Risk-Guided Screening for Atrial Fibrillation Using Electronic Health Records

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, 0.725–0.771]), with AUROC>0.7 in men and women in all cohorts, and improved performance compared with CHA 2 DS 2 -VASc and C 2 HEST. Of 1923 participants from 15 sites in the prospective study (mean age, 70.2 [SD 9.4] years), with a mean of 74.8 (SD, 19.4) ECG recordings, AF was diagnosed in 5 of 902 (0.6%) with low FIND-AF 2.0 risk and 46 of 1021 (4.5%) with high FIND-AF 2.0 risk (odds ratio, 8.46 [95% CI, 3.35–21.40], P <0.001). Median AF burden among high FIND-AF 2.0 risk–detected cases was 33.4% (interquartile range, 5.1%–91.6%), and 96.1% initiated oral anticoagulants. In the FinACAF registry, the rate of ischemic stroke for patients with high FIND-AF 2.0 risk, AF, and no anticoagulants was 6.0 events per 100 patient-years. CONCLUSIONS: The EHR-based machine learning model, FIND-AF 2.0, identifies a high-risk subpopulation for AF diagnosis among patients at elevated risk of stroke and could enable scalable, EHR-driven, risk-guided AF screening.

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Journal
Circulation
Published
2026-07-28
DOI
https://doi.org/10.1161/circulationaha.126.079391
Primary Topic
Atrial Fibrillation Management and Outcomes
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article
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article

Risk-Guided Screening for Atrial Fibrillation Using Electronic Health Records

Konsta Teppo, Ben Freedman, Christopher J. Hayward, Abraham Ka Chung Wai et al.
Circulation
Atrial Fibrillation Management and Outcomes
article

Risk-Guided Screening for Atrial Fibrillation Using Electronic Health Records

Konsta Teppo, Ben Freedman, Christopher J. Hayward, Abraham Ka Chung Wai, Ben Hurdus, Lior Rokach, A. John Camm, Juhani Airaksinen, Ramesh Nadarajah, Ville L. Langén, Juha E.K. Hartikainen, Koji Kawakami, Gregory Y.H. Lip, Moti Haim, Gary Tse, Jing Xin Kang, Sheena Bennett, Chris P Gale, Chenyi Gao, Olli Halminen, Ben Mercer, Ronen Arbel, Jukka Putaala A, Jiandong Zhou, Jari K. Haukka, James A. White, Mika Lehto, Doron Zahger, Mohammad Haris, Jacqueline Flewitt, Yoko M. Nakao, Dina Labib, Keerthenan Raveendra, Khalid Kazi, Emma Svennberg, Talish Razi Benita, Miika Linna, Tobin Joseph, Carlin Chang, Catherine Reynolds, Aleksi Winstén, Ali Wahab, Jianhua Wu
article en

Abstract

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, 0.725–0.771]), with AUROC>0.7 in men and women in all cohorts, and improved performance compared with CHA 2 DS 2 -VASc and C 2 HEST. Of 1923 participants from 15 sites in the prospective study (mean age, 70.2 [SD 9.4] years), with a mean of 74.8 (SD, 19.4) ECG recordings, AF was diagnosed in 5 of 902 (0.6%) with low FIND-AF 2.0 risk and 46 of 1021 (4.5%) with high FIND-AF 2.0 risk (odds ratio, 8.46 [95% CI, 3.35–21.40], P <0.001). Median AF burden among high FIND-AF 2.0 risk–detected cases was 33.4% (interquartile range, 5.1%–91.6%), and 96.1% initiated oral anticoagulants. In the FinACAF registry, the rate of ischemic stroke for patients with high FIND-AF 2.0 risk, AF, and no anticoagulants was 6.0 events per 100 patient-years. CONCLUSIONS: The EHR-based machine learning model, FIND-AF 2.0, identifies a high-risk subpopulation for AF diagnosis among patients at elevated risk of stroke and could enable scalable, EHR-driven, risk-guided AF screening.

Circulation
Ben-Gurion University of the Negev (IL), University of Leeds (GB), Department of Health (CN), Karolinska University Hospital (SE), University of Helsinki (FI), Cairo University (EG), Medical University of Białystok (PL), University of Liverpool (GB), University of Turku (FI), Sapir College (IL), Turku University of Applied Sciences (FI), St George's, University of London (GB), Queen Mary University of London (GB), University of Eastern Finland (FI), King's College London (GB), Kyoto University (JP), Kyoto Pharmaceutical University (JP), Leeds Teaching Hospitals NHS Trust (GB), Helsinki University Hospital (FI), Soroka Medical Center (IL), Jorvi Hospital (FI), The Heart Research Institute (AU), Turku University Hospital (FI), Finland University (FI), Second Hospital of Tianjin Medical University (CN), Metropolitan University (BD), Hospital District of Helsinki and Uusimaa (FI), Libin Cardiovascular Institute of Alberta (CA), Tianjin Medical University (CN), Clalit Health Services (IL), University of Hong Kong (HK), Aalborg University (DK), Aalto University (FI)
Openalex Percentile: Top 8%
Atrial Fibrillation Management and Outcomes
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