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Background Consumer wearable devices with health and wellness features are increasingly common and may enhance disease detection and management. Yet studies informing relationships between wearable device use, attitudes toward device data, and comprehensive clinical profiles are lacking. Methods and Results WATCH‐IT (Wearable Activity Tracking for Comprehensive Healthcare‐Integrated Technology) studied adults receiving longitudinal primary or ambulatory cardiovascular care in the Mass General Brigham health care system from January 2010 to July 2021. Participants completed a 20‐question electronic survey about perceptions and use of consumer wearable devices, with responses linked to electronic health records. Multivariable logistic regression was used to identify factors associated with device use. Among 214 992 individuals receiving longitudinal primary or cardiovascular care with an active electronic portal, 11 121 responded (5.2%). Most respondents (55.8%) currently used a wearable device, and most nonusers (95.3%) would use a wearable if provided at no cost. Although most users (70.2%) had not shared device data with their doctor previously, most believed it would be very (20.4%) or moderately (34.4%) important to share device‐related health information with providers. In multivariable models, older age (odds ratio [OR], 0.80 per 10‐year increase [95% CI, 0.77–0.82]), male sex (OR, 0.87 [95% CI, 0.80–0.95]), and heart failure (OR, 0.75 [95% CI, 0.63–0.89]) were associated with lower odds of wearable device use, whereas higher median income (OR, 1.08 per 1‐quartile increase [95% CI, 1.04–1.12]) and care in a cardiovascular medicine clinic (OR, 1.17 [95% CI, 1.05–1.30]) were associated with greater odds of device use. Conclusions Among patients in primary and cardiovascular medicine clinics, consumer wearable device use is common, and most users perceive value in wearable health data.

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

Genetic cardiomyopathies are associated with increased risk for cardiac arrhythmias and sudden cardiac death. The management of ventricular arrhythmias (VAs) in patients with these conditions can be nuanced due to particular disease-based considerations, yet data specifically addressing management in these patients are limited. Here we describe the current evidence-based approach to the management of ventricular rhythm disorders in patients with genetic forms of cardiomyopathy, namely, hypertrophic cardiomyopathy, arrhythmogenic cardiomyopathy, left ventricular noncompaction, and Brugada syndrome, including recommendations from consensus guideline statements when available.

Abstract Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure. Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome‐wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow‐up following heart failure diagnosis ranged from 2 to 116 months. Forty‐nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34–90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low‐frequency variants (allele frequency 0.01–0.05) at P

Background: Telemedicine and commercial wearable devices capable of detecting atrial fibrillation (AF) have revolutionized arrhythmia care during coronavirus disease 2019. However, not much is known about virtual patient-provider interactions or device sharing behaviors. Objective: The purpose of this study was to characterize how participants with or at risk of AF are engaging with their providers in the context of telemedicine and using commercially wearable devices to manage their health. Methods: We developed a survey to describe participant behaviors around telemedicine encounters and commercial wearable device use. The survey was distributed to participants diagnosed with AF or those at risk of AF (as determined by being at least 65 years old and having a CHA2DS2-VASc stroke risk score of >2) in the University of Massachusetts Memorial Health Care system. Results: The survey was distributed to 23,530 patients, and there were 1222 (5.19%) participant responses. Among the participants, 327 (26.8%) had AF and 895 (73.2%) were at risk of AF. Neither device ownership nor device type use differed by AF status. After adjusting for covariates that may influence surveyed participant communication patterns, we found that participants with AF were more likely to share their wearable device-derived data with providers (adjusted odds ratio 1.87; 95% confidence interval 1.02–3.41). Rates of sharing physical activity or sleep data were low for both groups and did not differ by AF status. Conclusion: Compared with participants at risk of developing AF, those with AF were more likely to share heart rate and rhythm data from their commercial wearable devices with providers. However, both groups had similar rates of sharing physical activity and sleep data, telemedicine engagement, and technology use and ownership.

Abstract This international multidisciplinary document intends to provide clinicians with evidence‐based practical patient‐centered recommendations for evaluating patients and decedents with (aborted) sudden cardiac arrest and their families. The document includes a framework for the investigation of the family allowing steps to be taken, should an inherited condition be found, to minimize further events in affected relatives. Integral to the process is counseling of the patients and families, not only because of the emotionally charged subject, but because finding (or not finding) the cause of the arrest may influence management of family members. The formation of multidisciplinary teams is essential to provide a complete service to the patients and their families, and the varied expertise of the writing committee was formulated to reflect this need. The document sections were divided up and drafted by the writing committee members according to their expertise. The recommendations represent the consensus opinion of the entire writing committee, graded by Class of Recommendation and Level of Evidence. The recommendations were opened for public comment and reviewed by the relevant scientific and clinical document committees of the Asia Pacific Heart Rhythm Society (APHRS) and the Heart Rhythm Society (HRS); the document underwent external review and endorsement by the partner and collaborating societies. While the recommendations are for optimal care, it is recognized that not all resources will be available to all clinicians. Nevertheless, this document articulates the evaluation that the clinician should aspire to provide for patients with sudden cardiac arrest, decedents with sudden unexplained death, and their families.

Background Models predicting atrial fibrillation (AF) risk, such as Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE‐AF), have not performed as well in electronic health records. Natural language processing (NLP) may improve models by using narrative electronic health record text. Methods and Results From a primary care network, we included patients aged ≥65 years with visits between 2003 and 2013 in development (n=32 960) and internal validation cohorts (n=13 992). An external validation cohort from a separate network from 2015 to 2020 included 39 051 patients. Model features were defined using electronic health record codified data and narrative data with NLP. We developed 2 models to predict 5‐year AF incidence using (1) codified+NLP data and (2) codified data only and evaluated model performance. The analysis included 2839 incident AF cases in the development cohort and 1057 and 2226 cases in internal and external validation cohorts, respectively. The C‐statistic was greater (P

Background: Cardiac magnetic resonance (CMR) is the gold standard for left ventricular hypertrophy (LVH) diagnosis. CMR-derived LV mass can be estimated using proprietary algorithms (eg, InlineVF), but their accuracy and availability may be limited. Objective: To develop an open-source deep learning model to estimate CMR-derived LV mass. Methods: Within participants of the UK Biobank prospective cohort undergoing CMR, we trained 2 convolutional neural networks to estimate LV mass. The first (ML4Hreg) performed regression informed by manually labeled LV mass (available in 5065 individuals), while the second (ML4Hseg) performed LV segmentation informed by InlineVF (version D13A) contours. We compared ML4Hreg, ML4Hseg, and InlineVF against manually labeled LV mass within an independent holdout set using Pearson correlation and mean absolute error (MAE). We assessed associations between CMR-derived LVH and prevalent cardiovascular disease using logistic regression adjusted for age and sex. Results: We generated CMR-derived LV mass estimates within 38,574 individuals. Among 891 individuals in the holdout set, ML4Hseg reproduced manually labeled LV mass more accurately (r = 0.864, 95% confidence interval [CI] 0.847–0.880; MAE 10.41 g, 95% CI 9.82–10.99) than ML4Hreg (r = 0.843, 95% CI 0.823–0.861; MAE 10.51, 95% CI 9.86–11.15, P = .01) and InlineVF (r = 0.795, 95% CI 0.770–0.818; MAE 14.30, 95% CI 13.46–11.01, P < .01). LVH defined using ML4Hseg demonstrated the strongest associations with hypertension (odds ratio 2.76, 95% CI 2.51–3.04), atrial fibrillation (1.75, 95% CI 1.37–2.20), and heart failure (4.67, 95% CI 3.28–6.49). Conclusions: ML4Hseg is an open-source deep learning model providing automated quantification of CMR-derived LV mass. Deep learning models characterizing cardiac structure may facilitate broad cardiovascular discovery.

Background: Many digital health technologies capable of atrial fibrillation (AF) detection are directly available to patients. However, adaptation into clinical practice by heart rhythm healthcare practitioners (HCPs) is unclear. Objective: To examine HCP perspectives on use of commercial technologies for AF detection and management. Methods: We created an electronic survey for HCPs assessing practice demographics and perspectives on digital devices for AF detection and management. The survey was distributed electronically to all members of 3 heart rhythm professional societies. Results: We received 1601 responses out of 73,563 e-mails sent, with 43.6% from cardiac electrophysiologists, 12.8% from fellows, and 11.6% from advanced practice practitioners. Most respondents (62.3%) reported having recommended patient use of a digital device for AF detection. Those who did not had concerns about their accuracy (29.6%), clinical utility of results (22.8%), and integration into electronic health records (19.8%). Results from a 30-second single-lead electrocardiogram were sufficient for 42.7% of HCPs to recommend oral anticoagulation for patients at high risk for stroke. Respondents wanted more data comparing the accuracy of digital devices to conventional devices for AF monitoring (64.9%). A quarter (27.3%) of HCPs had no reservations recommending digital devices for AF detection, and most (53.4%) wanted guidelines from their professional societies providing guidance on their optimal use. Conclusion: Many HCPs have already integrated digital devices into their clinical practice. However, HCPs reported facing challenges when using digital technologies for AF detection, and professional society recommendations on their use are needed.

Background Atrial fibrillation (AF) commonly occurs in the setting of acute conditions. We aimed to identify the acute conditions associated with secondary AF (AF precipitants) including pneumonia/sepsis, pneumothorax, respiratory failure, myocarditis, pericarditis, alcohol intoxication, thyrotoxicosis, cardiothoracic surgery, other surgery in patients with newly diagnosed AF and determine their association with subsequent oral anticoagulant use. Methods and Results We assembled a cohort of patients in the UMass Memorial Healthcare system with a new diagnosis of AF with and without AF precipitants. We used combinations of International Classification of Diseases, Tenth Revision (ICD‐10) codes, Current Procedural Terminology codes, laboratory values, imaging reports, and physician notes including discharge summary texts to identify AF precipitants. We then manually reviewed the individual charts to validate presence of AF precipitants. The study sample consisted of 185 patients with and 172 patients without AF precipitants. Pneumonia/sepsis, myocardial infarction, respiratory failure, and cardiothoracic surgery were the most common precipitants identified. In multivariable analyses adjusting for age, sex, patient comorbidities, left atrial enlargement, left ventricular ejection fraction, and antiplatelet use, patients with AF precipitants were less likely to receive subsequent anticoagulation therapy at 30 days after the initial AF diagnosis (odds ratio, 0.31; 95% CI, 0.19–0.52). The association was persistent after excluding men with CHA2DS2‐VASc score

Background Performance of existing atrial fibrillation (AF) risk prediction models in poststroke populations is unclear. We evaluated predictive utility of an AF risk model in patients with acute stroke and assessed performance of a fully refitted model. Methods and Results Within an academic hospital, we included patients aged 46 to 94 years discharged for acute ischemic stroke between 2003 and 2018. We estimated 5‐year predicted probabilities of AF using the Cohorts for Heart and Aging Research in Genomic Epidemiology for Atrial Fibrillation (CHARGE‐AF) model, by recalibrating CHARGE‐AF to the baseline risk of the sample, and by fully refitting a Cox proportional hazards model to the stroke sample (Re‐CHARGE‐AF) model. We compared discrimination and calibration between models and used 200 bootstrap samples for optimism‐adjusted measures. Among 551 patients with acute stroke, there were 70 incident AF events over 5 years (cumulative incidence, 15.2%; 95% CI, 10.6%–19.5%). Median predicted 5‐year risk from CHARGE‐AF was 4.8% (quartile 1–quartile 3, 2.0–12.6) and from Re‐CHARGE‐AF was 16.1% (quartile 1–quartile 3, 8.0–26.2). For CHARGE‐AF, discrimination was moderate (C statistic, 0.64; 95% CI, 0.57–0.70) and calibration was poor, underestimating AF risk (Greenwood‐Nam D’Agostino chi‐square, P

Background Atrial fibrillation (AF) screening is endorsed by certain guidelines for individuals aged ≥65 years. Yet many AF screening strategies exist, including the use of wrist‐worn wearable devices, and their comparative effectiveness is not well‐understood. Methods and Results We developed a decision‐analytic model simulating 50 million individuals with an age, sex, and comorbidity profile matching the United States population aged ≥65 years (ie, with a guideline‐based AF screening indication). We modeled no screening, in addition to 45 distinct AF screening strategies (comprising different modalities and screening intervals), each initiated at a clinical encounter. The primary effectiveness measure was quality‐adjusted life‐years, with incident stroke and major bleeding as secondary measures. We defined continuous or nearly continuous modalities as those capable of monitoring beyond a single time‐point (eg, patch monitor), and discrete modalities as those capable of only instantaneous AF detection (eg, 12‐lead ECG). In total, 10 AF screening strategies were effective compared with no screening (300–1500 quality‐adjusted life‐years gained/100 000 individuals screened). Nine (90%) effective strategies involved use of a continuous or nearly continuous modality such as patch monitor or wrist‐worn wearable device, whereas 1 (10%) relied on discrete modalities alone. Effective strategies reduced stroke incidence (number needed to screen to prevent a stroke: 3087–4445) but increased major bleeding (number needed to screen to cause a major bleed: 1815–4049) and intracranial hemorrhage (number needed to screen to cause intracranial hemorrhage: 7693–16 950). The test specificity was a highly influential model parameter on screening effectiveness. Conclusions When modeled from a clinician‐directed perspective, the comparative effectiveness of population‐based AF screening varies substantially upon the specific strategy used. Future screening interventions and guidelines should consider the relative effectiveness of specific AF screening strategies.

Background Optimal management of asymptomatic Brugada syndrome (BrS) with spontaneous type I electrocardiographic pattern is uncertain. Methods and Results We developed an individual‐level simulation comprising 2 000 000 average‐risk individuals with asymptomatic BrS and spontaneous type I electrocardiographic pattern. We compared (1) observation, (2) electrophysiologic study (EPS)‐guided implantable cardioverter‐defibrillator (ICD), and (3) upfront ICD, each using either subcutaneous or transvenous ICD, resulting in 6 strategies tested. The primary outcome was quality‐adjusted life years (QALYs), with cardiac deaths (arrest or procedural‐related) as a secondary outcome. We varied BrS diagnosis age and underlying arrest rate. We assessed cost‐effectiveness at $100 000/QALY. Compared with observation, EPS‐guided subcutaneous ICD resulted in 0.35 QALY gain/individual and 4130 cardiac deaths avoided/100 000 individuals, and EPS‐guided transvenous ICD resulted in 0.26 QALY gain and 3390 cardiac deaths avoided. Compared with observation, upfront ICD reduced cardiac deaths by a greater margin (subcutaneous ICD, 8950; transvenous ICD, 6050), but only subcutaneous ICD improved QALYs (subcutaneous ICD, 0.25 QALY gain; transvenous ICD, 0.01 QALY loss), and complications were higher. ICD‐based strategies were more effective at younger ages and higher arrest rates (eg, using subcutaneous devices, upfront ICD was the most effective strategy at ages 20–39.4 years and arrest rates >1.37%/year; EPS‐guided ICD was the most effective strategy at ages 39.5–51.3 years and arrest rates 0.47%–1.37%/year, and observation was the most effective strategy at ages >51.3 years and arrest rates

Background Frailty is associated bidirectionally with cardiovascular disease. However, the relations between frailty and atrial fibrillation (AF) have not been fully elucidated. Methods and Results Using the FHS (Framingham Heart Study) Offspring cohort, we sought to examine both the association between frailty (2005–2008) and incident AF through 2016 and the association between prevalent AF and frailty status (2011–2014). Frailty was defined using the Fried phenotype. Models adjusted for age, sex, and smoking. Cox proportional hazards models, adjusted for competing risk of death, assessed the association between prevalent frailty and incident AF. Logistic regression models assessed the association between prevalent AF and new‐onset frailty. For the incident AF analysis, we included 2053 participants (56% women; mean age, 69.7±6.9 years). By Fried criteria, 1018 (50%) were robust, 903 (44%) were prefrail, and 132 (6%) were frail. In total, 306 incident cases of AF occurred during an average 9.2 (SD, 3.1) follow‐up years. After adjustment, there was no statistically significant association between prevalent frailty status and incident AF (prefrail versus robust: hazard ratio [HR], 1.22 [95% CI, 0.95–1.55]; frail versus robust: HR, 0.92 [95% CI, 0.57–1.47]). At follow‐up, there were 111 new cases of frailty. After adjustment, there was no statistically significant association between prevalent AF and new‐onset frailty (odds ratio, 0.48 [95% CI, 0.17–1.36]). Conclusions Although a bidirectional association between frailty and cardiovascular disease has been suggested, we did not find evidence of an association between frailty and AF. Our findings may be limited by sample size and should be further explored in other populations.

Background It remains unclear whether heritable factors can contribute to risk stratification for ischemic stroke in patients with atrial fibrillation (AF). We examined whether having a sibling with ischemic stroke was associated with increased risk of ischemic stroke and mortality in patients with AF. Methods and Results In this nationwide study of the Swedish population, patients with AF and their siblings were identified from the Swedish patient registers and the Swedish MGR (Multi‐Generation Register). Ischemic stroke events were retrieved from the Swedish patient registers and CDR (Cause of Death Register). Risk of ischemic stroke was compared between patients with AF with and without a sibling affected by ischemic stroke, AF, or both ischemic stroke and AF. The total study population comprised 113 988 subjects (mean age, 60±12 years) diagnosed with AF between 1989 and 2012. In total, 11 709 of them were diagnosed with a first ischemic stroke and 20 097 died during a mean follow‐up time of 5.5 years for ischemic stroke and 5.9 years for mortality. After adjustment for covariates having a sibling with ischemic stroke, or both ischemic stroke and AF, was associated with increased risk of ischemic stroke (hazard ratio, 1.31; 95% CI, 1.23–1.40 or hazard ratio, 1.36; 95% CI, 1.24–1.49, respectively). Furthermore, ischemic stroke in a sibling was associated with all‐cause mortality (hazard ratio, 1.09; 95% CI, 1.05–1.14). In contrast, the risk of stroke was only marginally increased for patients with AF with a spouse affected by ischemic stroke. Conclusions Having a sibling affected by ischemic stroke confers an increased risk of ischemic stroke and death independently of traditional risk factors in patients with AF.

Background Variability in the management of atrial fibrillation (AF) in the emergency department (ED) leads to avoidable hospital admissions and prolonged length of stay (LOS). In a retrospective single‐center study, a multidisciplinary AF treatment pathway was associated with a reduced hospital admission rate and reduced LOS. To assess the applicability of the AF pathway across institutions, we conducted a 2‐center study. Methods and Results We performed a prospective, 2‐stage study at 2 tertiary care hospitals. During the first stage, AF patients in the ED received routine care. During the second stage, AF patients received care according to the AF pathway. The primary study outcome was hospital admission rate. Secondary outcomes included ED LOS and inpatient LOS. We enrolled 104 consecutive patients in each stage. Patients treated using the AF pathway were admitted to the hospital less frequently than patients who received routine care (15% versus 55%; P

Background Previous studies assessing the association between body mass index (BMI) and atrial fibrillation (AF) did not account for time‐varying covariates, which may be affected by previous BMI. We illustrate how the g‐formula can account for time‐varying confounding. Methods and Results We included 4392 participants from the Framingham Heart Study who were AF free at ages 45 to 55 years, and followed them for up to 20 years. We estimated hazard ratios (HRs) comparing time‐varying nonobese versus obese with Cox models. We used the g‐formula to compare nonobese versus obese and 10% annual decrease in BMI (until normal weight is reached) versus natural course. We estimated HRs and differences in restricted mean survival times, the mean difference in time alive and AF free. We adjusted for sex, age, and time‐varying risk factors. Cox models indicated that nonobese participants had a decreased rate of AF versus obese participants (HR, 0.83; 95% CI, 0.72–0.97). G‐formula analyses comparing everyone had they been nonobese versus obese yielded stronger associations (HR, 0.73; 95% CI, 0.58–0.91). The restricted mean survival time was 19.22 years had everyone been nonobese and 19.03 years had everyone been obese (difference, 2.25 months; 95% CI, −0.66 to 5.16). When assessing a 10% annual decrease in BMI, the association was weaker (HR 0.96; 95% CI, 0.86–1.08). Conclusions Decreased BMI was associated with a lower rate of AF after accounting for time‐varying covariates that depend on previous exposure using the g‐formula, which Cox models cannot accommodate. Absolute measures like the restricted mean survival time difference offer context to relative measures of association.

Background Prior studies relating proteomics markers to incident AF screened for limited numbers of proteins. Methods and Results We performed proteomics assays among participants from the Framingham Heart Study Offspring attending their fifth examination. Plasma protein levels (n=1373) were measured by the SOMAscan proteomic profiling platform. We used robust inference for the Cox proportional hazards model to relate each protein level with incident AF. In addition, we examined the association between AF‐related genetic loci and levels of proteins associated with AF. Our study included 1885 participants (mean age 55±10 years, 54% women) who had proteomic profiles measured. A total of 349 participants developed AF during follow‐up (mean follow‐up 18.3 years). We observed that 8 proteins were significantly associated with incident AF after adjusting for age, sex, technical covariates, and correction for multiple testing (P

Background Only 50% of eligible atrial fibrillation (AF) patients receive anticoagulation (AC). Feasibility and effectiveness of electronic medical record (EMR)–based interventions to profile and raise provider AC percentage is poorly understood. The SUPPORT‐AF (Supporting Use of AC Through Provider Profiling of Oral AC Therapy for AF) study aims to improve rates of adherence to AC guidelines by developing and delivering supportive tools based on the EMR to providers treating patients with AF. Methods and Results We emailed cardiologists and community‐based primary care providers affiliated with our institution reports of their AC percentage relative to peers. We also sent an electronic medical record–based message to these providers the day before an appointment with an atrial fibrillation patient who was eligible but not receiving AC. The electronic medical record message asked the provider to discuss AC with the patient if he or she deemed it appropriate. To assess feasibility, we tracked provider review of our correspondence. We also tracked the change in AC for intervention providers relative to alternate primary care providers not receiving our intervention. We identified 3786, 1054, and 566 patients cared for by 49 cardiology providers, 90 community‐based primary care providers, and 88 control providers, respectively. At baseline, the percentage of AC was 71.3%, 63.5%, and 58.3% for these 3 respective groups. Intervention providers reviewed our e‐mails and electronic medical record messages 45% and 96% of the time, respectively. For providers responding, patient refusal was the most common reason for patients not being on AC (21%) followed by high bleeding risk (19%). At follow‐up 10 weeks later, change in AC was no different for either cardiology or community‐based primary care providers relative to controls (0.2% lower and 0.01% higher, respectively). Conclusions Our intervention profiling AC was feasible, but not sufficient to increase AC in our population.

BackgroundAdvancing age is a prominent risk factor for atrial fibrillation (AF). Shorter telomere length is a biomarker of biological aging, but the link between shorter telomere length and increased risk of AF remains unclear. We examined the association between shorter leukocyte telomere length (LTL) and incident AF. Methods and ResultsWe included AF‐free participants from the observational Framingham Heart Study Offspring cohort from 1995 to 1998, who had LTL measurements. We examined the association between baseline LTL and incident AF with multivariable Cox models adjusted for age, sex, current smoking, height, weight, systolic and diastolic blood pressure, use of antihypertensive medication, diabetes mellitus, history of myocardial infarction, and history of heart failure. The study sample comprised 1143 AF‐free participants (52.8% women), with mean age of 60±8 years. The mean LTL at baseline was 6.95±0.57 kb. During 15.1±4.2 years mean follow‐up, 184 participants (64 women) developed AF. Chronological age was associated with increased risk of AF (hazard ratio per 10‐year increase, 2.16; 95% confidence interval, 1.71–2.72). There was no significant association between LTL and incident AF (hazard ratio per 1 SD decrease LTL, 1.01; 95% confidence interval, 0.86–1.19). Our study was observational in nature; hence, we could not exclude residual confounding and we were unable to establish causal pathways. ConclusionsIn our moderate‐sized community‐based cohort, we did not find evidence for a significant association between LTL and risk of incident AF.

BackgroundObesity is an important risk factor for nonalcoholic fatty liver disease and atrial fibrillation (AF). Less is known about the relations between nonalcoholic fatty liver disease and AF. We sought to evaluate the association between fatty liver and prevalent and incident AF in the community. Methods and ResultsWe examined Framingham Heart Study participants who underwent a study‐directed computed tomography scan, had hepatic steatosis (HS) evaluated, and did not report heavy alcohol use between 2002 and 2005. We evaluated cross‐sectional associations between liver fat and prevalent AF with logistic regression models. We assessed the relations between liver fat and incident AF during 12‐year follow‐up with Cox proportional hazards models. Of 2122 participants (53% women; mean age, 59.0±9.6 years), 20% had HS. AF prevalence (n=62) among individuals with HS was 4% compared to 3% among those without HS. There was no significant association between HS (measured as continuous or dichotomous variables) and prevalent AF in age‐ and sex‐adjusted or multivariable‐adjusted models. Incidence of AF (n=153) among participants with and without HS was 8.7 cases and 7.8 cases per 1000 person‐years, respectively. In age‐ and sex‐adjusted and multivariable‐adjusted models, there were no significant associations between continuous or dichotomous measures of HS and incident AF. ConclusionsIn our community‐based, longitudinal cohort study, liver fat by computed tomography scan was not significantly associated with increased prevalence or incidence of AF over 12 years of follow‐up.