Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation. (31st August 2021)

• Record Type: Journal Article
• Authors: Khurshid, Shaan; Mars, Nina; Haggerty, Christopher M.; Huang, Qiuxi; Weng, Lu-Chen; Hartzel, Dustin N.; Lunetta, Kathryn L.; Ashburner, Jeffrey M.; Anderson, Christopher D.; Benjamin, Emelia J.; Salomaa, Veikko; Ellinor, Patrick T.; Fornwalt, Brandon K.; Ripatti, Samuli; Trinquart, Ludovic; Lubitz, Steven A.
• Is Part Of: Circulation. Volume 14:Number 5(2021)
• Page Start: e003355
• Publication Date: 2021-08-31
• Subjects: aging -- atrial fibrillation -- genetic predisposition to disease -- genomics -- risk assessment; Cardiovascular system -- Diseases -- Periodicals; Cardiovascular system -- Genetics -- Periodicals; Cardiovascular Diseases -- genetics; Precision Medicine; Periodical; Fulltext; Internet Resources; Periodicals; Electronic journals; Periodicals; 616.1042
• DOI: 10.1161/CIRCGEN.121.003355
• Languages: English
• ISSNs: 2574-8300

Abstract

Abstract : Supplemental Digital Content is available in the text. 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.