Deep learning-derived biological age from preoperative chest radiographs predicts mortality following surgical and transcatheter procedures for structural heart disease beyond EuroSCORE II

Accurate risk stratification before structural heart disease interventions is essential for clinical decision-making. Traditional risk models, such as the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) and Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), were designed for surgical patients and show inconsistent performance in transcatheter cohorts. Biological age, reflecting cumulative physiological decline, may offer prognostic value beyond chronological age and established risk scores.

In this retrospective study of 1269 patients [non-transcatheter aortic valve implantation (TAVI) n = 751, TAVI n = 518] treated at the German Heart Center Munich, biological age was estimated from pre-operative chest radiographs using CXR-Age, a validated deep learning model. Analyses were conducted separately for surgical (non-TAVI) and transcatheter (TAVI) groups. For 30-day mortality, biological age outperformed EuroSCORE II in both subgroups [area under the receiver operating characteristic curve (AUC): non-TAVI 0.874 vs. 0.785, P < 0.001; TAVI 0.952 vs. 0.745, P = 0.004] and remained independently predictive after adjustment [TAVI OR 1.58 per year, 95% confidence interval (CI) 1.27–2.12]. While STS-PROM was the strongest single predictor for non-TAVI patients (AUC 0.949), it was similar to EuroSCORE II for TAVI patients (AUC 0.729). Notably, patients whose biological age exceeded their chronological age by more than 10 years faced higher major complication rates (17.3% vs. 9.2%; P = 0.016).

Biological age distinguished risk across both populations, suggesting that deep learning-based biological age estimation from routine chest radiographs could serve as an automated, accessible complement to existing risk models.