Objectives We explored the value of combining coronary computed tomography angiography (CCTA)-derived features with clinical factors to predict all-cause mortality after (transcatheter aortic valve replacement, TAVR).
Methods A total of 380 candidates for CCTA examined between September 2017 and September 2023 were enrolled in this multicenter retrospective cohort study with clinical end point being all-cause mortality. Univariate and multivariate Cox regression analyses were performed to identify independent clinical and CCTA-derived predictors of post-TAVR mortality to construct clinical or imaging factor only models and a combined factor model. Model performance was evaluated using time-dependent area under the receiver operating characteristic curve (time-AUC) and Harrell’s concordance index (C-index). To compare nested models, \chi^2 likelihood ratio analyses were performed.
Results Median follow-up duration was 706 days. All-cause mortality occurred in 13% (48/380) of the patients. The combined model (age + diabetes + coronary artery disease reporting and data system, (CAD-RADS) + (segment stenosis score, SSS) + (left anterior descending artery-fat attenuation index, LAD-FAI)) demonstrated superior predictive performance for all-cause mortality with higher C-index (0.690, 95% confidence interval CI: 0.590-0.772) compared with the clinical model (age + diabetes) (C-index, 0.630, 95% CI: 0.538-0.718). Likelihood ratio tests confirmed significantly better fit of the combined model than the clinical model. SHAP analysis indicated that LAD-FAI and age were the most influential factors contributing to model prediction accuracy.
Conclusion A combined model incorporating CCTA features and clinical risk factors can improve all-cause mortality prediction efficacy in patients after TAVR.
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