Association of of atrial fibrillation clinical phenotypes with treatment patterns and outcomes a multicenter registry study

Taku Inohara, Peter Shrader, Karen Pieper, Rosalia G. Blanco, Laine Thomas, Daniel E. Singer, James V. Freeman, Larry A. Allen, Gregg C. Fonarow, Bernard Gersh, Michael D. Ezekowitz, Peter R. Kowey, James A. Reiffel, Gerald V. Naccarelli, Paul S. Chan, Benjamin A. Steinberg, Eric D. Peterson, Jonathan P. Piccini

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


IMPORTANCE Atrial fibrillation (AF) is usually classified on the basis of the disease subtype. However, this characterization does not capture the full heterogeneity of AF, and a data-driven cluster analysis reveals different possible classifications of patients. OBJECTIVE To characterize patients with AF based on a cluster analysis and to evaluate the association between these phenotypes, treatment, and clinical outcomes. DESIGN, SETTING, AND PARTICIPANTS This cluster analysis used data from an observational cohort that included 9749 patients with AF who had been admitted to 174 US sites participating in the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF) registry. Data analysis was completed from January 2017 to October 2017. EXPOSURE Patients with diagnosed AF who were included in the registry. MAIN OUTCOMES AND MEASURES Composite of major adverse cardiovascular or neurological events and major bleeding, as defined by the International Society of Thrombosis and Hemostasis criteria. RESULTS Of 9749 total patients, 4150 (42.6%) were female; 8719 (89.4%) were white and 477 (4.9%) were African American. A cluster analysis was performed using 60 baseline clinical characteristics, and it classified patients with AF into 4 statistically driven clusters: (1) those with considerably lower rates of risk factors and comorbidities than all other clusters (n = 4673); (2) those with AF at younger ages and/or with comorbid behavioral disorders (n = 963); (3) those with AF who had similarities to patients with tachycardia-brachycardia and had device implantation owing to sinus node dysfunction (n = 1651); and (4) those with AF and prior coronary artery disease,myocardial infarction, and/or atherosclerotic comorbidities (n = 2462). Conventional classifications, such as AF subtype and left atrial size, did not drive cluster formation. Compared with the low comorbidity AF cluster, adjusted risks of major adverse cardiovascular or neurological events were significantly higher in the other 3 clusters (behavioral comorbidity cluster: hazard ratio [HR], 1.49; 95%CI, 1.10-2.00; device implantation cluster: HR, 1.39; 95%CI, 1.15-1.68; and atherosclerotic comorbidity cluster: HR, 1.59; 95%CI, 1.31-1.92). For major bleeding, adjusted risks were higher in the behavioral disorder comorbidity cluster (HR, 1.35; 95%CI, 1.05-1.73), those with device implantation (HR, 1.24; 95%CI, 1.05-1.47), and those with atherosclerotic comorbidities (HR, 1.13; 95%CI, 0.96-1.33) compared with the low comorbidity cluster. The same clusters were identified in an external validation in the ORBIT AF II registry. CONCLUSIONS AND RELEVANCE Cluster analysis identified 4 clinically relevant phenotypes of AF that each have distinct associations with clinical outcomes, underscoring the heterogeneity of AF and importance of comorbidities and substrates.

Original languageEnglish (US)
Pages (from-to)54-63
Number of pages10
JournalJAMA cardiology
Issue number1
StatePublished - Jan 2018

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine


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