Increased Heart Rate Is Associated With Higher Mortality in Patients With Atrial Fibrillation (AF): Results From the Outcomes Registry for Better Informed Treatment of AF (ORBIT-AF).
Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT_AF) Investigators and PatientsShow More
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BACKGROUND: Most patients with atrial fibrillation (AF) require rate control; however, the optimal target heart rate remains under debate. We aimed to assess rate control and subsequent outcomes among patients with permanent AF. METHODS AND RESULTS: We studied 2812 US outpatients with permanent AF in the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation. Resting heart rate was measured longitudinally and used as a time-dependent covariate in multivariable Cox models of all-cause and cause-specific mortality during a median follow-up of 24 months. At baseline, 7.4% (n=207) had resting heart rate <60 beats per minute (bpm), 62% (n=1755) 60 to 79 bpm, 29% (n=817) 80 to 109 bpm, and 1.2% (n=33) ≥110 bpm. Groups did not differ by age, previous cerebrovascular disease, heart failure status, CHA2DS2-VASc scores, renal function, or left ventricular function. There were significant differences in race (P=0.001), sinus node dysfunction (P=0.004), and treatment with calcium-channel blockers (P=0.006) and anticoagulation (P=0.009). In analyses of continuous heart rates, lower heart rate ≤65 bpm was associated with higher all-cause mortality (adjusted hazard ratio [HR], 1.15 per 5-bpm decrease; 95% CI, 1.01 to 1.32; P=0.04). Similarly, increasing heart rate >65 bpm was associated with higher all-cause mortality (adjusted HR, 1.10 per 5-bpm increase; 95% CI, 1.05 to 1.15; P<0.0001). This relationship was consistent across endpoints and in a broader sensitivity analysis of permanent and nonpermanent AF patients. CONCLUSIONS: Among patients with permanent AF, there is a J-shaped relationship between heart rate and mortality. These data support current guideline recommendations, and clinical trials are warranted to determine optimal rate control. CLINICAL TRIAL REGISTRATION: URL: http://clinicaltrials.gov/. Unique identifier: NCT01165710.
Aged, 80 and over
Proportional Hazards Models
Published Version (Please cite this version)10.1161/JAHA.115.002031
Publication InfoChang, P; Fonarow, GC; Gersh, Bernard John; Holmqvist, Fredrik Nils; Hylek, E; Kim, S; ... Thomas, Laine Elliott (2015). Increased Heart Rate Is Associated With Higher Mortality in Patients With Atrial Fibrillation (AF): Results From the Outcomes Registry for Better Informed Treatment of AF (ORBIT-AF). J Am Heart Assoc, 4(9). pp. e002031. 10.1161/JAHA.115.002031. Retrieved from https://hdl.handle.net/10161/15003.
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Adjunct Professor in the Department of Medicine
Visiting Research Scholar in the Department of Medicine
This author no longer has a Scholars@Duke profile, so the information shown here reflects their Duke status at the time this item was deposited.
Fred Cobb, M.D. Professor of Medicine
Dr Peterson is the Fred Cobb Distinguished Professor of Medicine in the Division of Cardiology, a DukeMed Scholar, and the Past Executive Director of the Duke Clinical Research Institute (DCRI), Durham, NC, USA. Dr Peterson is the Principal Investigator of the National Institute of Health, Lung and Blood Institute (NHLBI) Spironolactone Initiation Registry Randomized Interventional Trial in Heart Failure With Preserved Ejection Fraction (SPIRRIT) Trial He is also the Principal I
Associate Professor of Medicine
Jonathan P. Piccini, MD, MHS is a clinical cardiac electrophysiologist and Associate Professor of Medicine at Duke University Medical Center and the Duke Clinical Research Institute. His research interests include the conduct of clinical trials and the assessment of cardiovascular therapeutics for the care of patients with heart rhythm disorders. At present, he is the Director of the EP Clinical Trials Program and Arrhythmia Core Laboratory at Duke University. He also serves on the Clinical W
Associate Professor of Biostatistics and Bioinformatics
Measurement error, longitudinal data, joint modeling
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