- Email: [email protected]
Frequent Atrial Premature Complexes and Their Association With Risk of Atrial Fibrillation
Accepted Manuscript Frequent Atrial Premature Complexes and their Association with Risk of Atrial Fibrillation Tushar Acharya, MD, Steven Tringali, DO, Manminder Bhullar, MD, Marta Nalbandyan, DO, Vishnu Karthik Ilineni, MD, Enrique Carbajal, MD, Prakash Deedwania, MD PII:
S0002-9149(15)02004-4
DOI:
10.1016/j.amjcard.2015.09.025
Reference:
AJC 21427
To appear in:
The American Journal of Cardiology
Received Date: 28 August 2015 Revised Date:
13 September 2015
Accepted Date: 15 September 2015
Please cite this article as: Acharya T, Tringali S, Bhullar M, Nalbandyan M, Ilineni VK, Carbajal E, Deedwania P, Frequent Atrial Premature Complexes and their Association with Risk of Atrial Fibrillation, The American Journal of Cardiology (2015), doi: 10.1016/j.amjcard.2015.09.025. 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.
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Frequent Atrial Premature Complexes and their Association with Risk of Atrial Fibrillation
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Tushar Acharya, MD1,2, Steven Tringali, DO2, Manminder Bhullar, MD1,2, Marta Nalbandyan, DO2, Vishnu Karthik Ilineni, MD2, Enrique Carbajal, MD1,2, Prakash Deedwania, MD1,2 Running title: Atrial Premature Complexes and Atrial Fibrillation
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Affiliation: 1. Division of Cardiology; 2. Department of Internal Medicine. Veterans
Francisco, Fresno, California Corresponding Author: Tushar Acharya, MD
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Fellow, Cardiovascular Disease
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Administration Central California Health Care System and University of California, San
University of California San Francisco, Fresno
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UCSF Fresno, Division of Cardiology Academic Offices
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2335 E Kashian Lane, Suite 460 Fresno, California 93721 Phone: (559) 246-9449 Fax: (559) 459- 5157
Email: [email protected]
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ABSTRACT Identification of precursors of Atrial Fibrillation (AF) may lead to early detection and prevent
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associated morbidity and mortality. This study aims to examine the association between frequent atrial premature complexes (APCs) and incidence of AF. For this retrospective cohort study, we analyzed Holter recordings obtained between 2000 and 2010 of 1357 veterans free of AF at
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baseline. All pertinent data in electronic medical records were reviewed to ascertain baseline characteristics. Holter groups with frequent (≥100/day) and infrequent (<100/day) APCs were
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compared for development of new AF over a median follow up of 7.5 years. Multivariate Cox regression analyses were performed before and after propensity score matching. Mean age was 64 years with 93% men. Mean body mass index, hemoglobin A1C, low density lipoprotein, left atrial size and heart rate were 31.24 kg/m2, 6.42%, 107.92 mg/dL, 4.26 cm and 73 beats per minute, respectively. AF was noted in 21.8% of patients with frequent APCs compared to 5.6%
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of those with infrequent APCs. After adjusting for demographics, medication use, comorbidities, lab and echocardiographic findings, multivariate Cox regression analyses confirmed frequent APCs to be independently associated with higher incidence of AF (HR, 2.97 [95% CI,
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1.85 - 4.80]; P<0.001). In propensity-matched groups, this association remained significant (HR,
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2.87 [95% CI, 1.65 - 4.98]; P<0.001). Additionally, atrial couplets (≥50/day), atrial bigeminy (≥50/day), frequent runs of ≥3 APCs (≥20 runs/day) as well as longer runs (≥10 beats/run) were significantly associated with AF (HR, 3.11, 3.67, 2.94 and 1.73 respectively, all P<0.05). In conclusion, frequent APCs (≥100/day) are associated with higher risk of AF.
Key words: atrial premature complex; atrial fibrillation; risk factor
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INTRODUCTION Atrial fibrillation (AF) is the most common arrhythmia in the elderly. Its prevalence is estimated
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at 8.6% among Medicare beneficiaries and doubles with each advancing decade of life.1, 2 With the median age of 64 years, veterans are at a significantly risk.3 AF is an independent risk factor for stroke and mortality.4,5 It is responsible for recurrent hospital admissions and constitutes a
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significant health care burden. Treatment comprising of rate and rhythm control strategies is fraught with medication related adverse effects. Furthermore, prophylactic anticoagulant therapy
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puts this vulnerable elderly population at risk for bleeding complications. Early prediction and treatment of precursors could potentially reduce the incidence of AF and related morbidity, mortality and health care costs. Atrial Premature Complexes (APCs) are commonly seen in healthy individuals.6 Though shown to trigger paroxysmal AF, they are, at present, considered innocuous and generally left untreated.7-9 Our hypothesis is that patients with frequent and
METHODS
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recurrent APCs are more likely to develop AF.
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VA Pacific Island Healthcare System Institutional Review Board and VA Central California Healthcare System (VACCHCS) Research and Development Committee approved
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this retrospective cohort study protocol. A database search was done to identify all veterans who had undergone Holter electrocardiographic (ECG) examination for any indication at VACCHCS between the years 2000 and 2010. Patients thus identified were evaluated by chart review in electronic medical record. GE MARS 7.2 © 2005 software was used to analyze the Holter data which was then verified by a VA staff cardiologist. Each report was systematically reviewed to evaluate for various parameters including predominant rhythm; minimum, maximum and
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average heart rate; total beats; atrial and ventricular premature complexes including isolated beats, couplets, bigeminy and beats in runs. Demographic information, co-morbid disease diagnoses, laboratory and echocardiographic data, and medication prescription information at the
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time of the Holter examination was manually collected via thorough chart review. Investigators cross-verified data from randomly selected patients to ensure accuracy.
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To identify patients free of AF at baseline, we reviewed problem lists, clinic notes,
admission, progress and discharge notes as well as emergency room visits and all available
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ECGs in the electronic medical records. Patients with a preexisting diagnosis of AF before the Holter ECG exam and those found to have new AF on the index Holter were excluded. Patients with atrial or ventricular pacing were excluded as well (Figure 1).
An atrial complex with less than 80% coupling interval to the preceding QRS, when
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compared to the mean RR interval, was defined as APC. There exists no consensus definition of frequent APCs in literature. Previously defined cut offs vary between studies. Therefore, a receiver operator curve analysis was performed on a random subset of the study population, with
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the aim of finding a reasonably accurate point of separation for APC frequency that will be most sensitive and specific to predict AF (Figure 2). Based on the analysis, frequent APCs were
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defined as 100 or more events over a 24 hour ECG monitoring period. This cut off number would yield a sensitivity and specificity of 77.8% and 75.8% respectively for predicting AF (area under curve = 0.83). Less than 100 APCs per 24 hours were defined as infrequent APCs. Frequent atrial couplets were defined as more than 50 couplets per day. Frequent runs of more than 3 APCs were defined as more than 20 runs per day. Long runs of APCs were defined as more than 10 beats per run.
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Disease diagnoses of any given patient are enumerated in the ‘problem list’ section of the VA electronic medical record system. Hypertension was defined by the problem list entry as well as any anti-hypertensive medication prescription. Diabetes mellitus was defined by the
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problem list entry, hemoglobin A1C ≥6.5% or insulin or oral hypoglycemic agent use. Coronary artery disease was defined by problem list entry, history of coronary artery bypass surgery or percutaneous coronary intervention. Problem list entries were used for the diagnosis of
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congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disease,
obstructive sleep apnea, hypothyroidism and hyperthyroidism. Moderate to severe left sided
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stenotic or regurgitant valvular lesions were defined as valvular heart disease. Left atrial enlargement was defined as a left atrial diameter greater than 3.8 cm in women or 4.0 cm in men.10
Patients were grouped according APC frequency as defined above. Baseline
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characteristics of patients with and without frequent APCs were compared using independent ttest or chi-squared test. Continuous variables were reported as mean ± standard deviation. Discrete variables were reported as number and percentage. Univariate regression analysis was
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conducted using known risk factors for AF (Age, hypertension, diabetes, coronary disease, heart
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failure, left atrial enlargement, diastolic and systolic dysfunction) to evaluate their individual predictive value. Those characteristics found to be significantly associated with AF were added in a step wise fashion to a Cox multivariate regression model to adjust for confounding. Following this analysis, a propensity score was derived with frequent APCs as the
dependent variable and age, left atrial enlargement, and hypertension as covariates. Patients were then matched using an optimal matching method.11 Baseline characteristics of the matched groups were re-evaluated. Cox proportional-hazards regression method was used to compare the 5
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two groups with respect to the onset of AF. Data were censored at the time AF identification or at the end of 13 year study period, whichever occurred first. Subsequently, subgroup analysis was done utilizing the above methods to look predictive value of frequent isolated atrial
runs of APCs in AF in the propensity matched cohort.
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complexes, frequent atrial couplets, frequent atrial bigeminy, frequent runs of ≥3 APCs and long
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Hazard ratio (HR) and 95% confidence interval (CI) were used to depict strength of
association. Two tailed tests for statistical significance were reported and P value of <0.05 was
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considered statistically significant. Kaplan Meier estimator (not shown) was used for univariate and Cox proportional hazards modeling for multivariate survival analysis. Statistical calculations were performed using SPSS 20 (IBM SPSS Statistics for Windows, Version 20.0, Armonk, NY) and R Statistical Software version 2.13.2 with MatchIt package.12
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Comparison groups were followed via comprehensive electronic medical record review over a maximum follow up of 13 years and a median follow up of 7.5 years. Systematic review consisted of hospital admission and discharge summaries, emergency room visits, outpatient
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RESULTS
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clinic notes and all available ECGs. The outcome of interest was incident AF.
A total of 1742 patients underwent Holter ECG examination for any indication between
the years 2000 and 2010. After excluding 385 subjects with previously documented AF, baseline data was collected for 1357 patients. Ninety three percent of the study population was male with a mean age of 64 years. Twenty two percent were smokers. Mean body mass index was 31.2 kg/m2, mean hemoglobin A1C was 6.4% and mean low-density lipoprotein level was 107.9 mg/dl. Concomitant hypertension and diabetes mellitus was seen in 66% and 23% patients 6
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respectively. Mean left atrial size was 4.2 cm and average heart rate over 24 hour ECG recording period was 73 beats per minute.
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Table 1 summarizes baseline characteristics. Significant differences existed among comparison groups. Patients in the frequent APC group were older with a mean age difference of 11 years. There were more males in the frequent APC group. Racial differences were present
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as well. Patients with frequent APCs had a higher mean maximum heart rate and a lower mean minimum heart rate. Hypertension, coronary artery disease, heart failure and obstructive
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pulmonary disease were more prevalent in the frequent APC group. More left atrial enlargement as well as diastolic dysfunction was seen in the study group as compared to the control group. AF was noted in 21.8% of subjects with frequent APCs compared to 5.6% of those with infrequent APCs (Figure 3). After adjusting for demographics, medication use, co-morbidities,
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lab and echocardiographic findings, multivariate Cox regression analyses confirmed frequent APCs to be independently associated with higher incidence of AF (HR, 2.97 [95% CI, 1.85 4.80]; P<0.001). When plotted against time (Figure 4a), AF free survival curves for the two
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groups separated out early. By the end of the study period, event rate was significantly higher in frequent APC group.
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After propensity score matching, each comparison group had 353 subjects. As expected,
age, sex, race, all measured comorbidities, habits and medications, left atrial enlargement, systolic and diastolic dysfunction were now comparable (Table 1). Despite attrition of subjects that did not match, the study was powered to answer the question at hand. Predictive value to APCs for AF remained fairly unchanged in the post propensity score matched cohort. AF incidence in frequent APC group was 22.9% when compared to 7.6% in the 7
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infrequent APC group . Multivariate Cox regression analysis showed that after adjusting for age, left atrial enlargement, and systolic dysfunction, frequent APCs remained independently associated with AF (HR, 2.87 [95% CI, 1.65 - 4.98]; P<0.001). Cox analysis was performed in
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326 cases that had complete data including echocardiographic parameters. Age remained a
significant independent predictor of AF (HR, 1.04 [95%CI 1.01 - 1.07]; P=0.012). The actuarial
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curves looked fairly similar before and after propensity score matching (Figure 4b).
Four hundred and fifty four patients were found to have frequent isolated atrial
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complexes, 124 had frequent atrial couplets, 16 had frequent atrial bigeminy, 70 had frequent runs of ≥3 APCs and 137 had long runs of APCs. In the post propensity matched cohort, such repetitive premature atrial activity was found to be associated with higher incidence of AF in univariate fashion (Table 2). However, when evaluated together in multivariate Cox regression model, only isolated atrial ectopic activity (≥100 beats/day) remained significantly associated
DISCUSSION
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(HR, 2.80 [95% CI, 1.77 - 4.41]; P<0.001).
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This study shows frequent APCs to be an independent risk factor for development of incident AF. Patients with frequent APCs (defined as ≥ 100 events per day) were found to have
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almost three times greater the risk of developing AF as compared to patients with lesser APCs. Predictive value of PACs was independent of established risk factors like hypertension, diabetes, heart failure and valvular heart disease.13 Although not the primary objective of this study, we also found repetitive atrial premature activity (couplets, bigeminy, runs of APCs) to be associated with new onset AF.
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APCs and AF are closely related. Majority of APCs originate from pulmonary veins; followed by right atrium and posterior left atrium.14 AF has been shown to originate from the same focal trigger points. Multiple studies in patients with paroxysmal AF have shown
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increased APC activity right before the onset of AF imputing them as likely triggers.7-9
Pulmonary vein isolation and ablation performed for treatment of AF has been shown to
significantly reduce APC frequency.14,15 In contrast, incomplete APC suppression in these
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patients was associated with higher AF recurrence. Additional observations in patients
undergoing cardioversion for AF have shown excessive APC frequency before cardioversion to
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be associated with higher rate of AF recurrence post cardioversion.16-18 It appears that not only do APCs predict new AF; they also act as a surrogate marker for existing paroxysmal AF. This explains increased risk of stroke in patients with frequent APCs in the absence of documented AF.19-20
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There is no universal agreement regarding the cut off number for APC to be considered frequent. Healthy population may require higher atrial ectopic burden to unmask AF. Binici et al showed a relationship similar to ours in the population based Copenhagen Holter cohort.21
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They evaluated 678 healthy men and women without significant cardiovascular disease and
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demonstrated that over a median follow up period of 6.3 years; those with excessive atrial ectopic activity had a higher rate of AF. They arrived at a much higher cutoff value by using the top 10th percentile for frequency of APCs and defined excessive APCs as ≥ 30 beats per hour (≥ 720 beats per day) or runs of 20 or more beats. Chong et al evaluated Holter ECGs performed in 428 symptomatic Chinese patients and found that those with more than 100 APCs per day had higher incidence of AF when followed over 6.1 years.22 They used top quartile of APC frequency as cut off for defining frequent APCs. By using receiver-operator curve analysis (as 9
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previously described, figure 2), we reached the same cut off number. This may be explained by the fact that the patients in Chong’s and our study were symptomatic adults. Both studies analyzed patients who got a 24 hour Holter examination to evaluate for complaints such as
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palpitations, dizziness, syncope, shortness of breath etc. It is important to realize that this
introduces selection bias to these studies. However, such symptomatic patients are more likely to be encountered in clinical practice. They also appear to be at a higher risk of developing overt
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AF with much lower atrial ectopic activity.
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To our knowledge, this is the largest study of its kind with the longest follow up period looking at this patient group. Furthermore, this is the first study looking at a relevant cohort of patients in the United States. Additionally, this is the first study to perform propensity score matching to balance the comparison groups. At baseline, patients with frequent APCs were significantly older, had more prevalent HTN and had a larger mean left atrial size. This grouping
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of known AF risk factors in the frequent APC group was seen across the above mentioned studies from three continents. This prompted us to apply more rigorous statistical methods ensure comparability of groups. We performed two separate multivariate regression analyses
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before and after propensity score matching and demonstrated that the relationship between APC
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and AF remained robust.
Although not our primary objective, we also looked at recurrent atrial ectopic activity and
found frequent atrial couplets, atrial bigeminy, frequent runs and long runs of APCs to be associated with increased risk of AF. However, due to small numbers in individual sub groups, only isolated APCs remained significantly associated with AF on multivariate analysis.
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These findings are clinically relevant as APCs are readily detectable on routine EKG, cardiac monitoring in hospitals and long term ECG monitoring devices. High-risk patients with frequent APCs need to be followed up with long term EKG monitoring on a regular basis. This
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could potentially unmask paroxysmal AF in significant number of patients.23,24 Additionally, these patients need aggressive risk factor modification in the form of blood pressure and diabetes
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control and adequate management of heart failure to prevent onset of AF.
This study has limitations. Being a retrospective analysis, it is hypothesis generating.
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These findings need to be validated in a large prospective cohort using a priori statistical design. This is single center experience looking at an elderly, predominantly male population with significant co-morbidities. These results may not be generalizable to other populations. Diagnosis of incident AF may be underestimated due to the many reasons. Follow-up Holter exams were not routinely performed. Though most veterans tend to stay within the VA system,
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it is possible that some patients were treated at other hospitals. Lastly, undiagnosed AF remains a possibility, especially in asymptomatic patients.
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DISCLOSURES
The authors have no conflicts of interest to report. The views expressed herein are those
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of the authors and do not reflect the official policy or position of the Department of Veterans Affairs.
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premature contractions as a surrogate marker for paroxysmal atrial fibrillation in patients
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FIGURE LEGENDS: Figure 1. Study design
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Figure 2. Receiver-operator curve analysis performed on random subset of 100 patients to define cut-off point for frequent APC activity
Figure 3. Graphic representation of atrial fibrillation incidence in comparison groups before and
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after propensity score matching
Figure 4. Cox multivariate survival curves showing incidence of atrial fibrillation when plotted
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against time in cohorts with frequent and infrequent APC activity a) before propensity score
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matching and b) after propensity score matching
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Table 1. Baseline Characteristics of Patients With and Without Frequent Atrial Premature Complexes. Frequent APC Frequent APC After Propensity Score Matching Variable Yes No P value Yes No (n = 486) (n = 871) (n = 353) (n = 353) Age, mean (years) 71.4 ± 11.8 60.3 ± 13.4 <0.001 67.9 ± 9.1 67.4 ± 8.9 Men 465 (95.7%) 796 (91.4%) 0.003 340 (96.7%) 340 (96.7%) Race 0.04 White 242 (49.8%) 415 (47.6%) 170 (48.2%) 172 (48.7%) Latino/Hispanic 57 (11.5%) 142 (16.3%) 47 (13.3%) 59 (16.7%) Black 20 (4.1%) 60 (6.9%) 13 (3.7%) 22 (6.2%) Asian 3 (0.6%) 8 (0.9%) 2 (0.6%) 3 (0.8%) American Indian 8 (1.6%) 8 (0.9%) 5 (1.4%) 1 (0.3%) Native Hawaiian 3 (0.6%) 4 (0.5%) 2 (0.6%) 2 (0.6%) Unreported 153 (31.5%) 234 (26.9%) 114 (32.3%) 94 (26.6%) 2 BMI (kg/m ) 28.4 ± 5.7 29.3 ± 6.4 0.02 29.3 ± 5.8 27.3 ± 5.5 Heart rate average (bpm) 70.7 ± 12.5 74.0 ± 12.0 <0.001 70.9 ± 12.7 71.5 ± 11.7 Heart rate maximum (bpm) 124.2 ± 65.7 116.9 ± 22.7 0.02 124.8 ± 75.6 111.6 ± 21.1 Heart rate minimum (bpm) 49.8 ± 12.7 52.0 ± 13.2 0.003 50.4 ± 13.2 51.5 ± 11.6 Hypertension 361 (74.3%) 534 (61.3%) <0.001 261 (73.9%) 241 (68.3%) Diabetes Mellitus 109 (22.4%) 198 (22.7%) 0.90 84 (23.8%) 89 (25.2%) Coronary artery disease 116 (23.9%) 151 (17.3%) 0.004 88 (24.9%) 83 (23.5%) Congestive heart failure 34 (7.0%) 35 (4.0%) 0.02 21 (5.9%) 20 (5.7%) Chronic kidney disease 27 (5.6%) 34 (3.9%) 0.16 18 (5.1%) 18 (5.1%) Chronic obstructive 66 (13.6%) 69 (7.9%) 0.001 44 (12.5%) 40 (11.3%) pulmonary disease Obstructive sleep apnea 14 (2.9%) 27 (3.1%) 0.82 11 (3.1%) 8 (2.3%) Hypothyroidism 33 (6.8%) 65 (7.5%) 0.65 23 (6.5%) 29 (8.2%) Hyperthyroidism 2 (0.4%) 2 (0.2%) 0.55 2 (0.6%) 1 (0.3%) Valvular Heart Disease 15 (3.1%) 23 (2.6%) 0.63 9 (2.5%) 17 (4.8%) Habits Smoking 91 (18.7%) 197 (22.6%) 0.09 73 (20.7%) 66 (18.7%) Alcoholism 53 (10.9%) 132 (15.2%) 0.03 41 (11.6%) 48 (13.6%) Illicit Drug use 17 (3.5%) 45 (5.2%) 0.16 14 (4%) 9 (2.5%) Medications Aspirin 164 (33.7%) 244 (28.0%) 0.03 111 (31.4%) 124 (35.1%) Thienopyridines 16 (3.3%) 17 (2.0%) 0.12 14 (4%) 9 (2.5%) Beta Blockers 203 (41.8%) 328 (37.7%) 0.14 147 (41.6%) 153 (43.3%) CCBs 73 (15.0%) 96 (11.0%) 0.03 52 (14.7%) 43 (12.2%) ACE Inhibitors 183 (37.7%) 257 (29.5%) 0.002 142 (40.2%) 121 (34.3%) ARBs 33 (6.8%) 27 (3.1%) 0.002 21 (5.9%) 13 (3.7%) Laboratory values Cholesterol 170.4 ± 38.2 185.1 ± 44.4 <0.001 170.9 ± 37.6 177.8 ± 42.5 Triglyceride 134.0 ± 111.4 174.8 ± 209.0 <0.001 142.8 ± 123.9 152.7 ± 129.8 High-density lipoprotein 42.0 ± 13.0 41.3 ± 12.9 0.45 40.6 ± 12.4 41.3 ± 11.2 Low-density lipoprotein 102.5 ± 33.6 111.3 ± 37.2 <0.001 102.7 ± 32.9 106.6 ± 38.0 Thyroid stimulating 2.1 ± 1.7 2.6 ± 8.4 0.22 1.9 ± 1.5 2.8 ± 9.1 hormone Hemoglobin A1C 6.3 ± 1.1 6.5 ± 1.6 0.02 6.3 ± 1.2 6.6 ± 1.6 ECHO parameters
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P value 0.41 1.00 0.21
<0.001 0.49 0.002 0.24 0.10 0.66 0.66 0.87 1.00 0.64
0.49 0.39 0.56 0.11 0.51 0.43 0.29 0.30 0.29 0.65 0.32 0.10 0.16 0.05 0.37 0.47 0.22 0.20 0.04
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136 (28.0%)
160 (18.4%)
174 (79.8%) 27 (12.4%) 7 (3.2%) 10 (4.6%) 132 (60.6%)
286 (82.9%) 34 (9.9%) 15 (4.3%) 10 (2.9%) 173 (50.1%)
<0.001 0.48
0.02
105 (29.7%)
97 (27.5%)
126 (79.7%) 21 (13.3%) 3 (1.9%) 8 (5.1%) 90 (57%)
140 (83.3%) 17 (10.1%) 7 (4.2%) 4 (2.4%) 95 (56.5%)
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Left Atrial Enlargement Systolic Dysfunction Absent Mild Moderate Severe Diastolic Dysfunction
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0.51 0.29
0.94
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P value <0.001
Relationship between recurrent Atrial Ectopy and incident Atrial Fibrillation Subgroup analysis After Propensity Score Matching Risk Factors for AF Hazard Ratio [95% CI] Univariate P value Multivariate Isolated APCs* 3.39 [2.22 - 5.17] <0.001 2.80 [1.77 - 4.41] Atrial Couplets† 3.11 [2.01 - 4.82] <0.001 1.60 [0.92 - 2.80] Atrial Bigeminy‡ 3.67 [1.16 - 11.60] 0.03 1.82 [0.56 - 5.92] Atrial Runs§ 2.94 [1.73 - 5.00] <0.001 1.32 [0.65 - 2.67] Long Atrial Runs¶ 1.73 [1.09 - 2.74] 0.02 1.03 [0.61 - 1.73]
P value <0.001 0.10 0.32 0.44 0.92
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ᶲ Defined as more than 100 beats per day * Defined as more than 100 beats per day † Defined as more than 50 beats per day ‡ Defined as more than 50 beats per day § Defined as more than 20 runs per day ¶ Defined as more than 20 beats per run
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Table 2. Results Relationship between frequent APCs and incident Atrial Fibrillation Risk Factor for AF Hazard Ratio [95% CI] Unmatched Propensity Score Matched Univariate Multivariate P value Univariate Multivariate Frequent APCsᶲ 4.34 [3.09–6.08] 2.97 [1.85-4.80] <0.001 3.37 [2.18–5.22] 2.87 [1.65 - 4.98]
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Patients with Holter study in 2000-2010 period n=1742
Study Group Frequent APC (>100/day) n=486
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ROC Analysis
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Excluded Previous diagnosis of AF or AF on index Holter study or Pacemaker for any indication n=385
Control Group Infrequent APC (>100/day) n=871
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Cox Regression Analysis 1
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Propensity Matching
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Study Group Frequent APC (>100/day) n=353
Figure 1. Study design
Control Group Infrequent APC (>100/day) n=353
Cox Regression Analysis 2
Criterion: APC ≥100/d Sensitivity: 77.8% Specificity: 75.8% Area under curve 0.83
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Figure 2. Receiver-operator curve analysis performed on random subset of 100 patients to define cut-off point for frequent APC activity most predictive of atrial fibrillation.
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Atrial Fibrillation
No Atrial Fibrillation
80% 70% 77.1
78.2 94.4
50% 40% 30% 20% 10%
21.8
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APC APC ≥100/d <100/d Non Propensity Matched 106/486 49/871
7.6
APC APC ≥100/d <100/d Propensity Matched 81/353 27/353
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AF
22.9
5.6
0%
92.4
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60%
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90%
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% Patients with new Atrial Fibrillation
100%
Figure 3. Graphic representation of atrial fibrillation incidence in comparison groups before and after propensity score matching.
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Infrequent APCs
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a)
b)
Frequent APCs
Infrequent APCs
Frequent APCs
Frequent APCs
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Figure 4. Cox multivariate survival curves showing incidence of atrial fibrillation when plotted against time in cohorts with frequent and infrequent APC activity a) before propensity score matching and b) after propensity score matching.
S0002-9149(15)02004-4
DOI:
10.1016/j.amjcard.2015.09.025
Reference:
AJC 21427
To appear in:
The American Journal of Cardiology
Received Date: 28 August 2015 Revised Date:
13 September 2015
Accepted Date: 15 September 2015
Please cite this article as: Acharya T, Tringali S, Bhullar M, Nalbandyan M, Ilineni VK, Carbajal E, Deedwania P, Frequent Atrial Premature Complexes and their Association with Risk of Atrial Fibrillation, The American Journal of Cardiology (2015), doi: 10.1016/j.amjcard.2015.09.025. 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.
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Frequent Atrial Premature Complexes and their Association with Risk of Atrial Fibrillation
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Tushar Acharya, MD1,2, Steven Tringali, DO2, Manminder Bhullar, MD1,2, Marta Nalbandyan, DO2, Vishnu Karthik Ilineni, MD2, Enrique Carbajal, MD1,2, Prakash Deedwania, MD1,2 Running title: Atrial Premature Complexes and Atrial Fibrillation
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Affiliation: 1. Division of Cardiology; 2. Department of Internal Medicine. Veterans
Francisco, Fresno, California Corresponding Author: Tushar Acharya, MD
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Fellow, Cardiovascular Disease
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Administration Central California Health Care System and University of California, San
University of California San Francisco, Fresno
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UCSF Fresno, Division of Cardiology Academic Offices
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2335 E Kashian Lane, Suite 460 Fresno, California 93721 Phone: (559) 246-9449 Fax: (559) 459- 5157
Email: [email protected]
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ABSTRACT Identification of precursors of Atrial Fibrillation (AF) may lead to early detection and prevent
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associated morbidity and mortality. This study aims to examine the association between frequent atrial premature complexes (APCs) and incidence of AF. For this retrospective cohort study, we analyzed Holter recordings obtained between 2000 and 2010 of 1357 veterans free of AF at
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baseline. All pertinent data in electronic medical records were reviewed to ascertain baseline characteristics. Holter groups with frequent (≥100/day) and infrequent (<100/day) APCs were
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compared for development of new AF over a median follow up of 7.5 years. Multivariate Cox regression analyses were performed before and after propensity score matching. Mean age was 64 years with 93% men. Mean body mass index, hemoglobin A1C, low density lipoprotein, left atrial size and heart rate were 31.24 kg/m2, 6.42%, 107.92 mg/dL, 4.26 cm and 73 beats per minute, respectively. AF was noted in 21.8% of patients with frequent APCs compared to 5.6%
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of those with infrequent APCs. After adjusting for demographics, medication use, comorbidities, lab and echocardiographic findings, multivariate Cox regression analyses confirmed frequent APCs to be independently associated with higher incidence of AF (HR, 2.97 [95% CI,
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1.85 - 4.80]; P<0.001). In propensity-matched groups, this association remained significant (HR,
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2.87 [95% CI, 1.65 - 4.98]; P<0.001). Additionally, atrial couplets (≥50/day), atrial bigeminy (≥50/day), frequent runs of ≥3 APCs (≥20 runs/day) as well as longer runs (≥10 beats/run) were significantly associated with AF (HR, 3.11, 3.67, 2.94 and 1.73 respectively, all P<0.05). In conclusion, frequent APCs (≥100/day) are associated with higher risk of AF.
Key words: atrial premature complex; atrial fibrillation; risk factor
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INTRODUCTION Atrial fibrillation (AF) is the most common arrhythmia in the elderly. Its prevalence is estimated
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at 8.6% among Medicare beneficiaries and doubles with each advancing decade of life.1, 2 With the median age of 64 years, veterans are at a significantly risk.3 AF is an independent risk factor for stroke and mortality.4,5 It is responsible for recurrent hospital admissions and constitutes a
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significant health care burden. Treatment comprising of rate and rhythm control strategies is fraught with medication related adverse effects. Furthermore, prophylactic anticoagulant therapy
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puts this vulnerable elderly population at risk for bleeding complications. Early prediction and treatment of precursors could potentially reduce the incidence of AF and related morbidity, mortality and health care costs. Atrial Premature Complexes (APCs) are commonly seen in healthy individuals.6 Though shown to trigger paroxysmal AF, they are, at present, considered innocuous and generally left untreated.7-9 Our hypothesis is that patients with frequent and
METHODS
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recurrent APCs are more likely to develop AF.
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VA Pacific Island Healthcare System Institutional Review Board and VA Central California Healthcare System (VACCHCS) Research and Development Committee approved
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this retrospective cohort study protocol. A database search was done to identify all veterans who had undergone Holter electrocardiographic (ECG) examination for any indication at VACCHCS between the years 2000 and 2010. Patients thus identified were evaluated by chart review in electronic medical record. GE MARS 7.2 © 2005 software was used to analyze the Holter data which was then verified by a VA staff cardiologist. Each report was systematically reviewed to evaluate for various parameters including predominant rhythm; minimum, maximum and
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average heart rate; total beats; atrial and ventricular premature complexes including isolated beats, couplets, bigeminy and beats in runs. Demographic information, co-morbid disease diagnoses, laboratory and echocardiographic data, and medication prescription information at the
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time of the Holter examination was manually collected via thorough chart review. Investigators cross-verified data from randomly selected patients to ensure accuracy.
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To identify patients free of AF at baseline, we reviewed problem lists, clinic notes,
admission, progress and discharge notes as well as emergency room visits and all available
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ECGs in the electronic medical records. Patients with a preexisting diagnosis of AF before the Holter ECG exam and those found to have new AF on the index Holter were excluded. Patients with atrial or ventricular pacing were excluded as well (Figure 1).
An atrial complex with less than 80% coupling interval to the preceding QRS, when
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compared to the mean RR interval, was defined as APC. There exists no consensus definition of frequent APCs in literature. Previously defined cut offs vary between studies. Therefore, a receiver operator curve analysis was performed on a random subset of the study population, with
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the aim of finding a reasonably accurate point of separation for APC frequency that will be most sensitive and specific to predict AF (Figure 2). Based on the analysis, frequent APCs were
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defined as 100 or more events over a 24 hour ECG monitoring period. This cut off number would yield a sensitivity and specificity of 77.8% and 75.8% respectively for predicting AF (area under curve = 0.83). Less than 100 APCs per 24 hours were defined as infrequent APCs. Frequent atrial couplets were defined as more than 50 couplets per day. Frequent runs of more than 3 APCs were defined as more than 20 runs per day. Long runs of APCs were defined as more than 10 beats per run.
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Disease diagnoses of any given patient are enumerated in the ‘problem list’ section of the VA electronic medical record system. Hypertension was defined by the problem list entry as well as any anti-hypertensive medication prescription. Diabetes mellitus was defined by the
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problem list entry, hemoglobin A1C ≥6.5% or insulin or oral hypoglycemic agent use. Coronary artery disease was defined by problem list entry, history of coronary artery bypass surgery or percutaneous coronary intervention. Problem list entries were used for the diagnosis of
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congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disease,
obstructive sleep apnea, hypothyroidism and hyperthyroidism. Moderate to severe left sided
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stenotic or regurgitant valvular lesions were defined as valvular heart disease. Left atrial enlargement was defined as a left atrial diameter greater than 3.8 cm in women or 4.0 cm in men.10
Patients were grouped according APC frequency as defined above. Baseline
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characteristics of patients with and without frequent APCs were compared using independent ttest or chi-squared test. Continuous variables were reported as mean ± standard deviation. Discrete variables were reported as number and percentage. Univariate regression analysis was
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conducted using known risk factors for AF (Age, hypertension, diabetes, coronary disease, heart
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failure, left atrial enlargement, diastolic and systolic dysfunction) to evaluate their individual predictive value. Those characteristics found to be significantly associated with AF were added in a step wise fashion to a Cox multivariate regression model to adjust for confounding. Following this analysis, a propensity score was derived with frequent APCs as the
dependent variable and age, left atrial enlargement, and hypertension as covariates. Patients were then matched using an optimal matching method.11 Baseline characteristics of the matched groups were re-evaluated. Cox proportional-hazards regression method was used to compare the 5
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two groups with respect to the onset of AF. Data were censored at the time AF identification or at the end of 13 year study period, whichever occurred first. Subsequently, subgroup analysis was done utilizing the above methods to look predictive value of frequent isolated atrial
runs of APCs in AF in the propensity matched cohort.
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complexes, frequent atrial couplets, frequent atrial bigeminy, frequent runs of ≥3 APCs and long
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Hazard ratio (HR) and 95% confidence interval (CI) were used to depict strength of
association. Two tailed tests for statistical significance were reported and P value of <0.05 was
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considered statistically significant. Kaplan Meier estimator (not shown) was used for univariate and Cox proportional hazards modeling for multivariate survival analysis. Statistical calculations were performed using SPSS 20 (IBM SPSS Statistics for Windows, Version 20.0, Armonk, NY) and R Statistical Software version 2.13.2 with MatchIt package.12
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Comparison groups were followed via comprehensive electronic medical record review over a maximum follow up of 13 years and a median follow up of 7.5 years. Systematic review consisted of hospital admission and discharge summaries, emergency room visits, outpatient
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RESULTS
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clinic notes and all available ECGs. The outcome of interest was incident AF.
A total of 1742 patients underwent Holter ECG examination for any indication between
the years 2000 and 2010. After excluding 385 subjects with previously documented AF, baseline data was collected for 1357 patients. Ninety three percent of the study population was male with a mean age of 64 years. Twenty two percent were smokers. Mean body mass index was 31.2 kg/m2, mean hemoglobin A1C was 6.4% and mean low-density lipoprotein level was 107.9 mg/dl. Concomitant hypertension and diabetes mellitus was seen in 66% and 23% patients 6
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respectively. Mean left atrial size was 4.2 cm and average heart rate over 24 hour ECG recording period was 73 beats per minute.
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Table 1 summarizes baseline characteristics. Significant differences existed among comparison groups. Patients in the frequent APC group were older with a mean age difference of 11 years. There were more males in the frequent APC group. Racial differences were present
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as well. Patients with frequent APCs had a higher mean maximum heart rate and a lower mean minimum heart rate. Hypertension, coronary artery disease, heart failure and obstructive
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pulmonary disease were more prevalent in the frequent APC group. More left atrial enlargement as well as diastolic dysfunction was seen in the study group as compared to the control group. AF was noted in 21.8% of subjects with frequent APCs compared to 5.6% of those with infrequent APCs (Figure 3). After adjusting for demographics, medication use, co-morbidities,
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lab and echocardiographic findings, multivariate Cox regression analyses confirmed frequent APCs to be independently associated with higher incidence of AF (HR, 2.97 [95% CI, 1.85 4.80]; P<0.001). When plotted against time (Figure 4a), AF free survival curves for the two
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groups separated out early. By the end of the study period, event rate was significantly higher in frequent APC group.
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After propensity score matching, each comparison group had 353 subjects. As expected,
age, sex, race, all measured comorbidities, habits and medications, left atrial enlargement, systolic and diastolic dysfunction were now comparable (Table 1). Despite attrition of subjects that did not match, the study was powered to answer the question at hand. Predictive value to APCs for AF remained fairly unchanged in the post propensity score matched cohort. AF incidence in frequent APC group was 22.9% when compared to 7.6% in the 7
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infrequent APC group . Multivariate Cox regression analysis showed that after adjusting for age, left atrial enlargement, and systolic dysfunction, frequent APCs remained independently associated with AF (HR, 2.87 [95% CI, 1.65 - 4.98]; P<0.001). Cox analysis was performed in
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326 cases that had complete data including echocardiographic parameters. Age remained a
significant independent predictor of AF (HR, 1.04 [95%CI 1.01 - 1.07]; P=0.012). The actuarial
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curves looked fairly similar before and after propensity score matching (Figure 4b).
Four hundred and fifty four patients were found to have frequent isolated atrial
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complexes, 124 had frequent atrial couplets, 16 had frequent atrial bigeminy, 70 had frequent runs of ≥3 APCs and 137 had long runs of APCs. In the post propensity matched cohort, such repetitive premature atrial activity was found to be associated with higher incidence of AF in univariate fashion (Table 2). However, when evaluated together in multivariate Cox regression model, only isolated atrial ectopic activity (≥100 beats/day) remained significantly associated
DISCUSSION
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(HR, 2.80 [95% CI, 1.77 - 4.41]; P<0.001).
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This study shows frequent APCs to be an independent risk factor for development of incident AF. Patients with frequent APCs (defined as ≥ 100 events per day) were found to have
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almost three times greater the risk of developing AF as compared to patients with lesser APCs. Predictive value of PACs was independent of established risk factors like hypertension, diabetes, heart failure and valvular heart disease.13 Although not the primary objective of this study, we also found repetitive atrial premature activity (couplets, bigeminy, runs of APCs) to be associated with new onset AF.
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APCs and AF are closely related. Majority of APCs originate from pulmonary veins; followed by right atrium and posterior left atrium.14 AF has been shown to originate from the same focal trigger points. Multiple studies in patients with paroxysmal AF have shown
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increased APC activity right before the onset of AF imputing them as likely triggers.7-9
Pulmonary vein isolation and ablation performed for treatment of AF has been shown to
significantly reduce APC frequency.14,15 In contrast, incomplete APC suppression in these
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patients was associated with higher AF recurrence. Additional observations in patients
undergoing cardioversion for AF have shown excessive APC frequency before cardioversion to
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be associated with higher rate of AF recurrence post cardioversion.16-18 It appears that not only do APCs predict new AF; they also act as a surrogate marker for existing paroxysmal AF. This explains increased risk of stroke in patients with frequent APCs in the absence of documented AF.19-20
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There is no universal agreement regarding the cut off number for APC to be considered frequent. Healthy population may require higher atrial ectopic burden to unmask AF. Binici et al showed a relationship similar to ours in the population based Copenhagen Holter cohort.21
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They evaluated 678 healthy men and women without significant cardiovascular disease and
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demonstrated that over a median follow up period of 6.3 years; those with excessive atrial ectopic activity had a higher rate of AF. They arrived at a much higher cutoff value by using the top 10th percentile for frequency of APCs and defined excessive APCs as ≥ 30 beats per hour (≥ 720 beats per day) or runs of 20 or more beats. Chong et al evaluated Holter ECGs performed in 428 symptomatic Chinese patients and found that those with more than 100 APCs per day had higher incidence of AF when followed over 6.1 years.22 They used top quartile of APC frequency as cut off for defining frequent APCs. By using receiver-operator curve analysis (as 9
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previously described, figure 2), we reached the same cut off number. This may be explained by the fact that the patients in Chong’s and our study were symptomatic adults. Both studies analyzed patients who got a 24 hour Holter examination to evaluate for complaints such as
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palpitations, dizziness, syncope, shortness of breath etc. It is important to realize that this
introduces selection bias to these studies. However, such symptomatic patients are more likely to be encountered in clinical practice. They also appear to be at a higher risk of developing overt
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AF with much lower atrial ectopic activity.
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To our knowledge, this is the largest study of its kind with the longest follow up period looking at this patient group. Furthermore, this is the first study looking at a relevant cohort of patients in the United States. Additionally, this is the first study to perform propensity score matching to balance the comparison groups. At baseline, patients with frequent APCs were significantly older, had more prevalent HTN and had a larger mean left atrial size. This grouping
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of known AF risk factors in the frequent APC group was seen across the above mentioned studies from three continents. This prompted us to apply more rigorous statistical methods ensure comparability of groups. We performed two separate multivariate regression analyses
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before and after propensity score matching and demonstrated that the relationship between APC
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and AF remained robust.
Although not our primary objective, we also looked at recurrent atrial ectopic activity and
found frequent atrial couplets, atrial bigeminy, frequent runs and long runs of APCs to be associated with increased risk of AF. However, due to small numbers in individual sub groups, only isolated APCs remained significantly associated with AF on multivariate analysis.
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These findings are clinically relevant as APCs are readily detectable on routine EKG, cardiac monitoring in hospitals and long term ECG monitoring devices. High-risk patients with frequent APCs need to be followed up with long term EKG monitoring on a regular basis. This
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could potentially unmask paroxysmal AF in significant number of patients.23,24 Additionally, these patients need aggressive risk factor modification in the form of blood pressure and diabetes
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control and adequate management of heart failure to prevent onset of AF.
This study has limitations. Being a retrospective analysis, it is hypothesis generating.
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These findings need to be validated in a large prospective cohort using a priori statistical design. This is single center experience looking at an elderly, predominantly male population with significant co-morbidities. These results may not be generalizable to other populations. Diagnosis of incident AF may be underestimated due to the many reasons. Follow-up Holter exams were not routinely performed. Though most veterans tend to stay within the VA system,
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it is possible that some patients were treated at other hospitals. Lastly, undiagnosed AF remains a possibility, especially in asymptomatic patients.
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DISCLOSURES
The authors have no conflicts of interest to report. The views expressed herein are those
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of the authors and do not reflect the official policy or position of the Department of Veterans Affairs.
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with acute ischemic stroke. Heart 2003;89:1247-1248.
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premature contractions as a surrogate marker for paroxysmal atrial fibrillation in patients
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FIGURE LEGENDS: Figure 1. Study design
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Figure 2. Receiver-operator curve analysis performed on random subset of 100 patients to define cut-off point for frequent APC activity
Figure 3. Graphic representation of atrial fibrillation incidence in comparison groups before and
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after propensity score matching
Figure 4. Cox multivariate survival curves showing incidence of atrial fibrillation when plotted
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against time in cohorts with frequent and infrequent APC activity a) before propensity score
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matching and b) after propensity score matching
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Table 1. Baseline Characteristics of Patients With and Without Frequent Atrial Premature Complexes. Frequent APC Frequent APC After Propensity Score Matching Variable Yes No P value Yes No (n = 486) (n = 871) (n = 353) (n = 353) Age, mean (years) 71.4 ± 11.8 60.3 ± 13.4 <0.001 67.9 ± 9.1 67.4 ± 8.9 Men 465 (95.7%) 796 (91.4%) 0.003 340 (96.7%) 340 (96.7%) Race 0.04 White 242 (49.8%) 415 (47.6%) 170 (48.2%) 172 (48.7%) Latino/Hispanic 57 (11.5%) 142 (16.3%) 47 (13.3%) 59 (16.7%) Black 20 (4.1%) 60 (6.9%) 13 (3.7%) 22 (6.2%) Asian 3 (0.6%) 8 (0.9%) 2 (0.6%) 3 (0.8%) American Indian 8 (1.6%) 8 (0.9%) 5 (1.4%) 1 (0.3%) Native Hawaiian 3 (0.6%) 4 (0.5%) 2 (0.6%) 2 (0.6%) Unreported 153 (31.5%) 234 (26.9%) 114 (32.3%) 94 (26.6%) 2 BMI (kg/m ) 28.4 ± 5.7 29.3 ± 6.4 0.02 29.3 ± 5.8 27.3 ± 5.5 Heart rate average (bpm) 70.7 ± 12.5 74.0 ± 12.0 <0.001 70.9 ± 12.7 71.5 ± 11.7 Heart rate maximum (bpm) 124.2 ± 65.7 116.9 ± 22.7 0.02 124.8 ± 75.6 111.6 ± 21.1 Heart rate minimum (bpm) 49.8 ± 12.7 52.0 ± 13.2 0.003 50.4 ± 13.2 51.5 ± 11.6 Hypertension 361 (74.3%) 534 (61.3%) <0.001 261 (73.9%) 241 (68.3%) Diabetes Mellitus 109 (22.4%) 198 (22.7%) 0.90 84 (23.8%) 89 (25.2%) Coronary artery disease 116 (23.9%) 151 (17.3%) 0.004 88 (24.9%) 83 (23.5%) Congestive heart failure 34 (7.0%) 35 (4.0%) 0.02 21 (5.9%) 20 (5.7%) Chronic kidney disease 27 (5.6%) 34 (3.9%) 0.16 18 (5.1%) 18 (5.1%) Chronic obstructive 66 (13.6%) 69 (7.9%) 0.001 44 (12.5%) 40 (11.3%) pulmonary disease Obstructive sleep apnea 14 (2.9%) 27 (3.1%) 0.82 11 (3.1%) 8 (2.3%) Hypothyroidism 33 (6.8%) 65 (7.5%) 0.65 23 (6.5%) 29 (8.2%) Hyperthyroidism 2 (0.4%) 2 (0.2%) 0.55 2 (0.6%) 1 (0.3%) Valvular Heart Disease 15 (3.1%) 23 (2.6%) 0.63 9 (2.5%) 17 (4.8%) Habits Smoking 91 (18.7%) 197 (22.6%) 0.09 73 (20.7%) 66 (18.7%) Alcoholism 53 (10.9%) 132 (15.2%) 0.03 41 (11.6%) 48 (13.6%) Illicit Drug use 17 (3.5%) 45 (5.2%) 0.16 14 (4%) 9 (2.5%) Medications Aspirin 164 (33.7%) 244 (28.0%) 0.03 111 (31.4%) 124 (35.1%) Thienopyridines 16 (3.3%) 17 (2.0%) 0.12 14 (4%) 9 (2.5%) Beta Blockers 203 (41.8%) 328 (37.7%) 0.14 147 (41.6%) 153 (43.3%) CCBs 73 (15.0%) 96 (11.0%) 0.03 52 (14.7%) 43 (12.2%) ACE Inhibitors 183 (37.7%) 257 (29.5%) 0.002 142 (40.2%) 121 (34.3%) ARBs 33 (6.8%) 27 (3.1%) 0.002 21 (5.9%) 13 (3.7%) Laboratory values Cholesterol 170.4 ± 38.2 185.1 ± 44.4 <0.001 170.9 ± 37.6 177.8 ± 42.5 Triglyceride 134.0 ± 111.4 174.8 ± 209.0 <0.001 142.8 ± 123.9 152.7 ± 129.8 High-density lipoprotein 42.0 ± 13.0 41.3 ± 12.9 0.45 40.6 ± 12.4 41.3 ± 11.2 Low-density lipoprotein 102.5 ± 33.6 111.3 ± 37.2 <0.001 102.7 ± 32.9 106.6 ± 38.0 Thyroid stimulating 2.1 ± 1.7 2.6 ± 8.4 0.22 1.9 ± 1.5 2.8 ± 9.1 hormone Hemoglobin A1C 6.3 ± 1.1 6.5 ± 1.6 0.02 6.3 ± 1.2 6.6 ± 1.6 ECHO parameters
1
P value 0.41 1.00 0.21
<0.001 0.49 0.002 0.24 0.10 0.66 0.66 0.87 1.00 0.64
0.49 0.39 0.56 0.11 0.51 0.43 0.29 0.30 0.29 0.65 0.32 0.10 0.16 0.05 0.37 0.47 0.22 0.20 0.04
ACCEPTED MANUSCRIPT
136 (28.0%)
160 (18.4%)
174 (79.8%) 27 (12.4%) 7 (3.2%) 10 (4.6%) 132 (60.6%)
286 (82.9%) 34 (9.9%) 15 (4.3%) 10 (2.9%) 173 (50.1%)
<0.001 0.48
0.02
105 (29.7%)
97 (27.5%)
126 (79.7%) 21 (13.3%) 3 (1.9%) 8 (5.1%) 90 (57%)
140 (83.3%) 17 (10.1%) 7 (4.2%) 4 (2.4%) 95 (56.5%)
AC C
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Left Atrial Enlargement Systolic Dysfunction Absent Mild Moderate Severe Diastolic Dysfunction
2
0.51 0.29
0.94
ACCEPTED MANUSCRIPT
P value <0.001
Relationship between recurrent Atrial Ectopy and incident Atrial Fibrillation Subgroup analysis After Propensity Score Matching Risk Factors for AF Hazard Ratio [95% CI] Univariate P value Multivariate Isolated APCs* 3.39 [2.22 - 5.17] <0.001 2.80 [1.77 - 4.41] Atrial Couplets† 3.11 [2.01 - 4.82] <0.001 1.60 [0.92 - 2.80] Atrial Bigeminy‡ 3.67 [1.16 - 11.60] 0.03 1.82 [0.56 - 5.92] Atrial Runs§ 2.94 [1.73 - 5.00] <0.001 1.32 [0.65 - 2.67] Long Atrial Runs¶ 1.73 [1.09 - 2.74] 0.02 1.03 [0.61 - 1.73]
P value <0.001 0.10 0.32 0.44 0.92
AC C
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TE D
ᶲ Defined as more than 100 beats per day * Defined as more than 100 beats per day † Defined as more than 50 beats per day ‡ Defined as more than 50 beats per day § Defined as more than 20 runs per day ¶ Defined as more than 20 beats per run
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Table 2. Results Relationship between frequent APCs and incident Atrial Fibrillation Risk Factor for AF Hazard Ratio [95% CI] Unmatched Propensity Score Matched Univariate Multivariate P value Univariate Multivariate Frequent APCsᶲ 4.34 [3.09–6.08] 2.97 [1.85-4.80] <0.001 3.37 [2.18–5.22] 2.87 [1.65 - 4.98]
1
ACCEPTED MANUSCRIPT
Patients with Holter study in 2000-2010 period n=1742
Study Group Frequent APC (>100/day) n=486
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ROC Analysis
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Excluded Previous diagnosis of AF or AF on index Holter study or Pacemaker for any indication n=385
Control Group Infrequent APC (>100/day) n=871
TE D
Cox Regression Analysis 1
EP
Propensity Matching
AC C
Study Group Frequent APC (>100/day) n=353
Figure 1. Study design
Control Group Infrequent APC (>100/day) n=353
Cox Regression Analysis 2
Criterion: APC ≥100/d Sensitivity: 77.8% Specificity: 75.8% Area under curve 0.83
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ACCEPTED MANUSCRIPT
Figure 2. Receiver-operator curve analysis performed on random subset of 100 patients to define cut-off point for frequent APC activity most predictive of atrial fibrillation.
ACCEPTED MANUSCRIPT
Atrial Fibrillation
No Atrial Fibrillation
80% 70% 77.1
78.2 94.4
50% 40% 30% 20% 10%
21.8
TE D
APC APC ≥100/d <100/d Non Propensity Matched 106/486 49/871
7.6
APC APC ≥100/d <100/d Propensity Matched 81/353 27/353
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AF
22.9
5.6
0%
92.4
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60%
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90%
M AN U
% Patients with new Atrial Fibrillation
100%
Figure 3. Graphic representation of atrial fibrillation incidence in comparison groups before and after propensity score matching.
ACCEPTED MANUSCRIPT
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Infrequent APCs
AC C
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a)
b)
Frequent APCs
Infrequent APCs
Frequent APCs
Frequent APCs
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ACCEPTED MANUSCRIPT
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Figure 4. Cox multivariate survival curves showing incidence of atrial fibrillation when plotted against time in cohorts with frequent and infrequent APC activity a) before propensity score matching and b) after propensity score matching.