Key result
Combining CCTA parameters with clinical data improves MACE prediction over clinical models alone.
Why the study?
It was unknown whether anatomic and hemodynamic parameters derived from CCTA provide incremental prognostic value beyond clinical characteristics in patients with T2DM and stable CAD.
Does a combined model using CCTA-derived anatomic and hemodynamic parameters improve MACE prediction compared to a clinical model alone in T2DM patients with stable CAD?
Cohort (n=798)
Yes
Does a combined model using CCTA-derived anatomic and hemodynamic parameters improve MACE prediction compared to a clinical model alone in T2DM patients with stable CAD?
Effect estimate: C-index 0.755 (95% CI 0.562-0.849)
p-value: p=0.014; 0.042
Adding CCTA-derived anatomic and hemodynamic parameters (∆CT-FFR and CAD-RADS) to clinical characteristics significantly improves the prediction of MACE in patients with T2DM and stable CAD.
Improves MACE risk stratification in T2DM with stable CAD; extends clinical models with CCTA parameters.
OBJECTIVE: To investigate the additional prognostic value of anatomic and hemodynamic parameters derived from coronary computed tomography angiography (CCTA) over clinical characteristics in type 2 diabetes mellitus (T2DM) patients with stable coronary artery disease (CAD). MATERIALS AND METHODS: A multicenter cohort of 798 T2DM patients with stable CAD (training/test cohort: 478/320) was retrospectively included. The endpoints were major adverse cardiovascular events (MACE). Univariate and multivariate Cox regression analyses were used to identify independent clinical and CCTA-derived predictors. These independent predictors were subsequently used to construct the clinical model and the imaging model, respectively. The Coronary Artery Disease-Reporting and Data System (CAD-RADS) was used in combination with significant clinical and CCTA features to establish the combined model. The performance of the 3 models was evaluated with concordance index (C-index) and time-dependent area under the curve (time-AUC). Net reclassification improvement (NRI) was used to assess the additional prognostic value of CCTA parameters. RESULTS: In the test cohort, the clinical model, which incorporated previous myocardial infarction (prior MI) and hyperlipidemia, achieved a C-index of 0.631 (95% CI, 0.503-0.760). The imaging model constructed by the change in CT-derived fractional flow reserve (ΔCT-FFR) achieved a C-index of 0.744 (95% CI, 0.623-0.866). The combined model obtained the highest C-index of 0.755 (95% CI, 0.562-0.849). Compared with the clinical model, the combined model demonstrated superior diagnostic performance (P = 0.014; P = 0.042) in the test cohort, achieving time-AUC values of 0.683 and 0.637 for 1-year and 5-year MACE, respectively. CONCLUSIONS: The combined model based on prior MI, hyperlipidemia, ∆CT-FFR and CAD-RADS can effectively predict the risk of MACE in T2DM patients with stable CAD. CCTA-based anatomic and hemodynamic parameters have additional value in predicting MACE compared with clinical characteristics.
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Fu et al. (2026) conducted a cohort in Type 2 diabetes mellitus with stable coronary artery disease (n=798). CCTA-derived anatomic and hemodynamic parameters (∆CT-FFR and CAD-RADS) vs. Clinical characteristics alone was evaluated on Major adverse cardiovascular events (MACE) (C-index 0.755, 95% CI 0.562-0.849, p=0.014; 0.042). A combined prognostic model incorporating CCTA-derived parameters and clinical characteristics predicted MACE better than a clinical model alone (C-index 0.755 vs 0.631; P=0.014).
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