Prognostic analysis of sepsis-induced myocardial injury patients using propensity score matching and doubly robust analysis with machine learning-based risk prediction model development.

ABSTRACT:

Background

Sepsis-induced myocardial injury (SIMI) is a severe and common complication of sepsis; However, its definition remains unclear. Prognostic analyses may vary depending on the definition applied. Early prediction of SIMI is crucial for timely intervention, ultimately improving patient outcomes. This study aimed to evaluate the prognostic impact of SIMI and develop validated predictive models using advanced machine learning (ML) algorithms for identifying SIMI in critically ill sepsis patients.

Methods

Data were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.0) database. Patients meeting Sepsis-3.0 criteria were included, and SIMI was defined as troponin T (TNT) levels ≥0.1 ng/mL. Prognostic evaluation involved propensity score matching

SUBMITTER: Guo P 

PROVIDER: S-EPMC11880261 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature