Laila Staerk1,2, Sarah R Preis1,3, Honghuang Lin1,4, Juan P Casas5, Kathryn Lunetta3, Lu-Chen Weng6,7, Christopher D Anderson6,8,9,10, Patrick T Ellinor6,7,11, Steven A Lubitz6,7,11, Emelia J Benjamin1,12,13, Ludovic Trinquart1,3. 1. National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, MA (L.S., S.R.P., H.L., E.J.B., L.T.). 2. Department of Cardiology, Copenhagen University Hospital Herlev and Gentofte, Helleup, Denmark (L.S.). 3. Department of Biostatistics (S.R.P., K.L., L.T.), Boston University School of Public Health, MA. 4. Section of Computational Biomedicine (H.L.), Department of Medicine, Boston University School of Medicine, MA. 5. Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System (J.P.C.). 6. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA (L.-C.W., C.D.A., P.T.E., S.A.L.). 7. Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Massachusetts General Hospital, Boston. 8. Department of Neurology (C.D.A.), Massachusetts General Hospital, Boston. 9. Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital, Boston. 10. McCance Center for Brain Health (C.D.A.), Massachusetts General Hospital, Boston. 11. Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston. 12. Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA. 13. Cardiology and Preventive Medicine Sections (E.J.B.), Department of Medicine, Boston University School of Medicine, MA.
Abstract
BACKGROUND: Risk prediction models for atrial fibrillation (AF) do not give information about when AF might develop. Restricted mean survival time (RMST) quantifies risk into the time domain. Our objective was to use RMST to re-express individualized AF risk predictions. METHODS AND RESULTS: We included AF-free participants from the Framingham Heart Study community-based cohorts. We predicted new-onset AF over 10-year follow-up according to baseline covariates: age, height, weight, systolic blood pressure, diastolic blood pressure, current smoking, antihypertensive treatment, diabetes mellitus, prevalent heart failure, and prevalent myocardial infarction. First, we fitted a Cox regression model and estimated the 10-year predicted risk of AF. Second, we fitted an RMST model and estimated the predicted mean time free of AF and alive over a time horizon of 10 years. We included 7586 AF-free participants contributing to 11 088 examinations (mean age 61±11 years, 44% were men). During 10-year follow-up, 822 participants developed AF. The Cox and RMST models were in agreement regarding the direction, strength, and statistical significance of associations for all covariates. Low (<5%), intermediate (5%-15%), and high (>15%) 10-year predicted risk of AF corresponded to predicted mean time alive and free of AF of 9.9, 9.6, and 8.8 years, respectively. A 60-year-old woman with a body mass index of 25 kg/m2, no use of hypertension treatment and no history of heart failure had a predicted mean time alive and free of AF of 9.9 years, whereas a 70-year-old man with a body mass index of 30 kg/m2, use of hypertension treatment, and with prevalent heart failure had a predicted mean time alive and free of AF of 7.9 years. CONCLUSIONS: The RMST can be used to develop risk prediction models to express results in a time scale. RMST may offer a complementary risk communication tool for AF in clinical practice.
BACKGROUND: Risk prediction models for atrial fibrillation (AF) do not give information about when AF might develop. Restricted mean survival time (RMST) quantifies risk into the time domain. Our objective was to use RMST to re-express individualized AF risk predictions. METHODS AND RESULTS: We included AF-free participants from the Framingham Heart Study community-based cohorts. We predicted new-onset AF over 10-year follow-up according to baseline covariates: age, height, weight, systolic blood pressure, diastolic blood pressure, current smoking, antihypertensive treatment, diabetes mellitus, prevalent heart failure, and prevalent myocardial infarction. First, we fitted a Cox regression model and estimated the 10-year predicted risk of AF. Second, we fitted an RMST model and estimated the predicted mean time free of AF and alive over a time horizon of 10 years. We included 7586 AF-free participants contributing to 11 088 examinations (mean age 61±11 years, 44% were men). During 10-year follow-up, 822 participants developed AF. The Cox and RMST models were in agreement regarding the direction, strength, and statistical significance of associations for all covariates. Low (<5%), intermediate (5%-15%), and high (>15%) 10-year predicted risk of AF corresponded to predicted mean time alive and free of AF of 9.9, 9.6, and 8.8 years, respectively. A 60-year-old woman with a body mass index of 25 kg/m2, no use of hypertension treatment and no history of heart failure had a predicted mean time alive and free of AF of 9.9 years, whereas a 70-year-old man with a body mass index of 30 kg/m2, use of hypertension treatment, and with prevalent heart failure had a predicted mean time alive and free of AF of 7.9 years. CONCLUSIONS: The RMST can be used to develop risk prediction models to express results in a time scale. RMST may offer a complementary risk communication tool for AF in clinical practice.
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