CatLet Score as a Predictor of Cardiac Death in Patients With Acute Myocardial Infarction: Insights From Interpretable Machine Learning Models.

ABSTRACT:

Background

Predicting cardiac death in patients with acute myocardial infarction (AMI) remains a major challenge. The Coronary Artery Tree description and lesion evaluation (CatLet) angiographic scoring system can describe the variability in coronary artery anatomy, the degree of stenosis of the affected coronary artery, and the subtended myocardial territory. Therefore, this study aimed to establish an effective and interpretable machine learning (ML) model to explore the relationship between the CatLet score and cardiac death in patients with AMI.

Methods

The CatLet score was calculated in 767 consecutively enrolled patients with AMI. Cox regression analysis, Kaplan-Meier survival analysis, and restricted cubic spline analysis were used to explore the association between t

SUBMITTER: Lin XH 

PROVIDER: S-EPMC12781014 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature