Predicting the rapid progression of coronary artery lesions in patients with acute coronary syndrome based on machine learning.

ABSTRACT:

Purposes

Rapid coronary artery lesions (RCAL) are strongly linked to major adverse cardiovascular events in patients with acute coronary syndrome (ACS). This work developed a public online prediction platform for RCAL (OpRCAL) by comparing the performance of nine machine learning models.

Methods

We retrospectively examined the clinical data of 324 patients with ACS who received percutaneous coronary intervention (PCI). Using both univariate and multivariate analyses, the potential independent risk factors for RCAL were studied. Following the screening of all variables using Lasso regression, multiple machine learning models were constructed. The optimal model was then chosen and validated using an external cohort. Furthermore, to elucidate the contribution of each feature to

SUBMITTER: Gui L 

PROVIDER: S-EPMC12447524 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature