Multimodal prediction of major adverse cardiovascular events in hypertensive patients with coronary artery disease: integrating pericoronary fat radiomics, CT-FFR, and clinicoradiological features.

Abstract

Purpose: People with both hypertension and coronary artery disease (CAD) are at a significantly increased risk of major adverse cardiovascular events (MACEs). This study aimed to develop and validate a combination model that integrates radiomics features of pericoronary adipose tissue (PCAT), CT-derived fractional flow reserve (CT-FFR), and clinicoradiological features, which improves MACE prediction within two years.

Materials and methods: Coronary-computed tomography angiography data were gathered from 237 patients diagnosed with hypertension and CAD. These patients were randomly categorized into training and testing cohorts at a 7:3 ratio (165:72). The least absolute shrinkage and selection operator logistic regression and linear discriminant analysis method were used to select optimal radiomics characteristics. The predictive performance of the combination model was assessed through receiver operating characteristic curve analysis and validated via calibration, decision, and clinical impact curves.

Results: The results reveal that the combination model (Radiomics.

Clinical: Imaging) improves the discriminatory ability for predicting MACE. Its predictive efficacy is comparable to that of the Radiomics.Imaging model in both the training (0.886 vs. 0.872) and testing cohorts (0.786 vs. 0.815), but the combination model exhibits significantly improved specificity, accuracy, and precision. Decision and clinical impact curves further confirm the use of the combination prediction model in clinical practice.

Conclusions: The combination prediction model, which incorporates clinicoradiological features, CT-FFR, and radiomics features of PCAT, is a potential biomarker for predicting MACE in people with hypertension and CAD.