European heart journal. Digital health · 2026
Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions.
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Zheng Yi, Siontis Konstantinos C, Attia Zachi I, Shiri Isaac, Roten Laurent, Baj Giovanni, Kazaj Pooya Mohammadi, Lopez-Jimenez Francisco, Gersh Bernard J, Gräni Christoph, Reichlin Tobias, Friedman Paul A, Windecker Stephan, Noseworthy Peter A, Siontis George C M
Аннотация
AIMS: Artificial intelligence electrocardiography (AI-ECG) algorithms are emerging tools for identifying individuals at risk of atrial fibrillation (AF). We evaluated the predictive performance of a validated AI-ECG algorithm for incident AF in UK Biobank participants with AF risk factors, irrespective of prevalent cardiovascular disease, and its incremental value when added to clinical predictors.
METHODS AND RESULTS: The AI-ECG tool was applied to sinus rhythm ECGs from UK-Biobank participants with risk factors for AF but no AF. Model performance was evaluated using time-dependent ROC-AUC and Harrell's C-index. Multivariable Cox regression was used to identify clinical risk factors associated with incident AF and to quantify the contribution of AI-ECG. A total of 21 842 participants (56% male) were included. The median follow-up time was 3.7 years (IQR 0.5-5.4) The ECG-AI tool achieved a ROC-AUC of 0.73 (95% CI 0.68-0.78) at 1 and 0.69 (95% CI 0.66-0.72) at 3 years. A multivariable Cox regression model using clinical parameters achieved a ROC-AUC of 0.71 (95% CI 0.66-0.75) at 1 and 0.71 (95% CI 0.68-0.74) at 3 years. By adding ECG-AI to the clinical Cox regression model, the ROC-AUC increased to 0.75 (95%CI 0.71-0.80) at 1 and 0.74 (95% CI 0.71-0.77) at 3 years. AI-ECG showed a hazard ratio of 1.23 per decile increase (95% CI 1.18-1.27).
CONCLUSION: An AI-ECG algorithm improved the prediction of incident AF when added to a clinical parameter-based model over a median follow-up time of 3.7 years among individuals with comorbidities predisposing to AF who may benefit from targeted screening and preventive strategies.
METHODS AND RESULTS: The AI-ECG tool was applied to sinus rhythm ECGs from UK-Biobank participants with risk factors for AF but no AF. Model performance was evaluated using time-dependent ROC-AUC and Harrell's C-index. Multivariable Cox regression was used to identify clinical risk factors associated with incident AF and to quantify the contribution of AI-ECG. A total of 21 842 participants (56% male) were included. The median follow-up time was 3.7 years (IQR 0.5-5.4) The ECG-AI tool achieved a ROC-AUC of 0.73 (95% CI 0.68-0.78) at 1 and 0.69 (95% CI 0.66-0.72) at 3 years. A multivariable Cox regression model using clinical parameters achieved a ROC-AUC of 0.71 (95% CI 0.66-0.75) at 1 and 0.71 (95% CI 0.68-0.74) at 3 years. By adding ECG-AI to the clinical Cox regression model, the ROC-AUC increased to 0.75 (95%CI 0.71-0.80) at 1 and 0.74 (95% CI 0.71-0.77) at 3 years. AI-ECG showed a hazard ratio of 1.23 per decile increase (95% CI 1.18-1.27).
CONCLUSION: An AI-ECG algorithm improved the prediction of incident AF when added to a clinical parameter-based model over a median follow-up time of 3.7 years among individuals with comorbidities predisposing to AF who may benefit from targeted screening and preventive strategies.