Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery.

ABSTRACT:

Background

Unplanned postoperative intensive care unit admissions (UIAs) are rare events that cause significant challenges to perioperative workflow. We describe the development of a machine-learning derived model to predict UIAs using only widely used preoperative variables.

Methods

This was a 3-year retrospective review of all adult surgeries under the General, Vascular, and Thoracic surgical services with anticipated length of greater than 180 minutes at a single institution. A UIA was defined as any post-operative patient recovering in the post-anesthesia care unit (PACU) requiring direct transfer to the intensive care unit (ICU) for higher level of care. We developed our prediction model with a gradient-boosting decision tree algorithm (XGBoost). The model incorporated

SUBMITTER: Chiu C 

PROVIDER: S-EPMC12273467 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature