- Email: [email protected]
Usefulness of the Electrocardiographic P-Wave Axis as a Predictor of Atrial Fibrillation
Accepted Manuscript Usefulness of the Electrocardiographic P-Wave Axis as a Predictor of Atrial Fibrillation Maria Octavia Rangel, MD, MSc, Wesley T. O’Neal, MD, MPH, Elsayed Z. Soliman, MD, MSc, MS PII:
S0002-9149(15)02090-1
DOI:
10.1016/j.amjcard.2015.10.013
Reference:
AJC 21463
To appear in:
The American Journal of Cardiology
Received Date: 15 August 2015 Revised Date:
6 October 2015
Accepted Date: 9 October 2015
Please cite this article as: Rangel MO, O’Neal WT, Soliman EZ, Usefulness of the Electrocardiographic P-Wave Axis as a Predictor of Atrial Fibrillation, The American Journal of Cardiology (2015), doi: 10.1016/j.amjcard.2015.10.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Usefulness of the Electrocardiographic P-Wave Axis as a Predictor of Atrial Fibrillation
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Authors: Maria Octavia Rangel, MD, MSc a* Wesley T. O’Neal, MD, MPH b* Elsayed Z. Soliman, MD, MSc, MS a, c
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*Contributed equally in the writing of this manuscript.
Affiliations:
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a. Cardiovascular Medicine Section, Department of Internal Medicine, Wake Forest School of Medicine (Winston-Salem, North Carolina)
b. Department of Internal Medicine, Wake Forest School of Medicine (Winston-Salem, North Carolina)
c. Epidemiological Cardiology Research Center, Division of Public Health Sciences, Wake
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Forest School of Medicine (Winston-Salem, North Carolina)
Conflict of interest: The authors declare no conflicts of interest.
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CORRESPONDENCE:
Elsayed Z. Soliman MD, MSc, MS
Epidemiological Cardiology Research Center (EPICARE)
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Wake Forest School of Medicine
Medical Center Blvd. Winston Salem, NC 27157 Tel: (336) 716- 8632. Fax: (336) 716-0834 E-mail [email protected]
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ABSTRACT
The association between abnormal electrocardiographic P-wave axis with atrial fibrillation (AF)
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has not been systematically studied in community-based populations. We examined the association between abnormal P-wave axis and AF in 4,274 (41% male, 95% white) participants from the Cardiovascular Health Study (CHS). Axis values between 0º and 75º were considered
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normal. AF cases were identified from study electrocardiograms (ECGs) and from
hospitalization discharge data. During a median follow-up of 12.1 years, a total of 1,274 (30%)
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participants developed AF. The incidence rate of AF was 26 cases per 1000 person-years for those with abnormal P-wave axis and 24 cases per 1000 person-years for subjects with normal Pwave axis. Abnormal P-wave axis was associated with a 17% increased risk of AF (95% Confidence Interval=1.03, 1.33) after adjustment for age, sex, race, education, income, smoking, diabetes, coronary heart disease, stroke, heart failure, heart rate, systolic blood pressure, body
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mass index, total cholesterol, HDL cholesterol, antihypertensive medications, aspirin, and statins. The results were consistent in subgroup analyses stratified by age, sex, and race. In conclusion,
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abnormal P-wave axis, a routinely reported electrocardiographic measurement, is associated with an increased risk of AF. This finding suggests a potential role for P-wave axis in AF risk
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assessment.
Keywords: P-wave axis, atrial fibrillation, risk assessment
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Atrial fibrillation (AF) is the most common arrhythmia found in clinical practice.1 The economic burden of AF on the health care system is expected to increase in the coming years, and this parallels the ageing United States population.2 Therefore, simple ways of identifying individuals
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at risk for AF are important for both public health officials and the practicing clinician.3-7 P-wave axis, a routinely reported measure on the contemporary electrocardiogram (ECG), represents atrial electrical activity. Abnormalities in this parameter are reflective of atrial pathology and
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possibly associated with an increased risk of AF development.8 Therefore, we examined the
population-based cohort of the elderly. METHODS
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association between abnormal P-wave axis and AF in the Cardiovascular Health Study (CHS), a
Details of CHS have previously described.9 Briefly, CHS is a prospective populationbased cohort study of risk factors for coronary heart disease and stroke in individuals 65 years
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and older. A total of 5,888 participants with Medicare eligibility in the United States were recruited from 4 field centers located in the following locations: Forsyth County, NC; Sacramento County, CA; Washington County, MD; and Pittsburgh, PA. Subjects were followed
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with semi-annual contacts, alternating between telephone calls and surveillance clinic visits.
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CHS clinic exams ended in June of 1999 and since that time 2 yearly phone calls to participants were used to identify events and collect data. The institutional review board at each site approved the study and written informed consent was obtained from participants at enrollment. For the purpose of this analysis, participants were excluded if they had AF at baseline,
major intraventricular conduction delays (including complete bundle branch blocks and/or QRS duration ≥120 ms), or had missing baseline covariate data or missing follow-up data.
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Identical electrocardiographs (MAC PC, Marquette Electronics Inc., Milwaukee, Wisconsin) were used at all clinic sites, and resting, 10-second standard simultaneous 12-lead ECGs were recorded in all participants. All ECGs were processed in a central laboratory
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(initially at Dalhousie University, Halifax, NS, Canada and later at the Epidemiological
Cardiology Research Center, Wake Forest School of Medicine, Winston-Salem, NC). The
methodology and prevalence of ECG abnormalities in CHS have been previously reported.10
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Computerized automated analyses of the electrocardiographic data were performed which
included selective averaging to obtain the frontal P-wave axis. Axis values between 0º and 75º
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were considered normal.8
Baseline AF cases were identified from the initial study ECG or by self-reported history of a physician diagnosis. AF cases also were identified during the annual study ECGs that were performed every year until 1999. Additionally, hospitalization discharge data were used to
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identify AF cases using International Classification of Diseases codes 427.31 and 427.32. Hospital diagnosis codes for AF ascertainment have been shown to have a positive predictive value of 98.6%.11
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Participant characteristics were collected during the initial CHS interview and
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questionnaire. Age, sex, race, income, and education were self-reported. Annual income was dichotomized at $25,000 and education was dichotomized at “high school or less.” Smoking was defined as ever (e.g., current or former) or never smoker. Participants’ blood samples were obtained after a 12-hour fast at the local field center. Measurements of total cholesterol, highdensity lipoprotein (HDL) cholesterol, and plasma glucose were used in this analysis. Diabetes was defined as a self-reported history of a physician diagnosis, a fasting glucose value ≥126 mg/dL, or by the current use of insulin or oral hypoglycemic medications. Blood pressure was 4
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measured for each participant in the seated position and systolic measurements were used in this analysis. Heart rate was measured from the resting ECG. The use of aspirin, statins, and antihypertensive medications was self-reported. Body mass index was computed as the weight in
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kilograms divided by the square of the height in meters. Hypertension was defined as blood pressure values >140/90 or by the use of antihypertensive medications. Baseline coronary heart disease was determined by self-reported history or by medical record adjudication of the
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following diagnoses: myocardial infarction, angina pectoris without myocardial infarction,
coronary revascularization procedures (angioplasty and coronary artery bypass graft surgery).12
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Baseline cases of stroke and heart failure were identified by self-reported history of a physician diagnosis followed by review of medical records.
Categorical variables were reported as frequency and percentage while continuous variables were recorded as mean ± standard deviation. Statistical significance for categorical
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variables was tested using the chi-square method and the Wilcoxon rank-sum procedure for continuous variables. Follow-up time was defined as the time between the initial study visit until one of the following: AF development, death, loss to follow-up, or end of follow-up (December
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31, 2010). Kaplan-Meier estimates were used to compute cumulative incidence of AF by abnormal P-wave axis and the difference in estimates was compared using the log-rank
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procedure.13 Cox regression was used to compute hazard ratios (HR) and 95% confidence interval (CI) for the association between abnormal P-wave axis and incident AF. Multivariate models were constructed as follows: Model 1 adjusted for age, sex, race, education, and income; Model 2 adjusted for Model 1 covariates plus smoking, diabetes, coronary heart disease, stroke, heart failure, heart rate, systolic blood pressure, body mass index, total cholesterol, HDL cholesterol, antihypertensive medications, aspirin, and statins. We tested for interactions between
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our main effect variable and age (dichotomized at 75 years), sex, and race (white vs. black). We also constructed a restricted cubic spline model to examine the graphical dose-response relationship between P-wave axis and AF at the 5th, 50th, and 95th percentiles.14 Statistical
Version 9.3 (Cary, NC) was used for all analyses.
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RESULTS
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significance for our main effect models and interaction terms was defined as p<0.05. SAS
A total of 4,274 participants with complete data were used in this analysis. Baseline
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characteristics are reported in Table 1.
Over a median follow-up of 12.1 years, a total of 1,274 (30%) participants developed AF. The incidence rate of AF was higher for those with abnormal P-wave axis (26 cases per 1000 person-years) than normal P-wave axis (24 cases per 1000 person-years). The unadjusted
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cumulative incidence for AF by abnormal P-wave axis is shown in Figure 1. A U-shaped association was observed when P-wave axis was included in a restricted cubic spline model as a continuous variable (Figure 2).
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In a multivariate Cox regression analysis, abnormal P-wave axis was associated with a 17% increase in the risk of AF (Table 2). The association was consistent when we stratified the
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analysis by age, sex, and race (Table 3). DISCUSSION
In this analysis from CHS, we have demonstrated that abnormal P-wave axis is associated
with an increased risk of AF. P-wave axis determination is a relatively easy tool for the practicing clinician and it likely identifies persons with left atrial disease who have the necessary
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substrate to develop AF. These findings build upon prior work showing that left atrial disease predisposes to the supraventricular arrhythmias such as AF.15
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The P-wave axis represents propagation of electrical forces within the atria and is determined by atrial anatomy, electrical conduction, and orientation of the atria within the
chest.16 Mechanical and metabolic insults to the atria induce remodeling and abnormal electrical
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conduction which results in abnormal P-axis and ultimately leads to AF.17
In an analysis from the United States Third National Health and Nutrition Examination
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Survey (NHANES III), abnormal P-wave axis was associated with increased risk of all-cause mortality and cardiovascular mortality.8 Also, consistent with our results, P-wave axis was associated with AF development in a predominantly male patient-based population.18 These reports provide further evidence that abnormal P-wave axis possibly leads to AF. Recently, there has been a growing interest in electrocardiographic P-wave indices as
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predictors of AF, stroke, and mortality.19-25 These indices include parameters derived from Pwave morphology, such as duration, area, and amplitude. In particular, P-wave duration has been
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significantly associated with several adverse outcomes.19,24,25 However, unlike the P-wave axis, these indices are not typically reported during routine 12-lead ECG measurement and require
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additional processing.20-22
The PR interval is routinely reported by contemporary ECG machines and it has been
suggested as an alternative to P-wave indices in AF prediction models.3,5,6 However, the predictive ability of the PR interval has been inconsistent. In the AF risk score from the Framingham Heart Study (FHS), a prolonged PR interval was significantly predictive of AF in the initial regression analysis and was included in the final model.5,6 In contrast, short PR interval was predictive of AF in the CHARGE-AF consortium study.4 Also, PR interval was 7
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removed from the model developed for the Atherosclerosis Risk in Communities (ARIC) study, due to a lack of predictive value.3 Similarly, a prospective study of over 10,000 individuals followed for 30 years demonstrated that PR interval prolongation was not associated with an
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increased risk of AF.26 These conflicting results suggest that the PR interval is not the best
surrogate of atrial electrical activity to use in AF risk prediction models. This highlights the need for new atrial predictors of AF such as P-wave axis.
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This analysis should be interpreted in the context of certain limitations. The CHS study
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included participants aged 65 years and older who were mainly white. Hence, our results may not be generalizable to younger populations or other races. Nevertheless, AF is highly prevalent in the elderly and among whites, which makes the CHS population the ideal cohort for assessment of AF predictors. Additionally, we adjusted for several potential confounders but acknowledge that residual confounding remains a possibility. Despite these limitations, our
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results demonstrate a potential utility for abnormal P-wave axis to predict AF. ACKNOWLEDGEMENTS: This Manuscript was prepared using CHS Research Materials obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating
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Center and does not necessarily reflect the opinions or views of the CHS or the NHLBI.
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DISCLOSURES: None of the authors reports a conflict of interest.
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2. Odden MC, Coxson PG, Moran A, Lightwood JM, Goldman L, Bibbins-Domingo K. The impact of the aging population on coronary heart disease in the United States. Am J Med 2011;124:827-833.
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3. Chamberlain AM, Agarwal SK, Folsom AR, Soliman EZ, Chambless LE, Crow R, Ambrose M, Alonso A. A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the
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Launer LJ, Hofman A, Levy D, Gottdiener JS, Kaab S, Couper D, Harris TB, Soliman EZ, Stricker BH, Gudnason V, Heckbert SR, Benjamin EJ. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF
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6. Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB, Sr., NewtonCheh C, Yamamoto JF, Magnani JW, Tadros TM, Kannel WB, Wang TJ, Ellinor PT, Wolf PA,
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17. Spach MS. Mounting evidence that fibrosis generates a major mechanism for atrial fibrillation. Circ Res 2007;101:743-745. 18. Wong JK, Lobato RL, Pinesett A, Maxwell BG, Mora-Mangano CT, Perez MV. P-wave characteristics on routine preoperative electrocardiogram improve prediction of new-onset postoperative atrial fibrillation in cardiac surgery. J Cardiothorac Vasc Anesth 2014;28:14971504.
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Ellinor PT, Benjamin EJ. P wave duration and risk of longitudinal atrial fibrillation in persons >/= 60 years old (from the Framingham Heart Study). Am J Cardiol 2011;107:917-921 e911. 22. Magnani JW, Williamson MA, Ellinor PT, Monahan KM, Benjamin EJ. P wave indices: current status and future directions in epidemiology, clinical, and research applications. Circ
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24. Magnani JW, Gorodeski EZ, Johnson VM, Sullivan LM, Hamburg NM, Benjamin EJ, Ellinor PT. P wave duration is associated with cardiovascular and all-cause mortality outcomes:
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the National Health and Nutrition Examination Survey. Heart Rhythm 2011;8:93-100. 25. Nielsen JB, Kuhl JT, Pietersen A, Graff C, Lind B, Struijk JJ, Olesen MS, Sinner MF, Bachmann TN, Haunso S, Nordestgaard BG, Ellinor PT, Svendsen JH, Kofoed KF, Kober L, Holst AG. P-wave duration and the risk of atrial fibrillation: Results from the Copenhagen ECG Study. Heart Rhythm 2015;12:1887-1895.
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Heart J 2014;35:123-129.
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FIGURE LEGENDS Figure 1. Cumulative Incidence of Atrial Fibrillation* Incidence curves are not statistically different (log-rank P=0.082).
Figure 2. Risk of Atrial Fibrillation across P-wave Axis* *
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*
Each hazard ratio was computed with the median P-wave axis value of 63 degrees as the
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reference and was adjusted for age, sex, race, education, income, smoking, diabetes, coronary heart disease, stroke, heart failure, heart rate, systolic blood pressure, body mass index, total
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HDL=high-density lipoprotein.
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cholesterol, HDL cholesterol, antihypertensive medications, aspirin, and statins.
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Table 1. Baseline Characteristics of Study Participants (N=4,274) P-wave Axis P-value*
Characteristic Abnormal
Normal
(n=1,150)
(n=3,124)
65-70
474 (41%)
71-74
264 (23%)
75-80
290 (25%)
>80
122 (11%)
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Age (years)
1,439 (46%) 731 (23%) 672 (22%)
0.008
1,285 (41%)
0.22
2,978 (95%)
0.33
1,791 (57%)
0.33
1,938 (62%)
0.73
696 (61%)
1,599 (51%)
<0.0001
129 (11%)
472 (15%)
0.0012
192 (17%)
537 (17%)
0.70
37 (3.2%)
96 (3.1%)
0.81
35 (3.0%)
84 (2.7%)
0.53
66 (11)
64 (10)
<0.0001
Systolic blood pressure, mean (SD) (mm Hg)
139 (21)
138 (19)
0.65
Body mass index, mean (SD) (kg/m2)
25 (3.8)
27 (3.9)
<0.0001
Total cholesterol, mean (SD) (mg/dL)
211 (39)
214 (39)
0.0058
HDL cholesterol, mean (SD) (mg/dL)
58 (17)
53 (15)
<0.0001
673 (59%)
2,028 (65%)
0.0001
Antihypertensive medication use
427 (37%)
1,411 (45%)
<0.0001
Aspirin use
349 (30%)
1,063 (34%)
0.023
15 (1.3%)
63 (2.0%)
0.12
Male
449 (39%)
White
1,088 (95%) 640 (56%)
Annual income <$25,000
720 (63%)
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High school or less
Ever smoker Diabetes mellitus Coronary heart disease Stroke Heart failure
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Heart rate, mean (SD) (bpm)
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Statin use
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Hypertension
*
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282 (9%)
Statistical significance for continuous data was tested using Wilcoxon rank-sum procedure and categorical data was
tested using the chi-square test.
Bpm=beats per minute; HDL=high-density lipoprotein; SD=standard deviation.
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Table 2. P-Wave Axis and Risk of Atrial Fibrillation (N=4,274) Model 1* P-wave Axis Cases Person-years P-value HR (95%CI)
Model 2† HR (95%CI)
P-value
Normal
932
39,174
1.0
-
1.0
-
Abnormal
342
13,159
1.10 (0.97, 1.25)
0.12
1.17 (1.03, 1.33)
0.02
Adjusted for age, sex, race, education, and income.
†
Adjusted for Model 1 covariates plus smoking, diabetes, coronary heart disease, stroke, heart failure, heart
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*
rate, systolic blood pressure, body mass index, total cholesterol, HDL cholesterol, antihypertensive medications, aspirin, and statins.
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CI=confidence interval; HDL=high-density lipoprotein; HR=hazard ratio.
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Table 3. P-Wave Axis and Risk of Atrial Fibrillation Subgroup Analyses (N=4,274) Subgroup HR (95% CI) * P-value P-interaction 1.20 (1.02, 1.41)
0.032
Age ≥75 years
1.11 (0.89, 1.37)
0.37
Male
1.18 (0.97, 1.44)
0.095
Female
1.15 (0.97, 1.37)
0.11
White
1.17 (1.02, 1.33)
0.023
Black
1.61 (0.69, 3.73)
0.27
*
0.72
0.67
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Age <75 years
0.83
Adjusted for age, sex, race, education, income, smoking, diabetes, coronary heart disease, stroke,
cholesterol, antihypertensive medications, aspirin, and statins.
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heart failure, heart rate, systolic blood pressure, body mass index, total cholesterol, HDL
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CI=confidence interval; HDL=high-density lipoprotein; HR=hazard ratio.
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S0002-9149(15)02090-1
DOI:
10.1016/j.amjcard.2015.10.013
Reference:
AJC 21463
To appear in:
The American Journal of Cardiology
Received Date: 15 August 2015 Revised Date:
6 October 2015
Accepted Date: 9 October 2015
Please cite this article as: Rangel MO, O’Neal WT, Soliman EZ, Usefulness of the Electrocardiographic P-Wave Axis as a Predictor of Atrial Fibrillation, The American Journal of Cardiology (2015), doi: 10.1016/j.amjcard.2015.10.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Usefulness of the Electrocardiographic P-Wave Axis as a Predictor of Atrial Fibrillation
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Authors: Maria Octavia Rangel, MD, MSc a* Wesley T. O’Neal, MD, MPH b* Elsayed Z. Soliman, MD, MSc, MS a, c
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*Contributed equally in the writing of this manuscript.
Affiliations:
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a. Cardiovascular Medicine Section, Department of Internal Medicine, Wake Forest School of Medicine (Winston-Salem, North Carolina)
b. Department of Internal Medicine, Wake Forest School of Medicine (Winston-Salem, North Carolina)
c. Epidemiological Cardiology Research Center, Division of Public Health Sciences, Wake
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Forest School of Medicine (Winston-Salem, North Carolina)
Conflict of interest: The authors declare no conflicts of interest.
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CORRESPONDENCE:
Elsayed Z. Soliman MD, MSc, MS
Epidemiological Cardiology Research Center (EPICARE)
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Wake Forest School of Medicine
Medical Center Blvd. Winston Salem, NC 27157 Tel: (336) 716- 8632. Fax: (336) 716-0834 E-mail [email protected]
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ABSTRACT
The association between abnormal electrocardiographic P-wave axis with atrial fibrillation (AF)
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has not been systematically studied in community-based populations. We examined the association between abnormal P-wave axis and AF in 4,274 (41% male, 95% white) participants from the Cardiovascular Health Study (CHS). Axis values between 0º and 75º were considered
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normal. AF cases were identified from study electrocardiograms (ECGs) and from
hospitalization discharge data. During a median follow-up of 12.1 years, a total of 1,274 (30%)
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participants developed AF. The incidence rate of AF was 26 cases per 1000 person-years for those with abnormal P-wave axis and 24 cases per 1000 person-years for subjects with normal Pwave axis. Abnormal P-wave axis was associated with a 17% increased risk of AF (95% Confidence Interval=1.03, 1.33) after adjustment for age, sex, race, education, income, smoking, diabetes, coronary heart disease, stroke, heart failure, heart rate, systolic blood pressure, body
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mass index, total cholesterol, HDL cholesterol, antihypertensive medications, aspirin, and statins. The results were consistent in subgroup analyses stratified by age, sex, and race. In conclusion,
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abnormal P-wave axis, a routinely reported electrocardiographic measurement, is associated with an increased risk of AF. This finding suggests a potential role for P-wave axis in AF risk
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assessment.
Keywords: P-wave axis, atrial fibrillation, risk assessment
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Atrial fibrillation (AF) is the most common arrhythmia found in clinical practice.1 The economic burden of AF on the health care system is expected to increase in the coming years, and this parallels the ageing United States population.2 Therefore, simple ways of identifying individuals
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at risk for AF are important for both public health officials and the practicing clinician.3-7 P-wave axis, a routinely reported measure on the contemporary electrocardiogram (ECG), represents atrial electrical activity. Abnormalities in this parameter are reflective of atrial pathology and
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possibly associated with an increased risk of AF development.8 Therefore, we examined the
population-based cohort of the elderly. METHODS
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association between abnormal P-wave axis and AF in the Cardiovascular Health Study (CHS), a
Details of CHS have previously described.9 Briefly, CHS is a prospective populationbased cohort study of risk factors for coronary heart disease and stroke in individuals 65 years
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and older. A total of 5,888 participants with Medicare eligibility in the United States were recruited from 4 field centers located in the following locations: Forsyth County, NC; Sacramento County, CA; Washington County, MD; and Pittsburgh, PA. Subjects were followed
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with semi-annual contacts, alternating between telephone calls and surveillance clinic visits.
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CHS clinic exams ended in June of 1999 and since that time 2 yearly phone calls to participants were used to identify events and collect data. The institutional review board at each site approved the study and written informed consent was obtained from participants at enrollment. For the purpose of this analysis, participants were excluded if they had AF at baseline,
major intraventricular conduction delays (including complete bundle branch blocks and/or QRS duration ≥120 ms), or had missing baseline covariate data or missing follow-up data.
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Identical electrocardiographs (MAC PC, Marquette Electronics Inc., Milwaukee, Wisconsin) were used at all clinic sites, and resting, 10-second standard simultaneous 12-lead ECGs were recorded in all participants. All ECGs were processed in a central laboratory
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(initially at Dalhousie University, Halifax, NS, Canada and later at the Epidemiological
Cardiology Research Center, Wake Forest School of Medicine, Winston-Salem, NC). The
methodology and prevalence of ECG abnormalities in CHS have been previously reported.10
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Computerized automated analyses of the electrocardiographic data were performed which
included selective averaging to obtain the frontal P-wave axis. Axis values between 0º and 75º
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were considered normal.8
Baseline AF cases were identified from the initial study ECG or by self-reported history of a physician diagnosis. AF cases also were identified during the annual study ECGs that were performed every year until 1999. Additionally, hospitalization discharge data were used to
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identify AF cases using International Classification of Diseases codes 427.31 and 427.32. Hospital diagnosis codes for AF ascertainment have been shown to have a positive predictive value of 98.6%.11
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Participant characteristics were collected during the initial CHS interview and
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questionnaire. Age, sex, race, income, and education were self-reported. Annual income was dichotomized at $25,000 and education was dichotomized at “high school or less.” Smoking was defined as ever (e.g., current or former) or never smoker. Participants’ blood samples were obtained after a 12-hour fast at the local field center. Measurements of total cholesterol, highdensity lipoprotein (HDL) cholesterol, and plasma glucose were used in this analysis. Diabetes was defined as a self-reported history of a physician diagnosis, a fasting glucose value ≥126 mg/dL, or by the current use of insulin or oral hypoglycemic medications. Blood pressure was 4
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measured for each participant in the seated position and systolic measurements were used in this analysis. Heart rate was measured from the resting ECG. The use of aspirin, statins, and antihypertensive medications was self-reported. Body mass index was computed as the weight in
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kilograms divided by the square of the height in meters. Hypertension was defined as blood pressure values >140/90 or by the use of antihypertensive medications. Baseline coronary heart disease was determined by self-reported history or by medical record adjudication of the
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following diagnoses: myocardial infarction, angina pectoris without myocardial infarction,
coronary revascularization procedures (angioplasty and coronary artery bypass graft surgery).12
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Baseline cases of stroke and heart failure were identified by self-reported history of a physician diagnosis followed by review of medical records.
Categorical variables were reported as frequency and percentage while continuous variables were recorded as mean ± standard deviation. Statistical significance for categorical
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variables was tested using the chi-square method and the Wilcoxon rank-sum procedure for continuous variables. Follow-up time was defined as the time between the initial study visit until one of the following: AF development, death, loss to follow-up, or end of follow-up (December
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31, 2010). Kaplan-Meier estimates were used to compute cumulative incidence of AF by abnormal P-wave axis and the difference in estimates was compared using the log-rank
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procedure.13 Cox regression was used to compute hazard ratios (HR) and 95% confidence interval (CI) for the association between abnormal P-wave axis and incident AF. Multivariate models were constructed as follows: Model 1 adjusted for age, sex, race, education, and income; Model 2 adjusted for Model 1 covariates plus smoking, diabetes, coronary heart disease, stroke, heart failure, heart rate, systolic blood pressure, body mass index, total cholesterol, HDL cholesterol, antihypertensive medications, aspirin, and statins. We tested for interactions between
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our main effect variable and age (dichotomized at 75 years), sex, and race (white vs. black). We also constructed a restricted cubic spline model to examine the graphical dose-response relationship between P-wave axis and AF at the 5th, 50th, and 95th percentiles.14 Statistical
Version 9.3 (Cary, NC) was used for all analyses.
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RESULTS
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significance for our main effect models and interaction terms was defined as p<0.05. SAS
A total of 4,274 participants with complete data were used in this analysis. Baseline
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characteristics are reported in Table 1.
Over a median follow-up of 12.1 years, a total of 1,274 (30%) participants developed AF. The incidence rate of AF was higher for those with abnormal P-wave axis (26 cases per 1000 person-years) than normal P-wave axis (24 cases per 1000 person-years). The unadjusted
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cumulative incidence for AF by abnormal P-wave axis is shown in Figure 1. A U-shaped association was observed when P-wave axis was included in a restricted cubic spline model as a continuous variable (Figure 2).
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In a multivariate Cox regression analysis, abnormal P-wave axis was associated with a 17% increase in the risk of AF (Table 2). The association was consistent when we stratified the
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analysis by age, sex, and race (Table 3). DISCUSSION
In this analysis from CHS, we have demonstrated that abnormal P-wave axis is associated
with an increased risk of AF. P-wave axis determination is a relatively easy tool for the practicing clinician and it likely identifies persons with left atrial disease who have the necessary
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substrate to develop AF. These findings build upon prior work showing that left atrial disease predisposes to the supraventricular arrhythmias such as AF.15
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The P-wave axis represents propagation of electrical forces within the atria and is determined by atrial anatomy, electrical conduction, and orientation of the atria within the
chest.16 Mechanical and metabolic insults to the atria induce remodeling and abnormal electrical
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conduction which results in abnormal P-axis and ultimately leads to AF.17
In an analysis from the United States Third National Health and Nutrition Examination
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Survey (NHANES III), abnormal P-wave axis was associated with increased risk of all-cause mortality and cardiovascular mortality.8 Also, consistent with our results, P-wave axis was associated with AF development in a predominantly male patient-based population.18 These reports provide further evidence that abnormal P-wave axis possibly leads to AF. Recently, there has been a growing interest in electrocardiographic P-wave indices as
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predictors of AF, stroke, and mortality.19-25 These indices include parameters derived from Pwave morphology, such as duration, area, and amplitude. In particular, P-wave duration has been
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significantly associated with several adverse outcomes.19,24,25 However, unlike the P-wave axis, these indices are not typically reported during routine 12-lead ECG measurement and require
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additional processing.20-22
The PR interval is routinely reported by contemporary ECG machines and it has been
suggested as an alternative to P-wave indices in AF prediction models.3,5,6 However, the predictive ability of the PR interval has been inconsistent. In the AF risk score from the Framingham Heart Study (FHS), a prolonged PR interval was significantly predictive of AF in the initial regression analysis and was included in the final model.5,6 In contrast, short PR interval was predictive of AF in the CHARGE-AF consortium study.4 Also, PR interval was 7
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removed from the model developed for the Atherosclerosis Risk in Communities (ARIC) study, due to a lack of predictive value.3 Similarly, a prospective study of over 10,000 individuals followed for 30 years demonstrated that PR interval prolongation was not associated with an
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increased risk of AF.26 These conflicting results suggest that the PR interval is not the best
surrogate of atrial electrical activity to use in AF risk prediction models. This highlights the need for new atrial predictors of AF such as P-wave axis.
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This analysis should be interpreted in the context of certain limitations. The CHS study
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included participants aged 65 years and older who were mainly white. Hence, our results may not be generalizable to younger populations or other races. Nevertheless, AF is highly prevalent in the elderly and among whites, which makes the CHS population the ideal cohort for assessment of AF predictors. Additionally, we adjusted for several potential confounders but acknowledge that residual confounding remains a possibility. Despite these limitations, our
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results demonstrate a potential utility for abnormal P-wave axis to predict AF. ACKNOWLEDGEMENTS: This Manuscript was prepared using CHS Research Materials obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating
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Center and does not necessarily reflect the opinions or views of the CHS or the NHLBI.
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DISCLOSURES: None of the authors reports a conflict of interest.
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2. Odden MC, Coxson PG, Moran A, Lightwood JM, Goldman L, Bibbins-Domingo K. The impact of the aging population on coronary heart disease in the United States. Am J Med 2011;124:827-833.
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3. Chamberlain AM, Agarwal SK, Folsom AR, Soliman EZ, Chambless LE, Crow R, Ambrose M, Alonso A. A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the
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Launer LJ, Hofman A, Levy D, Gottdiener JS, Kaab S, Couper D, Harris TB, Soliman EZ, Stricker BH, Gudnason V, Heckbert SR, Benjamin EJ. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF
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5. Schnabel RB, Aspelund T, Li G, Sullivan LM, Suchy-Dicey A, Harris TB, Pencina MJ,
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D'Agostino RB, Sr., Levy D, Kannel WB, Wang TJ, Kronmal RA, Wolf PA, Burke GL, Launer LJ, Vasan RS, Psaty BM, Benjamin EJ, Gudnason V, Heckbert SR. Validation of an atrial fibrillation risk algorithm in whites and African Americans. Arch Intern Med 2010;170:19091917.
6. Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB, Sr., NewtonCheh C, Yamamoto JF, Magnani JW, Tadros TM, Kannel WB, Wang TJ, Ellinor PT, Wolf PA,
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Vasan RS, Benjamin EJ. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet 2009;373:739-745. 7. Benjamin EJ, Levy D, Vaziri SM, D'Agostino RB, Belanger AJ, Wolf PA. Independent risk
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factors for atrial fibrillation in a population-based cohort. The Framingham Heart Study. JAMA 1994;271:840-844.
8. Li Y, Shah AJ, Soliman EZ. Effect of electrocardiographic P-wave axis on mortality. Am J
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Cardiol 2014;113:372-376.
9. Fried LP, Borhani NO, Enright P, Furberg CD, Gardin JM, Kronmal RA, Kuller LH, Manolio
Ann Epidemiol 1991;1:263-276.
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TA, Mittelmark MB, Newman A, et al. The Cardiovascular Health Study: design and rationale.
10. Furberg CD, Manolio TA, Psaty BM, Bild DE, Borhani NO, Newman A, Tabatznik B, Rautaharju PM. Major electrocardiographic abnormalities in persons aged 65 years and older
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(the Cardiovascular Health Study). Cardiovascular Health Study Collaborative Research Group. Am J Cardiol 1992;69:1329-1335.
11. Psaty BM, Manolio TA, Kuller LH, Kronmal RA, Cushman M, Fried LP, White R, Furberg
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CD, Rautaharju PM. Incidence of and risk factors for atrial fibrillation in older adults. Circulation 1997;96:2455-2461.
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12. Psaty BM, Kuller LH, Bild D, Burke GL, Kittner SJ, Mittelmark M, Price TR, Rautaharju PM, Robbins J. Methods of assessing prevalent cardiovascular disease in the Cardiovascular Health Study. Ann Epidemiol 1995;5:270-277. 13. Gray RJ, Tsiatis AA. A linear rank test for use when the main interest is in differences in cure rates. Biometrics 1989;45:899-904.
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14. Marrie RA, Dawson NV, Garland A. Quantile regression and restricted cubic splines are useful for exploring relationships between continuous variables. J Clin Epidemiol 2009;62:511517.
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15. Eranti A, Aro AL, Kerola T, Anttonen O, Rissanen HA, Tikkanen JT, Junttila MJ, Kentta TV, Knekt P, Huikuri HV. Prevalence and prognostic significance of abnormal P terminal force in lead V1 of the ECG in the general population. Circ Arrhythm Electrophysiol 2014;7:1116-
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16. Hancock EW, Deal BJ, Mirvis DM, Okin P, Kligfield P, Gettes LS, Bailey JJ, Childers R,
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Gorgels A, Josephson M, Kors JA, Macfarlane P, Mason JW, Pahlm O, Rautaharju PM, Surawicz B, van Herpen G, Wagner GS, Wellens H, American Heart Association E, Arrhythmias Committee CoCC, American College of Cardiology F, Heart Rhythm S. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part V:
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17. Spach MS. Mounting evidence that fibrosis generates a major mechanism for atrial fibrillation. Circ Res 2007;101:743-745. 18. Wong JK, Lobato RL, Pinesett A, Maxwell BG, Mora-Mangano CT, Perez MV. P-wave characteristics on routine preoperative electrocardiogram improve prediction of new-onset postoperative atrial fibrillation in cardiac surgery. J Cardiothorac Vasc Anesth 2014;28:14971504.
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19. Magnani JW, Zhu L, Lopez F, Pencina MJ, Agarwal SK, Soliman EZ, Benjamin EJ, Alonso A. P-wave indices and atrial fibrillation: cross-cohort assessments from the Framingham Heart Study (FHS) and Atherosclerosis Risk in Communities (ARIC) study. Am Heart J 2015;169:53-
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20. Soliman EZ, Prineas RJ, Case LD, Zhang ZM, Goff DC, Jr. Ethnic distribution of ECG
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Ellinor PT, Benjamin EJ. P wave duration and risk of longitudinal atrial fibrillation in persons >/= 60 years old (from the Framingham Heart Study). Am J Cardiol 2011;107:917-921 e911. 22. Magnani JW, Williamson MA, Ellinor PT, Monahan KM, Benjamin EJ. P wave indices: current status and future directions in epidemiology, clinical, and research applications. Circ
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26. Aro AL, Anttonen O, Kerola T, Junttila MJ, Tikkanen JT, Rissanen HA, Reunanen A, Huikuri HV. Prognostic significance of prolonged PR interval in the general population. Eur
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Heart J 2014;35:123-129.
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FIGURE LEGENDS Figure 1. Cumulative Incidence of Atrial Fibrillation* Incidence curves are not statistically different (log-rank P=0.082).
Figure 2. Risk of Atrial Fibrillation across P-wave Axis* *
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*
Each hazard ratio was computed with the median P-wave axis value of 63 degrees as the
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reference and was adjusted for age, sex, race, education, income, smoking, diabetes, coronary heart disease, stroke, heart failure, heart rate, systolic blood pressure, body mass index, total
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HDL=high-density lipoprotein.
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cholesterol, HDL cholesterol, antihypertensive medications, aspirin, and statins.
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Table 1. Baseline Characteristics of Study Participants (N=4,274) P-wave Axis P-value*
Characteristic Abnormal
Normal
(n=1,150)
(n=3,124)
65-70
474 (41%)
71-74
264 (23%)
75-80
290 (25%)
>80
122 (11%)
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Age (years)
1,439 (46%) 731 (23%) 672 (22%)
0.008
1,285 (41%)
0.22
2,978 (95%)
0.33
1,791 (57%)
0.33
1,938 (62%)
0.73
696 (61%)
1,599 (51%)
<0.0001
129 (11%)
472 (15%)
0.0012
192 (17%)
537 (17%)
0.70
37 (3.2%)
96 (3.1%)
0.81
35 (3.0%)
84 (2.7%)
0.53
66 (11)
64 (10)
<0.0001
Systolic blood pressure, mean (SD) (mm Hg)
139 (21)
138 (19)
0.65
Body mass index, mean (SD) (kg/m2)
25 (3.8)
27 (3.9)
<0.0001
Total cholesterol, mean (SD) (mg/dL)
211 (39)
214 (39)
0.0058
HDL cholesterol, mean (SD) (mg/dL)
58 (17)
53 (15)
<0.0001
673 (59%)
2,028 (65%)
0.0001
Antihypertensive medication use
427 (37%)
1,411 (45%)
<0.0001
Aspirin use
349 (30%)
1,063 (34%)
0.023
15 (1.3%)
63 (2.0%)
0.12
Male
449 (39%)
White
1,088 (95%) 640 (56%)
Annual income <$25,000
720 (63%)
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High school or less
Ever smoker Diabetes mellitus Coronary heart disease Stroke Heart failure
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Heart rate, mean (SD) (bpm)
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Statin use
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Hypertension
*
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282 (9%)
Statistical significance for continuous data was tested using Wilcoxon rank-sum procedure and categorical data was
tested using the chi-square test.
Bpm=beats per minute; HDL=high-density lipoprotein; SD=standard deviation.
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Table 2. P-Wave Axis and Risk of Atrial Fibrillation (N=4,274) Model 1* P-wave Axis Cases Person-years P-value HR (95%CI)
Model 2† HR (95%CI)
P-value
Normal
932
39,174
1.0
-
1.0
-
Abnormal
342
13,159
1.10 (0.97, 1.25)
0.12
1.17 (1.03, 1.33)
0.02
Adjusted for age, sex, race, education, and income.
†
Adjusted for Model 1 covariates plus smoking, diabetes, coronary heart disease, stroke, heart failure, heart
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*
rate, systolic blood pressure, body mass index, total cholesterol, HDL cholesterol, antihypertensive medications, aspirin, and statins.
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CI=confidence interval; HDL=high-density lipoprotein; HR=hazard ratio.
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Table 3. P-Wave Axis and Risk of Atrial Fibrillation Subgroup Analyses (N=4,274) Subgroup HR (95% CI) * P-value P-interaction 1.20 (1.02, 1.41)
0.032
Age ≥75 years
1.11 (0.89, 1.37)
0.37
Male
1.18 (0.97, 1.44)
0.095
Female
1.15 (0.97, 1.37)
0.11
White
1.17 (1.02, 1.33)
0.023
Black
1.61 (0.69, 3.73)
0.27
*
0.72
0.67
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Age <75 years
0.83
Adjusted for age, sex, race, education, income, smoking, diabetes, coronary heart disease, stroke,
cholesterol, antihypertensive medications, aspirin, and statins.
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heart failure, heart rate, systolic blood pressure, body mass index, total cholesterol, HDL
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CI=confidence interval; HDL=high-density lipoprotein; HR=hazard ratio.
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