Key result
Higher ECG-derived age linked to ~67% higher all-cause mortality per SD.
Why the study?
Chronological age does not fully capture heterogeneity in cardiovascular risk, while routine ECGs offer non-invasive age-related physiological information. This study evaluated whether an ECG-derived age model is associated with mortality and adds predictive value relative to a conventional risk-factor-derived heart-age marker.
Does a machine-learning ECG-derived age model predict all-cause and cardiovascular mortality and improve 10-year mortality discrimination compared to Heart Foundation Heart Age in older adults without baseline cardiovascular disease?
Cohort (n=17,090)
Does a machine-learning ECG-derived age model predict all-cause and cardiovascular mortality and improve 10-year mortality discrimination compared to Heart Foundation Heart Age in older adults without baseline cardiovascular disease?
An ECG-derived age model using routine ECG parameters predicts all-cause and cardiovascular mortality with discrimination comparable to conventional risk-factor-based heart age and provides modest incremental predictive value when combined.
BACKGROUND Chronological age does not fully capture heterogeneity in cardiovascular risk. Electrocardiograms (ECGs) provide a widely available, non-invasive source of age-related electrical and physiological information. We developed an ECG-derived age model, evaluated its associations with mortality, and assessed its predictive value relative to a conventional-risk-factor-derived heart-age marker. OBJECTIVE To develop and internally validate an ECG-age model based on standard ECG parameters, evaluate its association with all-cause and cardiovascular mortality, and compare its 10-year mortality discrimination with that of Heart Foundation Heart Age (HFHA). METHODS We analysed baseline ECG parameters from the Guangzhou Biobank Cohort Study, a southern Chinese population-based cohort of older adults. ECG age was developed using machine-learning algorithms and validated in a 30% held-out sample. Associations with all-cause and cardiovascular disease (CVD) mortality were assessed using Cox proportional hazards regression after excluding participants with baseline CVD. Ten-year mortality prediction was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with HFHA. RESULTS Among 17,090 participants (mean age 60.6 years [SD=7.2]; 74.2% women) followed from 2003–08 through 2023, LightGBM showed the best age-prediction performance (mean absolute error=4.91 years; R²=0.302). Higher ECG age was associated with increased risk of all-cause mortality (hazard ratio [HR] per SD 1.67, 95%CI 1.62–1.71) and CVD mortality (1.67, 1.61–1.73). For 10-year mortality prediction, AUCs for ECG age versus HFHA were 0.758 versus 0.755 for all-cause mortality and 0.799 versus 0.806 for CVD mortality. Adding ECG age to HFHA improved discrimination for all-cause mortality (ΔAUC 0.006, 95%CI 0.003–0.010) and CVD mortality (0.010, 0.002–0.017). CONCLUSIONS ECG age was associated with all-cause and cardiovascular mortality and showed mortality discrimination similar to the Heart Foundation Heart Age marker in this internally validated cohort. External validation is required before clinical interpretation or use.
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Yang et al. (2026) conducted a cohort in Older adults without baseline cardiovascular disease (n=17,090). Machine-learning-derived ECG age from routine baseline ECG parameters vs. Heart Foundation Heart Age (HFHA) was evaluated on All-cause mortality (HR 1.67, 95% CI 1.62-1.71). Higher ECG-derived age was associated with increased risk of all-cause mortality (HR per SD 1.67, 95% CI 1.62-1.71) and CVD mortality (HR 1.67, 95% CI 1.61-1.73).
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