Artificial Intelligence-Derived Electrocardiographic Age Enhances Stroke Risk Stratification in Patients With Atrial Fibrillation With a CHA(2)DS(2)-VA Score of 1.

Published in Journal of the American Heart Association. Abstract: To evaluate whether artificial intelligence-derived electrocardiographic-age enhances the predictive utility of the CHA 2 DS 2 -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. From a multicenter atrial fibrillation and atrial flutter registry, we identified 832 anticoagulation-naïve patients with a CHA 2 DS 2 -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-CHA 2 DS 2 -VA score. Patients were grouped as EA-CHA 2 DS 2 -VA ≥2 (n=315) or <2 (n=517). Compared with patients with an EA-CHA 2 DS 2 -VA score <2, those with an EA-CHA 2 DS 2 -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-CHA 2 DS 2 -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). Substituting chronological age with artificial intelligence-derived ECG...