AI-CCTA for Predicting Cardiovascular Events in CAD

Overview

This multicenter, retrospective registry study aims to evaluate the value of artificial intelligence (AI)-derived coronary CT angiography (CCTA) parameters in predicting long-term cardiovascular events in patients with coronary artery disease (CAD). We plan to enroll 3,000 patients from five tertiary hospitals who underwent both CCTA and invasive coronary angiography (ICA) within 90 days. Using ICA and quantitative flow ratio (QFR) as reference standards, we will compare the diagnostic performance of different commercial AI software platforms. Furthermore, we will investigate the association between AI-extracted CCTA multidimensional parameters-including high-risk plaque features, CT-derived fractional flow reserve (CT-FFR), and pericoronary fat attenuation index (FAI)-and major adverse cardiac events (MACE) during over one year of follow-up. We will also explore how lipid-lowering therapies, antiplatelet regimens, and inflammatory biomarkers modify these predictive relationships. This study is expected to provide imaging evidence for personalized risk stratification and optimized clinical decision-making in CAD management.

Background Coronary artery disease (CAD) remains the leading cause of global mortality. While coronary computed tomography angiography (CCTA) is a Class I-recommended noninvasive modality for evaluating stable chest pain, its interpretation is often limited by inter-observer variability and physician experience. Although artificial intelligence (AI) assists in automating stenosis quantification and plaque analysis, current evidence lacks large-scale, real-world comparisons between different AI platforms using invasive coronary angiography (ICA) as the gold standard. Additionally, the interplay between CCTA-derived anatomical/functional parameters, systemic inflammation, and pharmacological modifications (e.g., statins, antiplatelet agents) in predicting long-term prognosis remains unclear. Objectives This study seeks to: (1) Perform a head-to-head comparison of different commercial AI software in diagnosing obstructive CAD using ICA as the reference; (2) Explore the correlation between AI-derived CCTA parameters (plaque characteristics, CT-FFR, pericoronary fat inflammation) and major adverse cardiac events (MACE); (3) Assess the modifying effects of lipid-lowering intensity, LDL-C attainment, antiplatelet regimens, and inflammatory markers on the prognostic value of CCTA. Methods This is a multicenter, retrospective cohort study involving 3,000 patients across five centers (Nanjing First Hospital, The Second Hospital of Jilin University, etc.). Patients aged ≥18 years who underwent CCTA and ICA within 90 days between June 2013 and June 2025 will be screened. CCTA images will be analyzed using commercially available AI platforms (Shukun, United Imaging) to extract parameters including stenosis grading, plaque volume, CT-FFR, and pericoronary fat attenuation index (FAI). Clinical data, laboratory results (lipids, hs-CRP, NT-proBNP), and medication histories will be retrospectively collected. The primary endpoint is MACE (a composite of cardiac death, myocardial infarction, unplanned revascularization, and hospitalization for unstable angina/heart failure). Follow-up duration will be at least one year. Analysis Sensitivity, specificity, and AUC will be calculated for AI-CCTA against ICA. Survival analysis (Kaplan-Meier and Cox regression) will be used to identify independent predictors of MACE. Interaction terms will be employed to evaluate the modifying effects of pharmacotherapies. Statistical analysis will be performed by the Department of Biostatistics, Nanjing Medical University. Significance By integrating anatomical, functional, and inflammatory dimensions of CCTA with real-world therapeutic data, this study aims to establish a comprehensive risk stratification framework for CAD, potentially guiding precision medicine approaches in clinical practice.