Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

ABSTRACT:

Background

Numerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratification of STEMI patients using machine learning algorithms.

Methods

A total of 1355 STEMI patients who underwent percutaneous coronary intervention were enrolled in this study during 2015-2018. Unsupervised machine learning (consensus clustering) was applied to the present cohort to classify patients into different lipid expression phenogroups, without the guidance of clinical outcomes. Kaplan-Meier curves were implemented to show prognosis during a 904-day median follow-up (interquartile range: 587-1316). In the adjusted Cox model

SUBMITTER: Xue Y 

PROVIDER: S-EPMC8101132 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature