Atrial Fibrillation Patterns and Risks of Subsequent Stroke, Heart Failure, or Death in the Community. Issue 5 (3rd September 2013) - Lubitz, Steven A.; Moser, Carlee; Sullivan, Lisa; Rienstra, Michiel; Fontes, João D.; Villalon, Mark L.; Pai, Manju; McManus, David D.; Schnabel, Renate B.; Magnani, Jared W.; Yin, Xiaoyan; Levy, Daniel; et al.
Abstract : Background: Atrial fibrillation (AF) patterns and their relations with long‐term prognosis are uncertain, partly because pattern definitions are challenging to implement in longitudinal data sets. We developed a novel AF classification algorithm and examined AF patterns and outcomes in the community. Methods and Results: We characterized AF patterns between 1980 and 2005 among Framingham Heart Study participants who survived ≥1 year after diagnosis. We classified participants based on their pattern within the first 2 years after detection as having AF without recurrence, recurrent AF, or sustained AF. We examined associations between AF patterns and 10‐year survival using proportional hazards regression. Among 612 individuals with AF, mean age was 72.5±10.8 years, and 53% were men. Of these, 478 participants had ≥2 electrocardiograms (median, 3; limits 2 to 23) within 2 years after initial AF and were classified as having AF without 2‐year recurrence (n=63, 10%), recurrent AF (n=162, 26%) or sustained AF (n=207, 34%), although some (n=46, 8%) were indeterminate. Of 432 classified participants, 363 died, 75 had strokes, and 110 were diagnosed with heart failure during the next 10 years. Relative to individuals without AF recurrence, the multivariable‐adjusted mortality was higher among people with recurrent AF (hazard ratio [HR], 2.04; 95% confidence interval [CI], 1.26 to 3.29) and sustained AF (HR, 2.36; 95% CI, 1.49 to 3.75). Conclusions: In our community‐based AF sample, only 10% had AF without early‐term (2‐year) recurrence. Compared with individuals without 2‐year AF recurrences, the 10‐year prognosis was worse for individuals with either sustained or recurrent AF. Our proposed AF classification algorithm may be applicable in longitudinal datas ets.