Enhancing prognostic accuracy in sepsis-induced cardiomyopathy: a machine learning approach.

ABSTRACT:

Background

Sepsis-induced cardiomyopathy (SIMD) is a severe yet potentially reversible complication of sepsis, characterized by myocardial dysfunction and associated with high short-term mortality. Conventional scoring systems and traditional statistical models inadequately capture the complex pathophysiology of SIMD, highlighting the need for robust prognostic tools.

Methods

We retrospectively analyzed 1068 adult SIMD patients from the MIMIC-IV database, of whom 236 (22.1%) died within 28 days of ICU discharge. Candidate predictors were screened using Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), and Recursive Feature Elimination with Cross-Validation (RFECV). Eight machine learning algorithms were developed and compared. Model performance was evaluated w

SUBMITTER: Li X 

PROVIDER: S-EPMC12784521 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature