Shaan Khurshid1,2,3, Nina Mars4, Christopher M Haggerty5,6, Qiuxi Huang7,8, Lu-Chen Weng2,3, Dustin N Hartzel9, Kathryn L Lunetta7,8, Jeffrey M Ashburner10, Christopher D Anderson11,12,3, Emelia J Benjamin13,14,8, Veikko Salomaa15, Patrick T Ellinor2,16,3, Brandon K Fornwalt5,6, Samuli Ripatti4,17,18, Ludovic Trinquart7,8, Steven A Lubitz2,16,3. 1. Division of Cardiology (S.K.), Massachusetts General Hospital, Boston. 2. Cardiovascular Research Center (S.K., L.-C.W., P.T.E., S.A.L.), Massachusetts General Hospital, Boston. 3. Cardiovascular Disease Initiative, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge (S.K., L.-C.W., C.D.A., P.T.E., S.A.L.). 4. Institute for Molecular Medicine Finland, FIMM, HiLIFE (N.M., S.R.), University of Helsinki, Finland. 5. Heart Institute (C.M.H., B.K.F.) and Informatics, Geisinger, Danville, PA. 6. Department of Translational Data Science (C.M.H., B.K.F.) and Informatics, Geisinger, Danville, PA. 7. Department of Biostatistics (Q.H., K.L.L, L.T.), Boston University School of Medicine. 8. Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, MA ((Q.H., K.L.L, E.J.B., L.T.). 9. Phenomic Analytics and Clinical Data Core, Geisinger Health, Danville, PA (D.N.H.). 10. Division of General Internal Medicine (J.M.A.), Massachusetts General Hospital, Boston. 11. Henry and Allison McCance Center for Brain Health (C.D.A.), Massachusetts General Hospital, Boston. 12. Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital, Boston. 13. Sections of Preventive Medicine and Cardiovascular Medicine, Department of Medicine (E.J.B.), Boston University School of Medicine. 14. Department of Epidemiology, Boston University School of Public Health, Boston (E.J.B.). 15. Regeneron Pharmaceuticals, Tarrytown, NY. Finnish Institute for Health and Welfare, Helsinki, Finland (V.S.). 16. Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston. 17. Department of Public Health (S.R.), University of Helsinki, Finland. 18. Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA (S.R.).
Abstract
BACKGROUND: Atrial fibrillation (AF) risk estimation using clinical factors with or without genetic information may identify AF screening candidates more accurately than the guideline-based age threshold of ≥65 years. METHODS: We analyzed 4 samples across the United States and Europe (derivation: UK Biobank; validation: FINRISK, Geisinger MyCode Initiative, and Framingham Heart Study). We estimated AF risk using the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) score and a combination of CHARGE-AF and a 1168-variant polygenic score (Predict-AF). We compared the utility of age, CHARGE-AF, and Predict-AF for predicting 5-year AF by quantifying discrimination and calibration. RESULTS: Among 543 093 individuals, 8940 developed AF within 5 years. In the validation sets, CHARGE-AF (C index range, 0.720-0.824) and Predict-AF (0.749-0.831) had largely comparable discrimination, both favorable to continuous age (0.675-0.801). Calibration was similar using CHARGE-AF (slope range, 0.67-0.87) and Predict-AF (0.65-0.83). Net reclassification improvement using Predict-AF versus CHARGE-AF was modest (net reclassification improvement range, 0.024-0.057) but more favorable among individuals aged <65 years (0.062-0.11). Using Predict-AF among 99 530 individuals aged ≥65 years across each sample, 70 849 had AF risk <5%, of whom 69 067 (97.5%) did not develop AF, whereas 28 681 had AF risk ≥5%, of whom 2264 (7.9%) developed AF. Of 11 379 individuals aged <65 years with AF risk ≥5%, 435 (3.8%) developed AF before age 65 years, with roughly half (46.9%) meeting anticoagulation criteria. CONCLUSIONS: AF risk estimation using clinical factors may prioritize individuals for AF screening more precisely than the age threshold endorsed in current guidelines. The additional value of genetic predisposition is modest but greatest among younger individuals.
BACKGROUND: Atrial fibrillation (AF) risk estimation using clinical factors with or without genetic information may identify AF screening candidates more accurately than the guideline-based age threshold of ≥65 years. METHODS: We analyzed 4 samples across the United States and Europe (derivation: UK Biobank; validation: FINRISK, Geisinger MyCode Initiative, and Framingham Heart Study). We estimated AF risk using the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) score and a combination of CHARGE-AF and a 1168-variant polygenic score (Predict-AF). We compared the utility of age, CHARGE-AF, and Predict-AF for predicting 5-year AF by quantifying discrimination and calibration. RESULTS: Among 543 093 individuals, 8940 developed AF within 5 years. In the validation sets, CHARGE-AF (C index range, 0.720-0.824) and Predict-AF (0.749-0.831) had largely comparable discrimination, both favorable to continuous age (0.675-0.801). Calibration was similar using CHARGE-AF (slope range, 0.67-0.87) and Predict-AF (0.65-0.83). Net reclassification improvement using Predict-AF versus CHARGE-AF was modest (net reclassification improvement range, 0.024-0.057) but more favorable among individuals aged <65 years (0.062-0.11). Using Predict-AF among 99 530 individuals aged ≥65 years across each sample, 70 849 had AF risk <5%, of whom 69 067 (97.5%) did not develop AF, whereas 28 681 had AF risk ≥5%, of whom 2264 (7.9%) developed AF. Of 11 379 individuals aged <65 years with AF risk ≥5%, 435 (3.8%) developed AF before age 65 years, with roughly half (46.9%) meeting anticoagulation criteria. CONCLUSIONS: AF risk estimation using clinical factors may prioritize individuals for AF screening more precisely than the age threshold endorsed in current guidelines. The additional value of genetic predisposition is modest but greatest among younger individuals.
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