Machine learning-based integration of pericoronary adipose tissue and clinical risk factors for cardiovascular risk prediction in type 2 diabetes: a retrospective cohort study

作者：Yuqing Tang, Xuankun Zheng, Xiaofei Yang, Sien Guo, Qi-yuan Luo, Meiyi Su, Huiqi Chen, Zhou Wu, Hong‐Qin Wang, Yue Liu, Guoqing Liu, Lei Wang · 发表于：European journal of medical research · 年份：2025 · DOI：10.1186/s40001-025-03237-4 · 被引用次数：4 · 研究领域：Cardiovascular Disease and Adiposity、Adipokines, Inflammation, and Metabolic Diseases、Adipose Tissue and Metabolism

BACKGROUND: Cardiovascular disease remains the predominant cause of morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). Traditional risk models are limited in predictive accuracy. Pericoronary adipose tissue (PCAT), a novel imaging biomarker of vascular inflammation, may offer additional prognostic value. Therefore, this study aimed to develop and validate a machine learning model that integrates PCAT parameters with clinical risk factors to improve the accuracy of cardiovascular risk prediction in individuals with T2DM. METHODS: This study retrospectively enrolled 686 hospitalized T2DM patients from four branches of Guangdong Provincial Hospital of Chinese Medicine between January 2017 and December 2021. PCAT-FAI and volume index were measured using coronary CTA. Major adverse cardiovascular events (MACE) were recorded during follow-up. Eight machine learning algorithms were applied, and multiple evaluation metrics were used to compare the predictive performance of the models. Feature contributions in the best-performing model were interpreted using both feature importance ranking and SHapley Additive exPlanations (SHAP) values. RESULTS: A total of 183 patients experienced MACE during the mean 38.4 months of follow-up. Among the eight machine learning models evaluated, the XGBoost model performed the best in predicting MACE in patients with T2DM. In the internal validation of the training set, the AUC was 0.818 (95% CI 0.777-0.858), and in the extern...