Biological age, inferred from physiological measurements, has been proposed as an indicator of functional state beyond chronological age (CA). However, it lacks a standard, clinically accepted definition. In cardiology, differences between CA and electrocardiogram (ECG)-based age estimates (ECG-age) have been associated with adverse cardiovascular outcomes, but their physiological interpretation remains unclear—whether they reflect intrinsic aging processes or serve as a surrogate for aggregate cardiovascular risk.
We developed a machine learning model to estimate ECG-age from ECG-derived interpretable features and evaluated its utility for outcome prediction. The model was trained and validated on the CODE-15 12-lead dataset (345,779 ECGs, 233,770 subjects) using over 1,300 explainable features of waveform morphology and rhythm.
Model performance was benchmarked against state-of-the-art deep learning-based ECG-age models. The model achieved a correlation coefficient of 0.83 with CA, comparable to models on the same dataset. Agreement between the two approaches showed a correlation of 0.88 with a mean absolute error of 8.4 years, indicating substantial differences in individual-level estimates.
The trained model was then applied to an adult congenital heart disease (ACHD) cohort (63,979 ECGs, 11,028 subjects). The ECG-age estimates were used to predict adverse cardiac outcomes (cardiac hospitalization, intervention, surgery, or death) within 3–90 months following ECG acquisition. Outcome prediction using CA alone, ECG-age alone, and their combination resulted in AUROC of 0.603, 0.587, and 0.627, respectively.
These results show that ECG-age provides incremental predictive value beyond CA in ACHD outcome prediction. However, if ECG-age represented a stable physiological marker, independently developed models with comparable population-level performance would be expected to yield concordant estimates at the individual level. The observed variability between models suggests that ECG-age is not uniquely identifiable from the ECG and instead reflects model-dependent regression of age-correlated features. Therefore, deviations of ECG-age from CA should not be interpreted as a biomarker for cardiovascular risk.