AI-Derived ECG Age Gap as a Digital Biomarker for Cardiovascular Risk Stratification

Abstract

ABSTRACT Cardiovascular diseases remain the leading cause of global mortality, and early risk stratification is critical for improving prognosis. Artificial intelligence–derived electrocardiography (AI-ECG) provides a promising approach to derive cardiac biological age as a non-invasive digital biomarker. This study developed an AI-ECG framework based on the ECGFounder foundation model to quantify cardiac biological aging and predict cardiovascular risk. A total of 67,824 ECGs from 63,512 UK Biobank participants were included. The model was trained on the development cohort (n = 26,871),comprising healthy individuals, and evaluated in an independent clinical evaluation cohort (n = 40,953). The AI-ECG Age Gap was assessed for its association with MACCE and other secondary outcomes using Cox models. The model demonstrated good agreement between predicted and chronological age in the development cohort (r = 0.646; MAE = 4.61 years). In the clinical cohort, after adjusting for clinical comorbidities, each 1-year increase in the age gap was associated with a significant 13% higher risk of (HR = 1.13, 95% CI: 1.11–1.14) Individuals with an overestimated age gap (> 6 years) exhibited substantially elevated risks of MACCE (HR 4.51) and other major cardiovascular outcomes, whereas those with an underestimated age gap (< −6 years) showed a significantly lower risk of MACCE (HR 0.46) alongside protective effects across other outcomes. The AI-ECG age gap effectively quantifies occult accelerated cardiac aging. As a non-invasive digital biomarker, it exhibits immense potential for cardiovascular risk stratification in broad populations.

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