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

Abstract

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 external test set, the AUC was 0.809 (95% CI 0.700-0.918). Additionally, the XGBoost model outperforms other models in all evaluation metrics (accuracy = 0.824, specificity = 0.882, F1 score = 0.654, Brier score = 0.248). In the feature importance analysis of the prediction model, RCA-FAI in the PCAT parameters consistently ranked among the top three in eight ML models. Further SHAP analysis indicated that RCA-FAI, body mass index (BMI), and the monocyte/high-density lipoprotein cholesterol ratio (MHR) were the most influential factors for MACE in patients with T2DM.

Conclusion: This study demonstrates the independent predictive value of PCAT parameters for long-term cardiovascular risk in patients with T2DM. The XGBoost model showed promise as a potential clinical decision support tool. Integrating PCAT parameters with conventional risk factors may improve the identification of high-risk individuals and enhance the ability to predict MACE in this population. Clinical trial registration ChiCTR2400079869.