BACKGROUND Atrial fibrillation (AF) occurs in many clinical contexts and is diagnosed and treated by clinicians across many specialties, which has been associated with treatment variation. OBJECTIVES We evaluated the association of treating specialty with AF outcomes among patients with newly-diagnosed AF. METHODS Using complete data of the TREAT-AF (The Retrospective Evaluation and Assessment of Therapies in AF) study from the Veterans Health Administration, we identified patients with newly diagnosed, nonvalvular AF between 2004-2012 and at least one outpatient encounter with primary care or cardiology within 90 days of AF diagnosis. Cox proportional hazards regression was used to evaluate association between treating specialty and AF outcomes. RESULTS Among 184,161 patients with newly-diagnosed AF (age 70±11; 1.7% women; CHADS-VASc 2.6±1.7), 40% received cardiology care and 60% received primary care only. After adjustment for covariates, cardiology care was associated with reductions in stroke (HR 0.91, 95% CI 0.86-0.96, p <0.001) and death (HR 0.89; 95% CI 0.88-0.91; p <0.0001) and increases in AF/SVT (HR 1.38; 95% CI 1.35-1.42; p <0.0001) and MI (HR 1.03; 95% CI 1.00-1.05; p <0.04). The propensity matched cohort had similar results. In mediation analysis, oral anticoagulation (OAC) prescription within 90 days of diagnosis may have mediated reductions in stroke but did not mediate reductions in survival. CONCLUSIONS In patients with newly-diagnosed AF, cardiology care was associated with improved outcomes, potentially mediated by early OAC prescription. Although hypothesis-generating, these data warrant serious consideration and study of health care system interventions at the time of new AF diagnosis.

Background: The Apple watch irregular pulse detection algorithm was found to have a positive predictive value of 0.84 for identification of atrial fibrillation (AF). We sought to describe the prevalence of arrhythmias other than AF in those with an irregular pulse detected on a smartwatch. Methods: The Apple Heart Study investigated a smartwatch-based irregular pulse notification algorithm to identify AF. For this secondary analysis, we analyzed participants who received an ambulatory ECG patch after index irregular pulse notification. We excluded participants with AF identified on ECG patch and described the prevalence of other arrhythmias on the remaining participant ECG patches. We also reported the proportion of participants self-reporting subsequent AF diagnosis. Results: Among 419 297 participants enrolled in the Apple Heart Study, 450 participant ECG patches were analyzed, with no AF on 297 ECG patches (66%). Non-AF arrhythmias (excluding supraventricular tachycardias <30 beats and pauses <3 seconds) were detected in 119 participants (40.1%) with ECG patches without AF. The most common arrhythmias were frequent PACs (burden ≥1% to <5%, 15.8%; ≥5% to <15%, 8.8%), atrial tachycardia (≥30 beats, 5.4%), frequent PVCs (burden ≥1% to <5%, 6.1%; ≥5% to <15%, 2.7%), and nonsustained ventricular tachycardia (4–7 beats, 6.4%; ≥8 beats, 3.7%). Of 249 participants with no AF detected on ECG patch and patient-reported data available, 76 participants (30.5%) reported subsequent AF diagnosis. Conclusions: In participants with an irregular pulse notification on the Apple Watch and no AF observed on ECG patch, atrial and ventricular arrhythmias, mostly PACs and PVCs, were detected in 40% of participants. Defining optimal care for patients with detection of incidental arrhythmias other than AF is important as AF detection is further investigated, implemented, and refined.

Background Approaches, tools, and technologies for atrial fibrillation (AF) ablation have evolved significantly since its inception. We sought to characterize secular trends in AF ablation success rates. Methods We performed a systematic review and meta-analysis of AF ablation from January 1, 1990, to August 1, 2016, searching PubMed, Scopus, and Cochrane databases. Major exclusion criteria were insufficient outcome reporting and ablation strategies that were not prespecified and uniform. We stratified treatment arms by AF type (paroxysmal AF; nonparoxysmal AF) and analyzed single-procedure outcomes. Multivariate meta-regressions analyzed effects of study, patient, and procedure characteristics on success rate trends. Registered in PROSPERO (CRD42016036549). Results A total of 180 trials and observational studies with 28,118 patients met inclusion. For paroxysmal AF ablation studies, unadjusted success rate summary estimates ranged from 73.1% in 2003 to 77.1% in 2016, increasing by 0.9%/year (95% CI 0.4%−1.4%; P = .001; I2 = 90%). After controlling for study design and patient demographics, rate of improvement in success rate summary estimate increased (1.6%/year; 95% CI 0.9%−2.2%; P = .001; I2 = 87%). For nonparoxysmal AF ablation studies, unadjusted success rate summary estimates ranged from 70.0% in 2010 to 64.3% in 2016 (1.1%/year; 95% CI −1.3% to 3.5%; P = .37; I2 = 85%), with no improvement in multivariate analyses. Conclusions Despite substantial research investment and health care expenditure, improvements in AF ablation success rates have been incremental. Meaningful improvements may require major paradigm or technology changes, and evaluation of clinical outcomes such as mortality and quality of life may prove to be important going forward.