Key result
Adding AI-ECG and genetics to CHARGE-AF boosts incident AF prediction to a ~0.75 C-index.
Why the study?
The CHARGE-AF score provides practical AF risk estimation but has limited predictive accuracy, motivating improvement by integrating AI-ECG and genetic data.
Does integrating genetic risk and AI-ECG-derived features improve the prediction of incident atrial fibrillation in adults without prior AF compared to clinical risk factors alone?
Cohort (n=39,478)
Yes
Does integrating genetic risk and AI-ECG-derived features improve the prediction of incident atrial fibrillation in adults without prior AF compared to clinical risk factors alone?
Effect estimate: ΔC-index 0.045 (95% CI 0.028-0.062)
Absolute Event Rate: 0.754% vs 0.708%
p-value: p=<0.001
Integrating genetic risk scores and AI-ECG-derived features (age gap and sex mismatch) significantly enhances the prediction of incident atrial fibrillation beyond established clinical models.
Background Accurate prediction of atrial fibrillation (AF) is essential for prevention. The CHARGE‐AF score, based on routinely available clinical factors, provides a practical tool for estimating AF risk but has limited predictive accuracy. This study aimed to improve AF risk prediction by integrating artificial intelligence (AI)–based electrocardiogram (ECG) analysis of age and sex with genetic information in addition to established clinical risk factors. Methods We analyzed 39 478 UK Biobank participants without prior AF. A polygenic risk score for AF (AF‐PRS), the AI‐ECG age gap (AI‐ECG–predicted age minus chronological age), and AI‐ECG–predicted sex mismatch were evaluated on top of the CHARGE‐AF model. Model performance was compared across sequential prediction models. Results Over a median follow‐up of 2.7 years (interquartile range, 1.7–4.2; maximum, 6.7), 533 participants (1.3%) developed AF. AF‐PRS (HR 1.61, 95% CI: 1.48–1.76) and the AI‐ECG age gap (HR 1.37, 95% CI: 1.24–1.51) were independently associated with incident AF. Compared with CHARGE‐AF alone (C‐index 0.708, 95% CI: 0.686–0.730), adding AF‐PRS improved discrimination (C‐index 0.743; Δ0.035, 95% CI: 0.021–0.049; p < 0.001). Adding traditional ECG parameters did not enhance performance, whereas incorporating AI‐ECG features (age gap and sex mismatch) into the combined clinical and genetic model provided additional gain in discrimination (C‐index 0.754; Δ0.045, 95% CI: 0.028–0.062), reclassification (net reclassification improvement 0.408), and discrimination improvement (0.007; all p < 0.001). Conclusions Integrating genetic risk and AI‐ECG–derived features enhances AF prediction beyond an established clinical model, supporting the use of combined digital and genetic biomarkers for risk stratification.
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Yang et al. (2026) conducted a cohort in Without prior atrial fibrillation (n=39,478). Integration of AI-ECG features (age gap and sex mismatch) and AF-PRS vs. CHARGE-AF clinical risk score alone was evaluated on Incident Atrial Fibrillation (model discrimination C-index) (ΔC-index 0.045, 95% CI 0.028-0.062, p=<0.001). Integrating genetic risk and AI-ECG-derived features significantly improved the prediction of incident atrial fibrillation compared to the CHARGE-AF clinical model alone (C-index 0.754 vs 0.708).
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