Adding an AI-ECG algorithm to a clinical model improved the prediction of incident atrial fibrillation over 3.7 years, increasing the 3-year ROC-AUC from 0.71 to 0.74.
Cohort (n=21,842)
Sí
Does an AI-ECG algorithm improve the prediction of incident atrial fibrillation in individuals with predisposing conditions compared to clinical parameters alone?
An AI-ECG algorithm provides incremental value over clinical parameters for predicting incident atrial fibrillation in patients with predisposing risk factors.
Estimación del efecto: HR 1.23 per decile increase (95% CI 1.18-1.27)
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.
Zheng et al. (Wed,) conducted a cohort in Atrial fibrillation (n=21,842). AI-ECG algorithm vs. Clinical parameter-based model was evaluated on Incident atrial fibrillation (HR 1.23 per decile increase, 95% CI 1.18-1.27). Adding an AI-ECG algorithm to a clinical model improved the prediction of incident atrial fibrillation over 3.7 years, increasing the 3-year ROC-AUC from 0.71 to 0.74.
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