Evaluation of AI ECG age in the prediction of cardiovascular diseases and risk factors: Exploratory data analysis.

Cardiovascular diseases (CVDs) are the most widespread cause of death across the world, and this aspect requires a better risk stratification method. Effectively, this paper tests the potential of the artificial intelligence (AI)-predicted electrocardiogram (ECG) age as a novel CVD risk predictor biomarker. We used the CODE-15 % data (over 344,000 ECG records with clinical annotation) to create a logistic regression model that incorporated age predicted from ECG, as well as demographic and comorbidity variables, to evaluate cardiovascular outcomes. The model performed better with ECG-predicted age than using chronological age alone, with a 92 % receiver operating characteristic curve area (AUROC) for atrial fibrillation (AF) detection, compared to 88 % when chronological age was used alone. From the results, the ECG-predicted age showed a stronger correlation with AF (r = 0.17), bundle branch blocks (r = 0.14), and Chagas disease (r = 0.074) than chronological age. Sex-specific patterns were also observed; male sex had an increased risk of sinus bradycardia (odds ratio [OR]: 3.090), while female sex had a lower risk of having a left bundle branch block (LBBB) OR: 0.885) with the variable of ECG-predicted age. These results demonstrate how ECG-predicted age may be used as a biologically meaningful indicator for cardiovascular risk assessment. This study advances personalized medicine by offering a cost-effective and scalable method for detecting CVD, particularly in under-resourced regions that lack conventional biomarkers.