Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.

ABSTRACT: Objective The present study evaluated the usefulness of machine learning (ML) models with the coronary computed tomography imaging and clinical parameters for predicting major adverse cardiac events (MACEs). Methods The Nationwide Gender-specific Atherosclerosis Determinants Estimation and Ischemic Cardiovascular Disease Prospective Cohort study (NADESICO) of 1,187 patients with suspected coronary artery disease 50-74 years old was used to build a MACE prediction model. The ML random forest (RF) model was compared with a logistic regression analysis. The performance of the ML model was evaluated using the area under the curve (AUC) with the 95% confidence interval (CI). Results Among 1,178 patients from the NADESICO dataset, MACEs occurred in 103 (8.7%) patients during a median follow-up o

SUBMITTER: Wada S 

PROVIDER: S-EPMC12021509 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature