-wave, QRS-duration, and ECG criteria for left ventricular hypertrophy are almost always available (even in acute situations) prior to cardiac surgery, correlate well with the echocardiographic measurements(21) and are associated with little cost. Furthermore we postulate that ECG measurements may have diagnostic properties that go beyond the role of a surrogate of anatomical features, but may also mirror the electrolyte and autonomic nerve milieu, myocardial tissue inflammation, ischemia and fibrosis. A recent study from the

Framingham cohort identified PR interval as an independent predictor for development of non-operative atrial fibrillation(22).

Elevated left

ventricular Valve disease

Ischemia afterload

Mechanisms Left ventricular Left ventricular Elevated left Left atrial remodeling & Pathophysiologic remodeling hypertrophy atrial pressure enlargement

Prolongation of ECG indices of PR interval, P- wave QRS duration left ventricular morphology hypertrophy ECG Measures

POSTOPERATIVE ATRIAL FIBRILLATION

Figure 2: Proposed pathways of myocardial remodeling and associated ECG findings leading to postoperative atrial fibrillation

Specific Aims:

1. Specific aim #1: To describe the association of quantitative measurements of

P wave, PR interval and QRS complex, as surrogates for pathological

structural and electrical remodeling of the heart, with POAF.

2. Specific aim #2: To describe the association of clinical patient characteristics,

such as medical comorbidities, procedure related variables and

echocardiographic data, with POAF.

3. Specific aim #3: To create a prediction rule, which can identify patients who

are at high risk of developing POAF after cardiac surgery.

Methods:

1. Study Sample:

1.1. Inclusion criteria:

• Patients undergoing isolated CABG or isolated heart valve surgery or a

combination of these at Cleveland Clinic from 1997 to 2003 (n=14,258).

• Patients in sinus rhythm prior to surgery as determined by clinical history and

the last ECG prior to surgery.

1.2. Exclusion criteria:

• Patients in AF or atrial flutter at time of surgery (defined as having permanent

AF or AF/atrial flutter on their last ECG prior to surgery, n= 173).

• Patients with an implanted pacemaker or cardioverter-defibrillator (n=89).

• Patients with missing ECG variables due to corrupted ECG data (n=580).

Rationale for exclusion of patients: Patients in chronic AF could not be considered at risk for new onset POAF. To exclude all patients who are in AF prior to their surgery, AF on the last ECG (recorded no more than 30 days before surgery) indicated non-eligibility. Patients with an implanted pacemaker by history were excluded at time of database query and additional 89 patients had evidence of a pacemaker on their last ECG prior to surgery and were excluded due to the lack of interpretability of their ECG tracings (pacemakers can alter all elements of the ECG measurements of interest in an unpredictable fashion depending on the amount of pacing in the atrium and ventricle). Patients with missing ECG data caused by corrupted data files (surgery dates prior to 1/7/1991 and between 1/1993 and 7/1993) were excluded, because missingness can be assumed as completely at random (missingness is not related to clinical characteristics of patients). All other patients with missing ECG data were not excluded, because informative missingness is possible (patients whose ECG was not stored in the database, may have had a paper copy only in the chart.

These patients are more likely transfers from other hospitals and may be different in regards of baseline characteristics and the acuity of cardiac surgery. Patients on anti-arrhythmic medications were not excluded as prophylactic use of such medications in this cohort is unlikely as all surgery dates precede the first larger randomized trials of amiodarone and beta blockers for primary prophylaxis in cardiac surgery(8-10).

1.3. Data collection:

Clinical and procedure related data of all patients who underwent cardiac surgery have been prospectively collected in the Cardiovascular Information Registry

(CVIR) of the Department of Thoracic and Cardiovascular Surgery at Cleveland

Clinic since 1972. Eligible patients were identified by computerized query with pre-specified inclusion and exclusion criteria as described above.

1.4. Electrocardiographic data acquisition:

All patients who underwent cardiac surgery at Cleveland Clinic had an ECG within 30 days prior to surgery, which has been stored in the MUSE (Marquette

Universal System for Electrocardiography) database if acquired at our institution.

Patients who had their ECG done at an outside hospital therefore could have had missing ECG data. Since this missingness was potentially not at random, patients outside of the time intervals with corrupted ECG data were not excluded.

If multiple ECGs were present, the last preoperative ECG was chosen for analyses. ECG data of patients treated at Cleveland Clinic between 1991 and

2003 were available for analyses. The ECG data underwent a built-in quality control that assessed noise ratios. No formal validation study has been done to compare automated with manual measurements, however, it would be impossible to manually measure amplitudes and durations with comparable accuracy.

The Magellan software (General Electric, Milwaukee, WI) measures a total of 478 variables from one ECG (37 in all 12 leads, 10 describing general variables, i.e. ventricular rate, and 24 calculated variables, i.e. voltage criteria of LVH). ECG variables are computed from raw electronic data rather than measuring the graphical output, making measurements in microseconds and microvolt possible.

The software is compatible with MUSE, the most commonly used ECG processing and storage system, allowing our study results to be applied in other health care facilities within and outside the USA. Noise and lead placement of the individual ECG tracings naturally affect ECG measurements. However, a one- sided effect (i.e. differential information bias) is unlikely since the introduction of noise is a random phenomenon that results in a higher probability of a type 2 error (i.e. resulting in more false negatives) rather than a type 1 error (i.e. resulting in less false positives). Farrell et al. described excellent reproducibility of standard ECG measurements with and without noise introduction. Noise did not bias the measurements in their study(23).

1.5. Patient data:

1.5.3. The ECG variables of interest for specific aims #1 and #3:

• P-wave analysis:

o Duration (time of onset to end of P-wave) [ms]

o Area (area under curve of P-wave, per Magellan algorithm calculated)

[ ]

o Amplitude (isoelectric to maximal excursion of P-wave) [µV]

o Dispersion (maximal P-wave duration – minimal P-wave duration in

any 2 of the 12 leads) [ms]

• PR interval (time of onset of P-wave to beginning of Q-wave) [ms]

• QRS complex analysis:

o Duration (onset of Q-wave to J-point) [ms]

o Indices of LVH:

1. Lyon-Sokolow(24): S(V1) + R(V5 or V6) [µV]

2. Cornell(25): R(aVL) + S(V3)

3. Cornell voltage product(26): R(aVL) + S(V3) * QRS duration [µV* ms].

• Heart rate (obtained from averaged R-R intervals) [s-1]

1.5.2 Rationale for ECG variable inclusion

With the exception of heart rate, indices of LVH and P wave dispersion, all variables have been evaluated in all 12 leads. P wave measurements and PR interval are affected by atrial size and intra-atrial and atrio-ventricular signal conduction. P wave dispersion is a measure of inhomogeneity of atrial depolarization. Such inhomogeneity can lead to intra-atrial wave fronts that if re- entrant, perpetuate atrial fibrillation. Heart rate is affected by autonomic nervous control, sinus node function and grade of beta blockade in patients receiving such medical therapy, all of which are related to the development of POAF. QRS duration reflects interventricular electrical signal propagation, which is altered by myocardial remodeling in cardiac and systemic disorders, such as hypertension, diabetes, coronary artery disease and heart valve abnormalities. Indices of left ventricular hypertrophy correlate with thickening of the heart muscle, oftentimes

secondary to elevated cardiac afterload, i.e. in hypertension and valvular heart disease. All of these structural abnormalities have been linked to POAF(21,24-

26) and are inherently prevalent in our study sample.

1.5.3. Clinical patient characteristics for specific aims #2 and #3:

• Clinical and surgical patient characteristics include: age, gender, race, presence of chronic obstructive pulmonary disease, thyroid abnormalities, congestive heart failure, diabetes mellitus, hypertension, kidney disease, New

York Heart Association dyspnea class, Canadian Cardiology Society angina class, history of prior cardiac surgery, type of bypass grafting (i.e. number of internal mammary artery grafts), valve procedure (i.e. repair vs. replacement), cardiopulmonary bypass time, aortic cross clamp time, on-pump verses off-pump surgery, amount of perioperative transfusions of blood products (i.e. packed red blood cells, fresh frozen plasma, platelets).

• Coronary catheterization data: location and degree of coronary stenotic lesions, number of affected arteries.

• Echocardiography data: left atrial (LA) volume (calculated as a scaled cubed function of LA-diameter): , left ventricular ejection fraction.

• Medication use: beta blockers, ACE-inhibitors/angiotensin receptor blockers

(ARBs) in 1 category, calcium channel blockers, nitroglycerine and vasopressors.

Missingness of anti-arrhythmic drug use exceeded 30% and was therefore not analyzed.

• Lab data: preoperative creatinine, blood urea nitrogen and hematocrit levels.

1.5.4. Outcome:

The primary outcome (i.e. dependent variable) was development of atrial fibrillation after cardiac surgery and prior to hospital discharge. POAF was diagnosed by continuous telemetry and/or by 12-lead ECG. Atrial fibrillation had to require treatment (medical or electrical cardioversion or rate controlling drugs).

Our definition is consistent with the Society of Thoracic Surgeons definition of

POAF (see: http://www.ctsnet.org/file/rptDataSpecifications252_1_ForVendorsPGS.pdf) and most prior investigations of POAF. Although we allowed for short episodes of

POAF to be missed and the total number of events to be underestimated, we believe that only more sustained episodes that require therapy have a significant impact on patient outcomes and health care resources.

1.6. Statistical methods:

1.6.1. Missing data

The outcome variable (POAF) was complete. Other missing values were imputed with informative 5-fold multiple imputation using the MCMC algorithm of PROC

MI in SAS version 9.1 (©SAS Institute Inc., Cary, NC).

1.6.2. Statistical analysis

Characteristics of patients with and without POAF were compared with two-tailed student’s t-test for continuous variables and χ² statistics for categorical variables,

and are described in Table 1. Multivariable logistic regression with stepwise variable selection adjusted for concomitant comorbidities. To more accurately describe relationships between continuous variables and the outcome, we considered their inverse, logarithmic, squared and exponential transformations for variable selection. Automated data-driven variable selection is subject to model instability, especially when multiple collinear related variables are considered for selection. The problem is as follows: A group of variables is closely related and may adjust for each other’s effect on the outcome (POAF).

When a variable from this group is chosen as a model predictor, the other univariate-statistically significant variables cannot maintain multivariable-adjusted statistical significance (“independence”), because of their close relation to the already selected variable. One may argue that this is not a problem as long as any one group-variable has the exact same effect on the outcome as the others, which is an assumption that cannot hold true in biologic systems. To identify the most important group variable and therefore increase model stability, 500-fold bootstrap aggregation (bagging) was applied(27). 500 bootstrap samples were obtained by randomly selecting patients from the original data set with replacement, allowing for some patients to be chosen multiple times, and others not at all in one particular bootstrap sample. The number of patients in each bootstrap sample was equal to the number of patients in the original data set.

Automated multivariable logistic regression selected statistically significant predictors of POAF in each bootstrap sample. To account for the multitude of variables considered for our prediction model, stringent criteria for variable

selection were applied: an alpha level of 0.02 or less had to be met at entry (for model consideration) and 0.01 or less at exit for variable retention. The selected variables were then aggregated by counting the frequency they appeared in the final model in each of the 500 bootstrap samples. Patient predictors with a reliability of 50% or higher (i.e. stepwise –selected variables appear in 50% or more of the bootstrapped prediction models) contributed to the final bagged prediction model (i.e. median rule). To further account for collinearity, variables likely to be closely related (such as continuous variables and their non-linear transformations, all P wave measurements, LVH indices etc.) were clustered. If pre-defined variable clusters had a reliability of 50% or higher, variable constellations within the clusters decided which variables were selected from within the cluster: for example if within a cluster of 20 variables 3 of them were selected in most of the bootstrap models (i.e. constellations with 3 variables had the highest reliability within the cluster), then the three variables with the overall highest reliability were selected for the final bagged prediction model, even if the individual cluster variables had reliability lower than 50%. We argue that a lower individual reliability in this case is related to collinearity rather than low predictive power. Variable and cluster reliabilities were obtained by aggregating the 5 multiply imputed data sets. Beta estimates were obtained from the first imputed data set and expressed as odds ratios and their 95% confidence intervals. Final model fit was improved with restricted cubic spline transformation of continuous variables if indicated. To give a descriptive estimate of the effect size for non- linear transformed predictors, odds ratios were calculate from their linear beta

estimates as non-linear variables cannot be expressed in such a way. Receiver operating characteristic curve was plotted and its C-statistic reported to describe discriminative abilities of the final prediction model (see Figure 5). Overfitting and model calibration was assessed by 40-fold bootstrapping of the final prediction model to obtain a bias adjusted C-statistic and calibration curves of predicted verses observed probabilities of the development of POAF (Figure 6).

Model fit was assessed with Hosmer-Lemeshow goodness-of-fit test. C-statistic of the prediction model with and without selected ECG predictors was compared.

However, a large change of the C-statistic was not expected, because its insensitivity in model comparison(28).

Results:

1. Patient selection:

Out of 14,258 patients who under went cardiac surgery 1997 to 2003 and met inclusion criteria, 842 patients were excluded, which left 13,416 patients in the final study cohort (Figure 2).

Patients undergoing CABG +/- valve surgery 1997-2003 (n=14,258)

Patients with corrupted ECG data (n=580)

Patients in AF prior to surgery (n=173)

Patients with pacemaker or ICD (n= 89)

Patients for analysis (n=13,416)

Figure 2: Patient flow diagram

2. Patient characteristics:

Compared to patients who did not develop POAF, patients, who developed

POAF showed clinically and statistically significant differences for many measured variables (see Table 1). Some variables show clinically irrelevant differences to be highly statistically significant, because of large patient numbers in the comparison groups. A significant p value has to be interpreted with caution,

because it is not a measure of the degree of difference between comparison groups but a measure of the probability that the difference is due to chance.

Table I. Patient characteristics

No POAF POAF P-value

Variable (n=8,654) (n=4,762)

Demographic

Age, years¹ 61 (12) 68 (10) <0.0001

BMI, kg/m² ¹ 28 (5) 28 (5) 0.013

Female gender 2458 (28) 1405 (30) 0.18

Race Caucasian 7128 (83) 4226 (90) <0.0001

Black 525 (6.1) 154 (3.2) <0.0001

Other 901 (11) 328 (7.0) <0.0001

Clinical Data

Blood pressure systolic, mmHg¹ 137 (65) 138 (60) 0.36

Blood pressure diastolic, mmHg¹ 78 (66) 77 (61) 0.22

Pulse pressure, mmHg¹ 59 (19) 61 (21) <0.0001

Canadian angina class <0.0001

0 1261 (15) 848 (18)

1 2485 (29) 1560 (33)

2 3477 (40) 1662 (35)

3 1076 (12) 543 (11)

4 350 (4.1) 147 (3.1)

New York Heart Association class 0.031

1 1087 (13) 544 (11)

2 4471 (52) 2467 (52)

3 1502 (17) 907 (19)

4 1589 (18) 843 (18)

Prior atrial arrhythmia 472 (5.5) 876 (18) <0.0001

Congestive heart failure 2114 (24) 1572 (33) <0.0001

COPD 1674 (19) 1088 (23) <0.0001

Prior cardiac surgery 1381 (16) 794 (17) 0.28

CVA 549 (6.3) 379 (8.0) 0.0004

Diabetes Mellitus 2167 (25) 1135 (24) 0.12

Hypertension 5743 (68) 3328 (71) <0.0001

Peripheral vascular disease 3075 (36) 2136 (45) <0.0001

Renal disease (Cr>2) 365 (4.2) 286 (6.0) <0.0001

Smoking 5229 (61) 2823 (60) 0.24

Hyperthyroidism 90 (1.0) 57 (1.2) 0.4

Hypothyroidism 757 (8.8) 440 (9.2) 0.34

Prior ventricular arrhythmia 675 (7.8) 549 (12) <0.0001

Echocardiographic data

Aortic valve stenosis 1302 (15) 1090 (23) <0.0001

Aortic valve stenosis grade <0.0001

0 7305 (85) 3662 (77)

1 132 (1.5) 108 (2.3)

2 13 (0.15) 10 (0.2)

3 79 (0.92) 75 (1.6)

4 95 (1.1) 80 (1.7)

5 981 (11) 817 (17)

Aortic valve regurgitation 2001 (23) 1472 (31) <0.0001

Aortic valve regurgitation grade <0.0001

0 6653 (77) 3290 (69)

1 808 (9.3) 650 (14)

2 509 (5.9) 444 (9.3)

3 247 (2.9) 172 (3.6)

4 433 (5.0) 201 (4.2)

Mitral valve regurgitation 3943 (46) 2806 (59) <0.0001

Mitral valve regurgitation severity <0.0001

0 4711 (55) 1956 (41)

1 1427 (17) 844 (18)

2 743 (8.6) 514 (11)

3 454 (5.3) 368 (7.7)

4 1303 (15) 1074 (23)

Mitral valve stenosis 121 (1.4) 173 (3.6) <0.0001

Mitral valve stenosis severity <0.0001

0 8494 (99) 4581 (96)

1 38 (0.44) 67 (1.4)

2 4 (0.05) 3 (0.06)

3 20 (0.23) 26 (0.55)

4 14 (0.16) 13 (0.27)

5 44 (0.51) 64 (1.4)

Pulmonary valve regurgitation 1141 (13) 1092 (23) <0.0001

Pulmonary valve stenosis 2 (0.02) 8 (0.17) 0.0033

Tricuspid valve regurgitation 2264 (26) 1761 (37) <0.0001

Tricuspid valve regurgitation <0.0001 severity

0 6390 (74) 3001 (63)

1 1495 (17) 1024 (22)

2 568 (6.6) 515 (11)

3 140 (1.6) 172 (3.61)

4 56 (0.65) 47 (0.99)

Left ventricular ejection fraction, 50 (13) 50 (13) 0.33

%¹

Left atrial volume, cc¹ 42 (24) 50 (30) <0.0001

Left atrial diameter >6 cm 84 (1.8) 104 (3.7) <0.0001

Lab data

BUN, mg/dl 19 (10) 21 (11) <0.0001

Creatinine, mg/dl¹ 1.1 (0.87) 1.2 (1) <0.0001

Hematocrit, g/dl¹ 38 (5.2) 38 (5.1) <0.0001

HDL, mg/dl¹ 44 (13) 45 (14) 0.0008

LDL, mg/dl¹ 118 (40) 113 (38) <0.0001

Coronary data

Number of systems >50%¹ 2 (1.2) 1.9 (1.2) <0.0001

LAD disease >50% 6126 (74) 3170 (68) <0.0001

LAD disease >70% 5453 (66) 2745 (59) <0.0001

LAD disease, any 6649 (81) 3597 (78) <0.0001

LCx disease >50% 5215 (63) 2795 (60) 0.0005

LCx disease >70% 4458 (54) 2369 (51) 0.0007

LCx disease, any 5841 (71) 3182 (69) 0.0046

Left main CA disease >50% 1542 (19) 899 (19) 0.37

Left main CA disease >70% 804 (9.8) 494 (11) 0.11

Left main CA disease, any 2895 (35) 1669 (36) 0.36

RCA disease >50% 5448 (66) 2912 (63) <0.0001

RCA disease >70% 4720 (57) 2542 (55) 0.0049

RCA disease, any 6111 (74) 3319 (72) 0.0008

Medication use

Angiotensin blockade 3791 (44) 2233 (47) 0.0006

Anticoagulants 4102 (47) 2574 (54) <0.0001

Beta blockers 4918 (57) 2691 (57) 0.72

Calcium channel blockers 2012 (23) 1193 (25) 0.019

Inotropes, preoperative 169 (2) 138 (2.9) 0.0005

Nitroglycerin iv 1154 (13) 654 (14) 0.52

Nitroglycerin po/patch 3920 (45) 2069 (43) 0.039

Statin 696 (51) 328 (47) 0.11

Surgical

CABG (isolated and combined with 6731 (78) 3573 (75) 0.0003

valve surgery)

Aortic valve replacement 1449 (17) 1158 (24) <0.0001

Aortic valve repair 211 (2.4) 58 (1.2) <0.0001

Mitral valve replacement 247 (2.9) 344 (7.2) <0.0001

Mitral valve repair 1456 (17) 1090 (23) <0.0001

Tricuspid valve replacement 4 (0.05) 3 (0.06) 0.70

Tricuspid valve repair 131 (1.5) 145 (3.0) <0.0001

Pulmonary valve replacement 7 (0.08) 1 (0.02) 0.27

Postop. intraaortic balloon pump 101 (1.2) 85 (1.8) 0.0034

Postop. ICD implant 130 (1.5) 107 (2.3) 0.0017

Internal thoracic artery graft <0.0001

(CABG only), #

0 3043 (35) 2015 (42)

1 4733 (55) 2471 (52)

2 878 (10) 276 (5.8)

Onpump (CABG and valve 7657 (88) 4389 (92) <0.0001

surgery)

Postop. FFP transfusion, units¹ 0.16 (1.2) 0.37 (1.7) <0.0001

Postop. platelet transfusion, units¹ 0.22 (1) 0.42 (1.8) <0.0001

Postop. RBC transfusion, units¹ 1.1 (3.2) 1.9 (4.2) <0.0001

Interval to surgery year, years¹ 8.9 (1.7) 8.8 (1.7) 0.9

Myocardial ischemia, min¹ 76 (30) 79 (29) 0.0085

Cardiopulmonary bypass, min¹ 99 (38) 104 (40) <0.0001

Emergency surgery 109 (1.3) 79 (1.7) 0.06

Quantitative ECG measurements

Atrial heart rate, 1/s¹ 69 (14) 69 (14) 0.34

Ventricular heart rate, 1/s¹ 69 (14) 69 (14) 0.34

LVH by Cornell criteria, µV¹ 1700 (840) 1700 (850) <0.0001

Cornell voltage duration product, 170 (110) 180- (110) <0.0001

µV*ms¹

LVH by Romhilt-Estes-Score¹ 1.7 (2) 1.9 (2.1) <0.0001

LVH by Sokolow criteria, µV¹ 2300 (1100) 2400 (1100) 0.46

Sokolow voltage duration product, 230 (130) 240 (120) 0.046

µV*ms¹

P wave dispersion, ms¹ 68 (21) 71 (23) <0.0001

P wave amplitude I, µV¹ 80 (30) 70 (33) <0.0001

P wave amplitude II, µV¹ 110 (46) 95 (47) <0.0001

P wave amplitude III, µV¹ 49 (45) 48 (45) <0.16

P wave amplitude aVF, µV¹ 76 (45) 70 (42) <0.0001

P wave amplitude aVL, µV¹ 18 (44) 12 (43) <0.0001

P wave amplitude aVR, µV¹ -91 (33) -79 (34) <0.0001

P wave amplitude V1, µV¹ 26 (51) 31 (52) <0.0001

P wave amplitude V2, µV¹ 46 (38) 47 (43) 0.09

P wave amplitude V3, µV¹ 61 (28) 59 (31) 0.0001

P wave amplitude V4, µV¹ 64 (26) 59 (27) <0.0001

P wave amplitude V5, µV¹ 63 (24) 57 (26) <0.0001

P wave amplitude V6, µV¹ 62 (24) 55 (26) <0.0001

P wave area I, 1/(4.88*4) µV*ms¹ 210 (87) 190 (95) <0.0001

P wave area II, 1/(4.88*4) µV*ms¹ 310 (140) 290 (150) <0.0001

P wave area III, 1/(4.88*4) µV*ms¹ 98 (130) 94 (140) 0.17

P wave area aVF, 200 (130) 190 (140) <0.0001

1/(4.88*4) µV*ms¹

P wave area aVL, 57 (88) 49 (92) <0.0001

1/(4.88*4) µV*ms¹

P wave area aVR, -260 (95) -240 (110) <0.0001

1/(4.88*4) µV*ms¹

P wave area V1, 1/(4.88*4) -63 (110) -62 (120) 0.9

µV*ms¹

P wave area V2, 1/(4.88*4) 51 (100) 42 (120) <0.0001

µV*ms¹

P wave area V3, 1/(4.88*4) 150 (88) 130 (96) <0.0001

µV*ms¹

P wave area V4, 1/(4.88*4) 180 (85) 170 (87) <0.0001

µV*ms¹

P wave area V5, 1/(4.88*4) 190 (78) 170 (84) <0.0001

µV*ms¹

P wave area V6, 1/(4.88*4) 180 (71) 170 (79) <0.0001

µV*ms¹

P wave axis, °¹ 44 (24) 45 (28) <0.0001

P wave duration I, ms¹ 110 (21) 110 (30) 0.34

P wave duration II, ms¹ 110 (21) 110 (26) 0.63

P wave duration III, ms¹ 86 (32) 85 (34) 0.09

P wave duration aVF, ms¹ 100 (26) 100 (30) 0.01

P wave duration aVL, ms¹ 83 (36) 81 (38) 0.015

P wave duration aVR, ms¹ 110 (20) 110 (25) 0.014

P wave duration V1, ms¹ 53 (26) 53 (27) 0.93

P wave duration V2, ms¹ 71 (33) 68 (32) <0.0001

P wave duration V3, ms¹ 98 (28) 94 (33) <0.0001

P wave duration V4, ms¹ 110 (22) 100 (29) <0.0001

P wave duration V5, ms¹ 110 (21) 110 (28) 0.33

P wave duration V6, ms¹ 110 (21) 110 (29) 0.26

P wave duration (median all 110 (18) 110 (22) <0.0001 leads), ms¹

P prime duration I, ms¹ 0.62 (7.2) 1.2 (9.6) 0.0002

P prime duration II, ms¹ 0.76 (6.7) 1.9 (10) <0.0001

P prime duration III, ms¹ 17 (26) 20 (28) <0.0001

P prime duration aVF, ms¹ 3.7 (14) 6.1 (18) <0.0001

P prime duration aVL, ms¹ 19 (30) 22 (31) <0.0001

P prime duration aVR, ms¹ 0.24 (3.9) 0.69 (6.8) <0.0001

P prime duration V1, ms¹ 53 (31) 56 (33) 0.0001

P prime duration V2, ms¹ 28 (33) 33 (34) <0.0001

P prime duration V3, ms¹ 6.2 (18) 10 (22) <0.0001

P prime duration V4, ms¹ 1.5 (9) 3.3 (14) <0.0001

P prime duration V5, ms¹ 0.65 (6.5) 1.3 (8.7) <0.0001

P prime duration V6, ms¹ 0.32 (4.8) 0.71 (7.2) 0.0012

PR interval (median all leads), ms¹ 170 (36) 180 (45) <0.0001

QRS area I, 1/(4.88*4) µV*ms¹ 1100 (790) 1100 (840) 0.71

QRS area II, 1/(4.88*4) µV*ms¹ 700 (870) 600 (880) <0.0001

QRS area III, 1/(4.88*4) µV*ms¹ -400 (1100) -500 (1190) <0.0001

QRS area aVF, 1/(4.88*4) µV*ms¹ 150 (920) 59 (960) <0.0001

QRS area aVL, 1/(4.88*4) µV*ms¹ 750 (870) 800 (930) 0.0033

QRS area aVR, 1/(4.88*4) µV*ms¹ -900 (610) -860 (620) 0.0004

QRS area V1, 1/(4.88*4) µV*ms¹ -1600 (1700) -1600 (1800) 0.98

QRS area V2, 1/(4.88*4) µV*ms¹ -1700 (2300) -1800 (2300) 0.14

QRS area V3, 1/(4.88*4) µV*ms¹ -590 (2100) -730 (2200) 0.0003

QRS area V4, 1/(4.88*4) µV*ms¹ 790 (1600) 700 (1700) 0.0036

QRS area V5, 1/(4.88*4) µV*ms¹ 1400 (1200) 1400 (1300) 0.95

QRS area V6, 1/(4.88*4) µV*ms¹ 1400 (1000) 1400 (1100) 0.19

QRS axis (median beat), °¹ 14 (39) 12 (41) 0.0007

QRS duration (median), ms¹ 99 (18) 101 (20) <0.0001

QT interval (median), ms¹ 400 (41) 410 (44) <0.0001

Data is presented as number (%) or mean (standard deviation) where indicated with ¹; Angiotensin blockade indicates preoperative use of angiotensin converting enzyme inhibitors or angiotensin II receptor blockers; BMI, body mass index; CA, coronary artery; CABG, coronary artery bypass graft surgery; COPD, chronic obstructive pulmonary disease; CVA, prior stroke; FFP,

fresh frozen plasma; ICD, implantable cardioverter-defibrillator; LAD, left anterior descending artery; LCx, left circumflex artery; LVH, left ventricular hypertrophy; On-pump, cardiac surgery on cardiopulmonary bypass support; POAF, postoperative atrial fibrillation; RBC, red blood cells; RCA right coronary artery;

3. Occurrence of POAF:

Out of 13,416 patients, 4762 (35%) developed POAF prior to discharge. In accordance to previous reports, occurrence of POAF was highest on day 2 followed by day 3 after surgery (see Figure 3).

0 1 2 3 4 5 >5 Days after Cardiac Surgery

Figure 3: Occurrence of postoperative atrial fibrillation

Occurrence of POAF varied among the surgical procedures. Isolated CABG had the lowest occurrence and combined valvular surgery had the highest (see

Figure 4).

Figure 4: Occurrence of postoperative atrial fibrillation by surgical procedure in % of procedure type. AVR indicates aortic valve replacement; CABG, coronary artery bypass graft surgery; MV, mitral valve; MVR, mitral valve replacement;

4. Treatment of POAF:

Out of 4762 patients, who developed POAF, 1163 (24%) underwent electrical cardioversion; the rest were treated medically with anti-arrhythmic drugs or rate controlling medications.

5. Prediction model:

Out of over 140 measured patient variables (see Table 1), that included demographic, clinical, echocardiographic, surgical and electrocardiographic

characteristics, those with an averaged individual or cluster reliability across all 5 imputed data sets of 50% or higher were retained in the final prediction model. 14 independent variables met these criteria. Bias (overfitting) adjusted C-statistic was 0.74 (see Figure 5), indicating fair discrimination.

Figure 5: Receiver operating characteristic (ROC) curve for the final prediction model indicating fair discrimination

Hosmer-Lemeshow goodness of fit test was non-significant with a p-value of 0.5, indicating goodness of fit. 40- fold bootstrap validation of the prediction model showed a mean absolute prediction error of 0.009. Low prediction error and good overlap of the apparent curve and the bias-adjusted (model-overfitting) curve indicate good model calibration (see Figure 6). The deviation of the model

prediction from ‘ideal’ is attributable to relatively small patient numbers with a probability of POAF greater than 70% (as indicated by the ‘carpet fringe’ bars at the top frame of the graph).

Figure 6: Calibration curve.

Predicted probabilities of POAF overlap closely observed probabilities.

Bias (overfitting) corrected curve appears almost identical to the apparent

(model) curve indicating good model fit. The vertical fringe bars on top of the graph represent density of patient numbers.

6. Clinical predictors of POAF:

Independent clinical risk factors were older age, body mass index, previous episodes of atrial arrhythmia, left atrial volume, preoperative therapy with beta- 36

blockers, preoperative hematocrit level and number of perioperative red blood cell transfusions. Patients who were African American and those with hypothyroidism were less likely to develop POAF. Independent surgical predictors were aortic valve replacement, mitral valve repair and mitral valve replacement (see Table 2).

Variable Odds Ratio 95% CI Reliability

Older Age* per 1 year 2.16 1.93-2.41 100%

Higher BMI per 1 kg/m² 1.06 1.01 -1.11 60%†

Black race 0.44 0.32 -0.60 100%

Prior atrial arrhythmia 2.70 2.37 -3.07 100%

Beta blocker use 1.08 1.01 -1.18 81%

Higher preoperative hematocrit* 1.12 1.07 -1.18 80%†

per 1 g/dl

Larger left atrial volume* per 10 ml 1.26 1.20 -1.33 100%

Hypothyroidism 0.79 0.69 -0.91 75%

Mitral valve repair 1.81 1.62 -2.01 89%

Mitral valve replacement 2.28 1.87 -2.79 88%

Aortic valve replacement 1.43 1.30 -1.59 91%

More RBC transfusions* per 1 unit 1.21 1.17 -1.25 100%

Table 2: Multivariable adjusted clinical predictors of postoperative atrial

fibrillation

*non-linear transformed in prediction model

† Cluster reliability

BMI indicates body mass index; CI, confidence interval;

7. ECG predictors:

Out of all 74 measured ECG variables, P wave amplitude in lead aVR and V1 had the highest individual reliability (60% and 40%, respectively), while the cluster of P wave measurements had 100% reliability for predicting POAF (see

Table 3). P wave amplitude in lead I (reliability 57%), P wave area in lead aVR

(reliability 36%), and P prime duration in lead III (reliability 38%) were retained in the final bagged model. However, their association with POAF could be almost completely explained by P wave amplitude in lead aVR. These ECG variables were highly correlated and adjusted (i.e. modulated) each other’s association with POAF. To allow interpretation of our prediction model we deleted P wave amplitude in lead I, P wave area in lead aVR and P prime duration in lead III.

Restricted cubic spline transformation of P wave amplitude in aVR and V1 improved model fit and made at least in part up for excluding their 3 adjusting variables.

Variable Odds Ratio 95% CI Reliability

Less negative P amplitude in aVR 1.46 1.32-1.61 100%†

(per 10 µV)

Higher P wave amplitude in V1 (per 1.25 1.16-1.36 100%†

10 µV)

Table 3: Multivariable adjusted ECG predictors of postoperative atrial fibrillation

† Cluster reliability

CI indicates confidence interval

As an extreme example of adjusting effects of correlated variables, P wave area in aVR, which was selected in the bagged prediction model interestingly switched from a univariate risk factor to a multivariable-adjusted protective patient characteristic (see Figure 7). Such effect is difficult to interpret and deletion of adjusting variables did not significantly affect either the predictive ability or model fit.

Figure 7: Top graph shows unadjusted univariate relationship of P wave area in lead aVR (greater P wave area increases the probability of postoperative atrial fibrillation), while the bottom graph depicts the multivariable adjusted relationship (greater P wave area decreases the probability of postoperative atrial fibrillation).

The effect of P wave amplitude in lead V1 as a risk factor indicates that a more positive initial deflection increases the risk of POAF. This may be related to right atrial enlargement, which can lead to a large positive deflection in V1. The effect size of P wave variables was dependent on the age group they were applied to

(see Figure 8). For patients younger than 50 years, less negative P wave amplitude in aVR only slightly increased the probability of POAF. In older patients the effect was much more pronounced, as indicated by the steeper slopes of the curves.

Figure 8: Model adjusted probability of postoperative atrial fibrillation by ECG predictor P wave amplitude in lead aVR and V1 plotted by age deciles. The effect of P wave amplitude depends on patients’ age. Model adjustment was done with the most frequent values (mode) for categorical and median values for continuous variables.

We created a nomogram to display the effect sizes of multivariable adjusted predictors of the final prediction model with inclusion of peri- and postoperative variables, which would not be available for pre-operative risk assessment (Figure

9). A second nomogram uses a prediction model of preoperative predictors only that may be clinically more relevant to estimate patients’ risk of POAF (Figure

10). To obtain the probability of POAF, one extrapolates each variable’s value to the point score on the top line of the nomogram and adds all point scores. Then,

‘Total points’ of the second to last line are extrapolated to obtain the ‘predicted probability of postoperative atrial fibrillation’ expressed as a fraction between 0 and 1. For clinical user friendliness our risk prediction algorithm could be incorporated within electronic medical records systems.

Figure 9: Nomogram for clinical risk assessment with pre and peri-operative predictors: Find for each patient variable the associated score in the first line and add them to get ‘Total Points’. The total point score can then be extrapolated to the probability of developing postoperative atrial fibrillation after cardiac surgery (e.g. 0.5 = 50%)

Figure 10: Nomogram for clinical risk assessment of developing postoperative atrial fibrillation with preoperative predictors only: Find for each patient variable the associated score in the first line and add them to get ‘Total Points’. The total point score can then be extrapolated to the probability of developing postoperative atrial fibrillation after cardiac surgery (e.g. 0.5 = 50%)

Example of risk assessment:

A 70 year old (79 points) female with a history of paroxysmal atrial fibrillations (21 points), left atrial volume of 40 cc (6 points), Caucasian race (19 points), no preoperative beta blocker use, no history of hypothyroidism (5 points), a P wave amplitude in lead aVR of -50 µV (20 points) and P wave amplitude in lead V1 of -

200 µV (5 points) and a body mass index of 30 (2 points) undergoing CABG without valve surgery receives a total score of 157 points giving her a 70% probability of developing POAF. This patient should be considered for prophylactic medication use before surgery.

8. Additional predictive power of ECG variables:

The C-statistic of the prediction model decreased from 7.4 to 7.2 when ECG predictors were disregarded. Model fit worsened slightly and bootstrap validated mean prediction error increased from 0.009 to 0.011 which is equal to a 19% relative increase (see Figure 11). This indicates that the prediction model without

ECG variables predicts the development of POAF with less accuracy. However, interpretation of this increase of prediction error has to be done with caution, because estimates are drawn from random samples and have therefore variability (i.e. change of prediction error could be a random phenomenon caused by sampling [bootstrapping] of different patients).

Figure 11: Bootstrap validation of the prediction model without ECG variables. Mean prediction error increased by 19%.

Discussion

P wave amplitude measurements predict postoperative atrial fibrillation after cardiac surgery with high reliability (see Table 3). We further present a prediction rule of postoperative atrial fibrillation from a comprehensive evaluation of pre- and perioperative patient characteristics using bagging techniques for model stability and increased prediction accuracy. We show that automated measurements of P wave amplitude in lead aVR and V1 significantly contribute and improve model discrimination, while P wave duration, dispersion, PR interval, QRS duration and ECG indices of LVH do not independently contribute to the risk assessment of POAF. Our model’s C-statistic of 0.74 indicates that approximately 50% of patients can be accurately risk stratified. The accurate prediction of POAF is important, because it can identify subgroups of patients that are most likely to benefit from prophylactic interventions that to date are not routinely employed despite their proven efficacy.

ECG predictors of POAF:

P wave amplitude in lead aVR and lead V1 showed to be the most reliable out of

74 measured ECG variables. Most clinicians pay little attention to lead aVR when interpreting a 12 lead ECG. Our study suggests that the diagnostic value of lead aVR should be revisited. There are no data describing the behavior of P wave amplitude in lead aVR in left or right atrial enlargement. Only 55 patients had a positive P wave axis in our study, some of which may be explained by ectopic rhythms. 1403 patients had a P wave amplitude more positive than -50 µV. Left atrial volume was significantly larger in patients with a P wave amplitude in aVR

greater than -50 µV (see figure 12). Therefore less negative P wave amplitude in lead aVR may be related to larger left atrial size. The overall correlation of P wave amplitude in lead aVR with left atrial size was statistically significant, but not very strong with a correlation coefficient of 0.1 (see Figure 13). Therefore, P wave amplitude in lead aVR appears to be related to another unknown mechanism, possibly structural and electrical changes within the right atrium.

Interestingly, it is the right atrium that undergoes most injury during cannulation for cardiopulmonary bypass.

Figure 12: On average, left atrial volumes of patients with P wave amplitude in lead aVR greater than – 50 µV were significantly larger than those of patients with smaller P wave amplitudes in lead aVR.

Figure 13: Correlation matrix of P wave amplitude in lead aVR (x-axis) and left atrial volume (y-axis). The slope is relatively flat indicating a weak correlation (Pearson correlation factor of 0.1, p= <0.0001).

The correlation of P wave amplitude in lead V1 with left and right atrial size has been established. Large positive P wave amplitude in V1 indicates right atrial enlargement. Predominantly negative or biphasic P wave amplitude in lead V1 with pronounced terminal negative force indicates left atrial enlargement.

Therefore, P wave amplitude in lead V1 is related to right and left atrial enlargement. Our nomogram shows that more negative as well as more positive

P wave amplitudes increase the risk of postoperative atrial fibrillation (see figure

9 and 10), the first likely being related to left atrial enlargement and the latter to right atrial enlargement. However, as we stated earlier, ECG measurements may

not be solely based on cardiac dimensions, but are affected by other processes such as inflammation, myocardial remodeling and chest wall properties (i.e. higher impedance in obese patients). It is unlikely that smaller P wave amplitudes are simply a marker of obesity because BMI remained an independent predictor in our model and neither interaction between P wave amplitude and BMI nor their correlation coefficient was statistically significant. The high reliability of P wave amplitude in lead aVR and V1 for prediction of POAF is unexpected, but intriguing and warrants more research. Our study is consistent with Amar’s report of only a small contribution of P wave duration and PR prolongation to the overall prediction of POAF. Amar’s analysis did not include P wave amplitudes. Although

Passman and colleagues investigated similar ECG measurements, their smaller patient number may have precluded them from detecting an association of P wave amplitude in lead aVR and V1 with POAF.

Clinical predictors and pathophysiologic interpretations:

Although it is impossible to prove causation in an observational study, we can hypothesize about possible underlying mechanisms leading to observed associations with POAF. The predictive importance of patients’ age is consistent with previous reports(3) and is most likely linked to a higher prevalence of hypertension, left ventricular systolic and diastolic dysfunction and left atrial remodeling leading to abnormalities of atrial conduction, sinus node dysfunction and an increase in effective refractory period(29). Prior episodes of atrial arrhythmias are indicators of an atrial substrate that predisposes to POAF. These

prior arrhythmic events can induce atrial remodeling and perpetuate atrial fibrillation(30). Patients with paroxysmal and chronic atrial fibrillation have elevated hematocrit levels(31), which may explain the independent predictive power of preoperative levels in our study. Chronic lung disease, obstructive sleep apnea causing intermittent hypoxemia and subsequently elevated hematocrit levels via an erythropoietin-mediated pathway may also be related to our observation. We confirmed the previously reported association of body mass index with POAF(32). A higher prevalence of left ventricular diastolic dysfunction and left atrial enlargement in obesity is a plausible explanation(33).

Arrhythmogenic properties of free fatty acids released via catecholamine induced lipolysis during surgical stress have been proposed but lack clinical evidence(34).

The link between perioperative transfusions of red blood cells and POAF demonstrated in our study has been previously observed(35), and may be linked to an inflammatory response to foreign blood cells(36). We hypothesize that the protective role of hypothyroidism in our study is linked to lower endogenous thyroxine hormone levels in this group of patients, although we did not measure thyroid stimulating hormone levels as indicator of thyroid function. Interestingly, it has been reported that exogenous hormone supplementation in the perioperative period decreases the occurrence of POAF(37). Our study provides further evidence that African Americans may be at a lower risk of developing atrial fibrillation. Alonso and colleagues reported in a recent study that patients of

African American descent have a 41% lower age and gender adjusted cumulative risk of developing atrial fibrillation in the non-surgical setting, despite

a higher incidence of hypertension(38). Although it has been argued that this difference observed in the outpatient setting may be due to worse access to medical care of African-American patients and detection bias(39), the diagnosis of POAF in our study should not be affected by such bias making a genetic basis for differences in occurrence of POAF very plausible. Socioeconomic differences that have not been investigated in our study should also be considered. Off-pump

CABG (without cardiopulmonary bypass) has been described to be protective from POAF. In our study, off-pump CABG was not associated with POAF after multivariable adjustment.

Finally, heart valve surgery such as mitral valve repair and replacement and aortic valve replacement are strong predictors of POAF, likely due to pressure or volume overload of left atria from valve disease with associated remodeling of ion channels(40), stretch and fibrosis(41) prior to surgery as well as inflammation caused by mechanical manipulation during surgery. The rate of POAF after valve surgery was higher than in isolated CABG (see Figure 4), which is consistent with previous studies(1).

Clinical implications:

Current guidelines(42) recommend use of perioperative beta blockers (class I), amiodarone (class IIA) and sotalol (class IIB) for the prevention of POAF. Many clinicians have valid concerns of potential adverse events from these medications given for POAF, which in the majority of cases has a minor impact on patients’ immediate well-being. The impact on health care resources may not

outweigh physicians’ safety concerns, which could prevent routine application of medical prophylaxis. Our proposed prediction model was created with statistical techniques that increase model stability and generalizability and therefore could change current practice, by increasing the use of prophylactic medications with a better risk-benefit ratio in high-risk patients. Our risk scoring system can be applied prior to cardiac surgery and gives treating physicians and patients a fairly accurate estimate of the probability of developing postoperative atrial fibrillation.

This information will help clinicians and patients to make informed decisions about prophylactic medication use.

Limitation

Novel methods of bootstrap aggregation have been employed in our study to stabilize our prediction model and increase generalizability of our results.

However, the data were obtained from a single tertiary referral center and our patients may differ from patients at other institutions in regards to their risk profile. Therefore, our prediction model should be tested in other patient populations. Although we adjusted for multiple prospectively collected patient variables, hidden bias cannot be excluded in an observational study. To validate our prediction rule, we will apply it to 120 prospectively enrolled patients in a study that is currently underway. To obtain quantitative ECG measurements, commercially available software is required and although applicable to the most commonly used electronic ECG storage system (MUSE, General Electric,

Milwaukee, WI) some ECG systems do not have the ability to measure individual

ECG variables with comparable accuracy. We did not have information about statin use, which currently is under investigation as a potential prophylactic drug therapy for POAF and could have affected our results. We did not investigate clinical or surgical subgroups, in which prediction accuracy may differ. An inflation of odds ratios in their translation into relative risk should be considered given our high event rate.

Conclusions:

Postoperative atrial fibrillation can be predicted with fair accuracy using a combination of electrocardiographic and clinical patient variables. P wave amplitude in lead aVR and V1 are novel reliable predictors of POAF, increase discrimination and should be assessed preoperatively. Our risk score may guide the application of prophylactic medications, which should be considered in high- risk patients to improve surgical outcomes and reduce burden on health care resources. A prospective validation of our prediction rule is needed and underway.

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