Heart rate is associated with progression of atrial fibrillation, independent of rhythm.
Abstract
OBJECTIVE: Atrial fibrillation (AF) often progresses from paroxysmal or persistent
to more sustained forms, but the rate and predictors of AF progression in clinical
practice are not well described. METHODS: Using the Outcomes Registry for Better Informed
Treatment of AF, we analysed the incidence and predictors of progression and tested
the discrimination and calibration of the HATCH (hypertension, age, TIA/stroke, chronic
obstructive pulmonary disease, heart failure) and CHA₂DS₂VASc scores for identifying
AF progression. RESULTS: Among 6235 patients with paroxysmal or persistent AF at baseline,
1479 progressed, during follow-up (median 18 (IQR 12-24) months). These patients were
older and had more comorbidities than patients who did not progress (CHADS₂ 2.3±1.3
vs 2.1±1.3, p<0.0001). At baseline, patients with AF progression were more often on
a rate control as opposed to a rhythm control strategy (66 vs 56%, p<0.0001) and had
higher heart rate (72(64-80) vs 68(60-76) bpm, p<0.0001). The strongest predictors
of AF progression were AF on the baseline ECG (OR 2.30, 95% CI 1.95 to 2.73, p<0.0001)
and increasing age (OR 1.16, 95% CI1.09 to 1.24, p<0.0001, per 10 increase), while
patients with lower heart rate (OR 0.84, 95% CI 0.79 to 0.89, p<0.0001, per 10 decrease
≤80) were less likely to progress. There was no significant interaction between rhythm
on baseline ECG and heart rate (p=0.71). The HATCH and CHA₂DS₂VASc scores had modest
discriminatory power for AF progression (C-indices 0.55 (95% CI 0.53 to 0.58) and
0.55 (95% CI 0.52 to 0.57)). CONCLUSIONS: Within 1.5 years, almost a quarter of the
patients with paroxysmal or persistent AF progress to a more sustained form. Progression
is strongly associated with heart rate, and age.
Type
Journal articleSubject
Age FactorsAged
Aged, 80 and over
Atrial Fibrillation
Cohort Studies
Disease Progression
Female
Heart Failure
Heart Rate
Humans
Hypertrophy, Left Ventricular
Male
Risk Factors
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https://hdl.handle.net/10161/15008Published Version (Please cite this version)
10.1136/heartjnl-2014-307043Publication Info
Holmqvist, Fredrik; Kim, Sunghee; Steinberg, Benjamin A; Reiffel, James A; Mahaffey,
Kenneth W; Gersh, Bernard J; ... ORBIT-AF Investigators (2015). Heart rate is associated with progression of atrial fibrillation, independent of rhythm.
Heart, 101(11). pp. 894-899. 10.1136/heartjnl-2014-307043. Retrieved from https://hdl.handle.net/10161/15008.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Eric David Peterson
Fred Cobb, M.D. Distinguished 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
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.
Jonathan Paul Piccini Sr.
Associate Professor of Medicine
Jonathan P. Piccini, MD, MHS, FACC, FAHA, FHRS is a clinical cardiac electrophysiologist
and Associate Professor of Medicine with Tenure at Duke University Medical Center
and the Duke Clinical Research Institute. He is the Director of the Cardiac Electrophysiology
section at the Duke Heart Center. His focus is on the care of patients with atrial
fibrillation and complex arrhythmias, with particular emphasis on catheter ablation,
left atrial appendage occlusion, and lead extraction. His resear
Benjamin Steinberg
House Staff
Laine Elliott Thomas
Associate Professor of Biostatistics & Bioinformatics
Causal Inference, Heterogeneity of Treatment Effects, Observational Data, Time-varying
Treatments, Real World Evidence
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