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Abstract
Background
The increasing burden of atrial fibrillation (AF) emphasizes the need to identify high-risk individuals for enrolment in clinical trials of AF screening and primary prevention. We aimed to develop prediction models to identify individuals at high-risk of AF across prediction horizons from 6-months to 10-years.
MethodsWe used secondary-care linked primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between January 2, 1998 and November 30, 2018; randomly divided into derivation (80%) and validation (20%) datasets. Models were derived using logistic regression from known AF risk factors for incident AF in prediction periods of 6 months, 1-year, 2-years, 5-years, and 10-years. Performance was evaluated using in the validation dataset with bootstrap validation with 200 samples, and compared against the CHA
Of 2,081,139 individuals in the cohort (1,664,911 in the development dataset, 416,228 in the validation dataset), the mean age was 49.9 (SD 15.4), 50.7% were women, and 86.7% were white. New cases of AF were 7,386 (0.4%) within 6 months, 15,349 (0.7%) in 1 year, 38,487 (1.8%) in 5 years, and 79,997 (3.8%) by 10 years. Valvular heart disease and heart failure were the strongest predictors, and association of hypertension with AF increased at longer prediction horizons. The optimal risk models incorporated age, sex, ethnicity, and 8 comorbidities. The models demonstrated good-to-excellent discrimination and strong calibration across prediction horizons (AUROC, 95%CI, calibration slope: 6-months, 0.803, 0.789-0.821, 0.952; 1-year, 0.807, 0.794-0.819, 0.962; 2-years, 0.815, 0.807-0.823, 0.973; 5-years, 0.807, 0.803-0.812, 1.000; 10-years 0.780, 0.777-0.784, 1.010), and superior to the CHA
The FIND-AF models demonstrate high discrimination and calibration across short- and long-term prediction horizons in 2 million individuals. Their utility to inform trial enrolment and clinical decisions for AF screening and primary prevention requires further study.
Details
; Nakao, Yoko M 3 ; Nakao, Kazuhiro 4 ; Arbel, Ronen 5 ; Haim, Moti 6 ; Zahger, Doron 6 ; Lip, Gregory Y H 7 ; J Campbell Cowan 8 ; Gale, Chris P 9 1 Wolfson Institute of Population Health, Queen Mary, University of London, UK
2 Leeds Institute of Data Analytics, University of Leeds, UK; Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, UK; Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
3 Leeds Institute of Data Analytics, University of Leeds, UK; Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, UK; Department of Pharmacoepidemiology, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan
4 Leeds Institute of Data Analytics, University of Leeds, UK; Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, UK; Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Medicine, Suita, Japan
5 Community Medical Services Division, Clalit Health Services, Tel Aviv, Israel; Maximizing Health Outcomes Research Lab, Sapir College, Sderot, Israel
6 Department of Cardiology, Soroka University Medical Center, Beer Sheva, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel
7 Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK; Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
8 Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
9 Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, UK; Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK; Department of Pharmacoepidemiology, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan