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.

中文翻译：

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心房颤动临床和遗传风险模型的预测准确性


背景：使用有或没有遗传信息的临床因素进行心房颤动 (AF) 风险评估可能比基于指南的年龄阈值≥65 岁更准确地识别 AF 筛查候选者。方法：我们分析了美国和欧洲的 4 个样本（推导：英国生物银行；验证：FINRISK、Geisinger MyCode Initiative 和 Framingham Heart Study）。我们使用 CHARGE-AF（基因组流行病学 AF 心脏和衰老研究队列）评分以及 CHARGE-AF 和 1168 变异多基因评分 (Predict-AF) 的组合来估计 AF 风险。我们通过量化歧视和校准来比较年龄、CHARGE-AF 和 Predict-AF 在预测 5 年 AF 方面的效用。结果：在 543 093 人中，8940 人在 5 年内发生 AF。在验证集中，CHARGE-AF（C 指数范围，0.720-0.824）和 Predict-AF（0.749-0.831）在很大程度上具有相当的辨别力，两者都有利于连续年龄（0.675-0.801）。使用 CHARGE-AF（斜率范围，0.67-0.87）和 Predict-AF（0.65-0.83）进行的校准类似。使用 Predict-AF 与 CHARGE-AF 相比，使用 Predict-AF 的净重分类改善幅度不大（净重分类改善范围为 0.024–0.057），但在 <65 岁的个体中更为有利（0.062–0.11）。在每个样本的 99 530 名年龄 ≥ 65 岁的个体中使用 Predict-AF，发现 70 849 人的 AF 风险为 <5%，其中 69 067 人 (97.5%) 没有发生 AF，而 28 681 人的 AF 风险≥5%，其中2264 名 (7.9%) 出现房颤。在 11 379 名年龄为 <65 岁且房颤风险≥5%的个体中，435 名 (3.8%) 在 65 岁之前发生房颤，其中约一半 (46.9%) 符合抗凝标准。结论：使用临床因素进行房颤风险评估可能会优先考虑个体房颤筛查比现行指南中认可的年龄阈值更精确。 遗传倾向的附加价值不大，但在年轻人中最大。