Journal of the American Heart Association · 2026
Artificial Intelligence-Derived Electrocardiographic Age Enhances Stroke Risk Stratification in Patients With Atrial Fibrillation With a CHA2DS2-VA Score of 1.
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Wu Chen-Shu, Chen Po-Huang, Lin Wen-Yu, Liu Wei-Ting, Chan Po Kai, Wu Hsuan Yi, Lin Chin-Sheng, Lin Chin, Lee Chiao-Chin
Аннотация
BACKGROUND: To evaluate whether artificial intelligence-derived electrocardiographic-age enhances the predictive utility of the CHA2DS2-VA (congestive heart failure/left ventricular dysfunction, hypertension, age ≥75 years [doubled], diabetes, stroke/transient ischemic attack [doubled], vascular disease, age 65-74 years) score in patients with atrial fibrillation representing a borderline indication for anticoagulation with a score of 1.
METHODS: From a multicenter atrial fibrillation and atrial flutter registry, we identified 832 anticoagulation-naïve patients with a CHA2DS2-VA score of 1 and at least one 12-lead ECG within 90 days of diagnosis. ECG-age was estimated using a previously validated deep learning (convolutional neural network) model. Chronological age was replaced with ECG-age (1 point for 65-74 years, 2 points for ≥75 years), leaving all other score components unchanged, to form the EA-CHA2DS2-VA score. Patients were grouped as EA-CHA2DS2-VA ≥2 (n=315) or <2 (n=517).
RESULTS: Compared with patients with an EA-CHA2DS2-VA score <2, those with an EA-CHA2DS2-VA score ≥2 had a significantly higher risk of ischemic stroke or transient ischemic attack (hazard ratio [HR], 1.82 [95% CI, 1.06-3.13]; P=0.028). All-cause mortality was also significantly elevated in the EA-CHA2DS2-VA ≥2 group (HR, 2.22 [95% CI, 1.43-3.45]; P<0.001). Major bleeding did not differ significantly between groups (P=0.386).
CONCLUSIONS: Substituting chronological age with artificial intelligence-derived ECG-age in the CHA2DS2-VA score was associated with improved risk stratification in patients with atrial fibrillation with intermediate stroke risk. This refined risk stratification facilitates timely initiation of anticoagulation therapy, potentially reducing stroke and mortality. Future prospective studies are essential to integrate this artificial intelligence-enhanced strategy into clinical practice.
REGISTRATION: This study was registered on the Open Science Framework (URL: https://doi.org/10.17605/OSF.IO/39BND; unique identifier: 10.17605/OSF.IO/39BND).
METHODS: From a multicenter atrial fibrillation and atrial flutter registry, we identified 832 anticoagulation-naïve patients with a CHA2DS2-VA score of 1 and at least one 12-lead ECG within 90 days of diagnosis. ECG-age was estimated using a previously validated deep learning (convolutional neural network) model. Chronological age was replaced with ECG-age (1 point for 65-74 years, 2 points for ≥75 years), leaving all other score components unchanged, to form the EA-CHA2DS2-VA score. Patients were grouped as EA-CHA2DS2-VA ≥2 (n=315) or <2 (n=517).
RESULTS: Compared with patients with an EA-CHA2DS2-VA score <2, those with an EA-CHA2DS2-VA score ≥2 had a significantly higher risk of ischemic stroke or transient ischemic attack (hazard ratio [HR], 1.82 [95% CI, 1.06-3.13]; P=0.028). All-cause mortality was also significantly elevated in the EA-CHA2DS2-VA ≥2 group (HR, 2.22 [95% CI, 1.43-3.45]; P<0.001). Major bleeding did not differ significantly between groups (P=0.386).
CONCLUSIONS: Substituting chronological age with artificial intelligence-derived ECG-age in the CHA2DS2-VA score was associated with improved risk stratification in patients with atrial fibrillation with intermediate stroke risk. This refined risk stratification facilitates timely initiation of anticoagulation therapy, potentially reducing stroke and mortality. Future prospective studies are essential to integrate this artificial intelligence-enhanced strategy into clinical practice.
REGISTRATION: This study was registered on the Open Science Framework (URL: https://doi.org/10.17605/OSF.IO/39BND; unique identifier: 10.17605/OSF.IO/39BND).