Key result
High FIND-AF 2.0 score linked to ~8-fold higher rate of newly diagnosed AF on intermittent monitoring.
Why the study?
Screening for atrial fibrillation based on AF risk may be more effective, prompting the development, external validation, and prospective testing of an EHR-based machine learning prediction model to guide screening.
Does a high FIND-AF 2.0 risk score predict newly diagnosed atrial fibrillation in patients at elevated stroke risk undergoing intermittent ECG monitoring?
Cohort (n=1,923)
Outcome assessors (electrophysiologists) blinded to risk classification
Yes
Does a high FIND-AF 2.0 risk score predict newly diagnosed atrial fibrillation in patients at elevated stroke risk undergoing intermittent ECG monitoring?
Odds Ratio: 8.46 (95% CI 3.35–21.4)
Absolute Event Rate: 4.5% vs 0.6%
p-value: p=<0.001
The FIND-AF 2.0 machine learning model effectively identifies patients at high risk for undiagnosed atrial fibrillation, enabling targeted and higher-yield screening.
Supports targeted AF screening in high FIND-AF 2.0 patients; prospective trials needed to confirm outcome benefit.
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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Nadarajah et al. (2026) conducted a cohort in Elevated stroke risk without known atrial fibrillation (n=1,923). High FIND-AF 2.0 risk score (≥0.0053) vs. Low FIND-AF 2.0 risk score (<0.0053) was evaluated on Newly diagnosed atrial fibrillation (OR 8.46, 95% CI 3.35-21.40, p=<0.001). High FIND-AF 2.0 risk was associated with a significantly higher rate of newly diagnosed atrial fibrillation compared to low risk (4.5% vs 0.6%, OR 8.46) during 3 weeks of intermittent ECG monitoring.