Development and validation of machine learning models for predicting extubation failure in patients undergoing cardiac surgery: a retrospective study.

ABSTRACT: Patients with multiple comorbidities and those undergoing complex cardiac surgery may experience extubation failure and reintubation. The aim of this study was to establish an extubation prediction model using explainable machine learning and identify the most important predictors of extubation failure in patients undergoing cardiac surgery. Data from 776 adult patients who underwent cardiac surgery and were intubated for more than 24 h were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The primary endpoint was extubation failure according to the WIND criteria, with 205 patients experiencing extubation failure. The data was split into a training set (80%) and a test set (20%). The performance of the XGBoost algorithm was the highest (AUC 0.793, Mean Pre

SUBMITTER: Jiang X 

PROVIDER: S-EPMC11903652 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature