Risk-Guided Screening for Atrial Fibrillation Using Electronic Health Records

作者：Ramesh Nadarajah, Jianhua Wu, Ali Wahab, Catherine Reynolds, Mohammad Haris, Tobin Joseph, Keerthenan Raveendra, Ben Hurdus, Khalid Kazi, Sheena Bennett, Christopher J. Hayward, Ben Mercer, Jing Kang, Chenyi Gao, Yoko M. Nakao, Koji Kawakami, Carlin Chang, Abraham Ka Chung Wai, Jiandong Zhou, Gary Tse, Talish Razi Benita, Lior Rokach, Ronen Arbel, Moti Haim, Doron Zahger, Dina Labib, Jacqueline Flewitt, James A. White, Konsta Teppo, Mika Lehto, Ville L. Langén, Aleksi Winstén, Juhani Airaksinen, Jari Haukka, Olli Halminen, Jukka Putaala, Juha Hartikainen, Miika Linna, Ben Freedman, Emma Svennberg, A. John Camm, Gregory Y.H. Lip, Chris P Gale · 发表于：Circulation · 年份：2026 · DOI：10.1161/circulationaha.126.079391 · 研究领域：Atrial Fibrillation Management and Outcomes、ECG Monitoring and Analysis、Machine Learning in Healthcare

BACKGROUND: Screening for atrial fibrillation (AF) on the basis of AF risk may be more effective. We aimed to develop, externally validate, and prospectively test a machine learning prediction model using electronic health records (EHRs) to guide AF screening. METHODS: We developed and validated a random forest prediction model for new AF within 6 months, using age, sex, and 10 comorbidities (Future Innovations in Novel Detection of Atrial Fibrillation [FIND-AF] 2.0) in EHRs in the United Kingdom (n=2 081 139), Japan (n=7 795 244), Israel (n=2 166 795), Canada (n=627 919), and China (n=149 145). We conducted a prospective study where participants ≥30 years old without AF and with a CHA 2 DS 2 -VASc score ≥2 in men and ≥3 in women, stratified by FIND-AF 2.0 into high and low risk, undertook 4 ECG recordings per day for 3 weeks using a handheld ECG recorder, with a primary outcome of newly diagnosed AF. We estimated stroke risk associated with nonanticoagulated AF in patients with high FIND-AF 2.0 risk in the FinACAF (Finnish Anticoagulation in Atrial Fibrillation) registry of patients with AF (n=229 565). RESULTS: FIND-AF 2.0 was applicable to all EHRs and showed good to excellent prediction performance (United Kingdom: area under the receiver operating characteristic curve [AUROC], 0.819 [95% CI, 0.809–0.829]; Israel: AUROC, 0.835 [95% CI, 0.828–0.842]; Japan: AUROC, 0.751 [95% CI, 0.745–0.757]; Canada: AUROC, 0.747 [95% CI, 0.741–0.753]; China: AUROC, 0.753 [95% CI, 0.725–0....