Development and validation of a machine learning-based prediction model for intraoperative hypothermia in Chinese patients undergoing gastrointestinal surgery.

ABSTRACT:

Background

Intraoperative hypothermia, defined as a core temperature < 36.0 °C, is a common complication during gastrointestinal surgery, potentially affecting patient outcomes. This study aimed to develop a predictive model for intraoperative hypothermia based on clinical features.

Methods

In this retrospective, single-center study, we analyzed data from 418 patients who underwent gastrointestinal surgery between January and March 2024 at the First Hospital of Putian City, China. Core temperature was measured intraoperatively using a deep nasal probe every 15 min. Five machine learning models (logistic regression, random forest, support vector machine, XGBoost, and multilayer perceptron) were evaluated to develop a prediction model for hypothermia. Logistic regression (LR)

SUBMITTER: Huang L 

PROVIDER: S-EPMC12487104 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature