Prediction of obstructive coronary artery disease using coronary calcification and epicardial adipose tissue assessments from CT calcium scoring scans.

ABSTRACT:

Background

Low-cost/no-cost non-contrast CT calcium scoring (CTCS) exams can provide direct evidence of coronary atherosclerosis. In this study, using features from CTCS images, we developed a novel machine learning model to predict obstructive coronary artery disease (CAD), as defined by the coronary artery disease-reporting and data system (CAD-RADS).

Methods

This study analyzed 1324 patients from the SCOT-HEART trial who underwent both CTCS and CT angiography. Obstructive CAD was defined as CAD-RADS 4A-5, while CAD-RADS 0-3 were considered non-obstructive CAD. We analyzed clinical, Agatston-score-derived, and epicardial fat-omics features to predict obstructive CAD. The most predictive features were selected using elastic net logistic regression and used to train a CatBoo

SUBMITTER: Lee J 

PROVIDER: S-EPMC12178320 | biostudies-literature | 2025 Mar-Apr

REPOSITORIES: biostudies-literature