AI-derived ECG age gap predicted mortality post-CABG (HR 1.05/year, p=0.01) and post-PCI (HR 1.04/year, p<0.005); 10-year gap raised mortality ~50-60%.
Does a higher AI-derived ECG age gap predict increased 120-day and 3-year mortality in patients undergoing coronary revascularization (CABG or PCI)?
The AI-derived ECG age gap serves as an independent predictor of mortality following coronary revascularization, though significant short-term variability suggests a need for serial monitoring rather than single-point assessment.
Absolute Event Rate: 0% vs 0%
Abstract Background and Aims The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap—the difference between AI-predicted ECG age and chronological age—is an emerging biomarker of biological ageing linked to mortality. This study assessed its prognostic value for short- and long-term mortality after coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI), addressing model bias in ageing cohorts and short-term intra-patient variability. Methods A residual neural network was trained on 532,301 retrospective ECGs and optimized with a distribution-aware loss function to reduce age-imbalance bias (mean absolute error 6.87 years, R² 0.71). We reproduced known mortality associations in a general cardiology cohort (n=22,457). Revascularization cohorts included 1,354 CABG and 1,545 PCI patients, with ECG age gap derived from pre-procedure averages. Multivariable Cox models adjusted for age, sex, Charlson Comorbidity Index, smoking, and obesity assessed 120-day and 3-year mortality. Exploratory analyses quantified intra-patient variability and the change in ECG age gap before and after the procedure. Results Higher ECG age gap predicted increased mortality: hazard ratios (HRs) per year were 1.05 (95% CI 1.01–1.10; p=0.01) for 120-day post-CABG, 1.05 (1.02–1.08; p0.005) for 3-year post-CABG, 1.03 (0.99–1.07; p=0.13) for 120-day post-PCI, and 1.04 (1.01–1.06; p0.005) for 3-year post-PCI. A 10-year gap corresponded to approximately 60% and 50% higher mortality post-CABG and post-PCI, respectively. Short-term variability revealed a median 8.8-year spread, and CABG patients showed a significant age gap reduction of 1.42-years (p0.005). Conclusions The AI-derived ECG age gap independently predicts mortality after revascularization, but substantial short-term variability necessitates serial monitoring for reliable clinical use.
Heyde et al. (Wed,) reported a other. AI-derived ECG age gap predicted mortality post-CABG (HR 1.05/year, p=0.01) and post-PCI (HR 1.04/year, p<0.005); 10-year gap raised mortality ~50-60%.