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 c

SUBMITTER: Tang Y 

PROVIDER: S-EPMC12512623 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature