Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator.

ABSTRACT:

Background

Sepsis-associated delirium (SAD) occurs due to disruptions in neurotransmission linked to inflammatory responses from infections. It poses significant challenges in clinical management and is associated with poor outcomes. Survivors often experience long-term cognitive and behavioral issues that impact their quality of life and place a burden on their families. This study aimed to develop and validate an interpretable machine learning model for early prediction of SAD in critically ill patients. Additionally, we constructed an online risk calculator to facilitate real-time clinical assessment.

Methods

This study is a retrospective analysis utilizing data from 16,120 patients in the Medical Information Mart for Intensive Care IV database. To manage imbalanced data,

SUBMITTER: Gao L 

PROVIDER: S-EPMC12266397 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature