TY - JOUR
T1 - Association of of atrial fibrillation clinical phenotypes with treatment patterns and outcomes a multicenter registry study
AU - Inohara, Taku
AU - Shrader, Peter
AU - Pieper, Karen
AU - Blanco, Rosalia G.
AU - Thomas, Laine
AU - Singer, Daniel E.
AU - Freeman, James V.
AU - Allen, Larry A.
AU - Fonarow, Gregg C.
AU - Gersh, Bernard
AU - Ezekowitz, Michael D.
AU - Kowey, Peter R.
AU - Reiffel, James A.
AU - Naccarelli, Gerald V.
AU - Chan, Paul S.
AU - Steinberg, Benjamin A.
AU - Peterson, Eric D.
AU - Piccini, Jonathan P.
N1 - Funding Information:
part by cooperative agreement 1U19 HS021092 from the Agency of Healthcare Research and Quality and JSPS Overseas Research Fellowship. The Outcomes Registry for Better Informed Treatment of Atrial Fibrillation is sponsored by Janssen Scientific Affairs LLC.
Funding Information:
completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Inohara reports receiving grants from JSPS during the conduct of the study, plus grants from Pfizer Health Research Foundation and Miyata Cardiac Research Promotion Foundation outside the submitted work. Dr Thomas has participated in research with Novartis, Boston Scientific, Gilead Sciences, Inc, and Janssen Scientific. Dr Singer has acted as a consultant and advisory board member for Boehringer Ingelheim, Bristol-Myers Squibb, Merck, Johnson & Johnson, Pfizer, and Medtronic. Dr Singer also reports research grants from Boehringer Ingelheim and Bristol-Myers Squibb, personal fees from Johnson & Johnson during the conduct of the study, and grants from Boehringer Ingelheim, Bristol-Myers Squibb, and Medtronic, plus personal fees from Boehringer Ingelheim, Bristol-Myers Squibb, CVS Health, Johnson & Johnson, Merck, and Pfizer outside the submitted work. Dr Freeman has acted as a consultant and advisory board member for Janssen Scientific; he also reports personal fees from Janssen Pharmaceuticals and American College of Cardiology National Cardiovascular Data Registry outside the submitted work. Dr Allen has received grants from Patient-Centered Outcomes Research Institute, American Heart Association, and National Institutes of Health National Heart, Lung, and Blood Institute as well as personal fees from Novartis, Janssen, and Boston Scientific outside the submitted work. Dr Fonarow has acted as a consultant and advisory board member for Janssen Pharmaceuticals and reports personal fees from Janssen Pharmaceuticals and St. Jude outside the submitted work. Dr Gersh is a member of a data safety monitoring board for Mount Sinai St. Luke’s, Boston Scientific Corporation, Teva Pharmaceutical Industries, St. Jude Medical, Janssen Research & Development, Baxter Healthcare Corporation, and Cardiovascular Research Foundation; he has also acted as a consultant or advisory board member for Janssen Scientific Affairs, Cipla Limited, Armetheon Inc, and Medtronic and reports personal fees from Mount Sinai St. Luke’s, Boston Scientific Corporation, Teva Pharmaceutical Industries, St. Jude Medical, Janssen Research & Development, Baxter Healthcare Corporation, Cardiovascular Research Foundation, Janssen Scientific Affairs, Cipla Limited, Armetheon Inc, and Medtronic during the conduct of the study. Dr Ezekowitz has acted as a consultant or advisory board member for Boehringer Ingelheim, Daiichi Sankyo, Pfizer, Bristol-Myers Squibb, and Janssen Scientific Affairs; reports personal fees from Bristol-Myers Squibb Boehringer Ingelheim, Armetheon, Pfizer, Sanofi, Portola, Daiichi Sankyo, Medtronics, Johnson & Johnson, and Janssen Scientific Affairs; and has received grants from Pfizer and BMS. Dr Kowey has provided ad hoc consulting services to Johnson & Johnson for several related and nonrelated projects. Dr Reiffel has received a research grant from Janssen Pharmaceuticals and research support from Boehringer Ingelheim Pharmaceuticals Inc and GlaxoSmithKline; he also reports acting as a consultant for Sanofi, Gilead Sciences Inc, CV Therapeutics, GlaxoSmithKline, Merck, Cardiome Pharma Corp, Boehringer Ingelheim Pharmaceuticals Inc, and Medtronic Inc. Dr Reiffel has received speakers’ bureau income from Sanofi and Boehringer Ingelheim Pharmaceuticals Inc. Dr Naccarelli has received a research grant from Janssen; acted as a consultant or advisory board member for Janssen and Daiichi Sankyo; received personal fees from Janssen during the conduct of the study; and reports receiving grants from Janssen, and personal fees from Janssen, GlaxoSmithKline, and Daiichi Sankyo outside the submitted work. Dr Chan is an employee of Janssen, as well as a consultant for Optum Rx and Johnson & Johnson. Dr Steinberg reports grants from Janssen Pharmaceuticals during the conduct of the study and grants and other support from Janssen Pharmaceuticals, as well as other support from BMS-Pfizer and Bayer, outside the submitted work. Dr Peterson reports receiving a research grant from Janssen Pharmaceuticals and Eli Lilly and is a consultant for Janssen Pharmaceuticals and Boehringer Ingelheim. Dr Piccini reports receiving a research grant from the Agency for Healthcare Research and Quality, ARCA Biopharma, Boston Scientific, Gilead Sciences, Janssen Pharmaceuticals, Johnson & Johnson, ResMed, Spectranetics, and St Jude Medical; and acting as a consultant and advisory board member for BMS/ Pfizer, GlaxoSmithKline, Janssen Pharmaceuticals, Johnson & Johnson, Medtronic, and Spectranetics. No other disclosures were reported.
PY - 2018/1
Y1 - 2018/1
N2 - 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.
AB - 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.
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U2 - 10.1001/jamacardio.2017.4665
DO - 10.1001/jamacardio.2017.4665
M3 - Article
C2 - 29128866
AN - SCOPUS:85041459031
VL - 3
SP - 54
EP - 63
JO - JAMA Cardiology
JF - JAMA Cardiology
SN - 2380-6583
IS - 1
ER -