| Summary: | Background: Atrial fibrillation (AF) is a heterogeneous condition caused by various underlying disorders and comorbidities. A cluster analysis is a statistical technique that attempts to group populations by shared traits. Applied to AF, it could be useful in classifying the variables and complex presentations of AF into phenotypes of coherent, more tractable subpopulations. Objectives: This study aimed to characterize the clinical phenotypes of AF using a national AF patient registry using a cluster analysis. Methods: We used data of an observational cohort that included 7406 patients with non-valvular AF enrolled from 158 sites participating in a nationwide AF registry (J-RHYTHM). The endpoints analyzed were all-cause mortality, thromboembolisms, and major bleeding. Results: The optimal number of clusters was found to be 4 based on 40 characteristics. They were those with (1) a younger age and low rate of comorbidities (n = 1876), (2) a high rate of hypertension (n = 4579), (3) high bleeding risk (n = 302), and (4) prior coronary artery disease and other atherosclerotic comorbidities (n = 649). The patients in the younger/low comorbidity cluster demonstrated the lowest risk for all 3 endpoints. The atherosclerotic comorbidity cluster had significantly higher adjusted risks of total mortality (odds ratio [OR], 3.70; 95% confidence interval [CI], 2.37–5.80) and major bleeding (OR, 5.19; 95% CI, 2.58–10.9) than the younger/low comorbidity cluster. Conclusions: A cluster analysis identified 4 distinct groups of non-valvular AF patients with different clinical characteristics and outcomes. Awareness of these groupings may lead to a differentiated patient management for AF.
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