Key result
FIND-AF machine learning algorithm effectively predicts incident AF using routine EHR data.
Why the study?
Electronic health records can be used to target atrial fibrillation screening, but scalable risk prediction models across nationwide EHRs require evaluation.
Does the FIND-AF machine learning algorithm improve risk stratification for undiagnosed atrial fibrillation compared to CHA2DS2-VASc and C2HEST scores in individuals aged ≥30 years?
Cohort (n=2,166,795)
Does the FIND-AF machine learning algorithm improve risk stratification for undiagnosed atrial fibrillation compared to CHA2DS2-VASc and C2HEST scores in individuals aged ≥30 years?
Answers are grounded in this paper and cite their sources.
The FIND-AF machine learning algorithm applied to routine EHR data provides superior discrimination for incident atrial fibrillation compared to traditional clinical risk scores.
AIMS: Electronic health records (EHR) can be used to target atrial fibrillation (AF) screening. We evaluated the performance of risk prediction models scalable across nationwide EHRs. METHODS: Retrospective cohort study individuals aged ≥30 years without diagnosed AF in the Clalit Health Services (Israel) EHR dataset between January 1 2019 and June 30, 2019. The primary outcome was a diagnosis of AF or atrial flutter (AFl) within 6 months. The FIND-AF, CHA2DS2-VASc and C2HEST scores were evaluated, with prediction performance assessed overall and by sex. The optimum threshold to apply in prospective screening was determined with a lift analysis. RESULTS: Of 2,166,795 individuals in the cohort 4,275 developed AF within 6 months. Prediction performance was strongest for FIND-AF (AUROC 0.871, 95% CI 0.864-0.877; calibration slope 0.73, 95% CI 0.67-0.79) compared with CHA2DS2-VASc (AUROC 0.838, 95% CI 0.831-0.845; calibration slope 0.63, 95% CI 0.60-0.67) and C2HEST scores (AUROC 0.834, 95% CI 0.823-0.844; calibration slope 0.62, 95% CI 0.58-0.65), including in women (FIND-AF AUROC 0.883, 95% CI 0.876-0.889; CHA2DS2-VASc AUROC 0.865, 95% CI 0.858-0.872; C2HEST AUROC 0.853, 95% CI 0.846-0.861) and men (FIND-AF AUROC 0.857, 95% CI 0.850-0.864; CHA2DS2-VASc AUROC 0.835, 95% CI 0.828-0.843; C2HEST AUROC 0.814, 95% CI, 0.806-0.822). Lift analysis suggested that screening the top 15% of FIND-AF risk compared to screening by age would identify 72% compared to 63% of AF diagnoses. CONCLUSION: The FIND-AF machine learning algorithm was scalable in routine EHR data with good discrimination for incident AF. Prospective evaluation is now required to evaluate risk-guided AF screening.
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Rokach et al. (2026) conducted a cohort in Undiagnosed atrial fibrillation (n=2,166,795). FIND-AF machine learning algorithm vs. CHA2DS2-VASc and C2HEST scores was evaluated on Diagnosis of atrial fibrillation or atrial flutter within 6 months (AUROC 0.871, 95% CI 0.864-0.877). The FIND-AF machine learning algorithm strongly predicted incident atrial fibrillation within 6 months (AUROC 0.871; 95% CI 0.864-0.877), outperforming CHA2DS2-VASc and C2HEST scores.
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