Machine learning-based risk stratification for gastrointestinal bleeding in ICU patients with cirrhosis: evidence from the MIMIC database.

ABSTRACT:

Background

In critically ill patients with cirrhosis, gastrointestinal bleeding (GIB) is a common complication that significantly impacts clinical outcomes during ICU hospitalization. Early identification of high-risk patients is crucial for preventing complications and guiding appropriate clinical interventions, which can improve treatment outcomes.

Objective

To develop and externally validate a machine learning model for predicting in-hospital GIB in ICU patients with cirrhosis, identify key predictors, and assess its clinical utility for risk stratification and decision-making.

Methods

A retrospective cohort study was conducted, including 3,160 ICU patients diagnosed with cirrhosis from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patient

SUBMITTER: Duan Y 

PROVIDER: S-EPMC12714975 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature