PREDICTING UNPLANNED TRAUMA INTENSIVE CARE UNIT ADMISSIONS FOR INITIAL NONOPERATIVE, NON-ICU PATIENTS.

ABSTRACT:

Abstract

Introduction: Unplanned intensive care unit (ICU) admissions are associated with increased morbidity and mortality. This study uses interpretable machine learning to predict unplanned ICU admissions for initial nonoperative trauma patients admitted to non-ICU locations. Methods: TQIP (2020-2021) was queried for initial nonoperative adult patients admitted to non-ICU locations. Univariable analysis compared patients who required an unplanned ICU admission to those who did not. Using variables that could be known at hospital admission, gradient boosting machines (CatBoost, LightGBM, XGBoost) were trained on 2021 data and tested on 2020 data. SHapley Additive exPlanations (SHAP) were used for interpretation. Results: The cohort had 1,107,822 patients; 1.6% had an unplanned IC

SUBMITTER: Zander T 

PROVIDER: S-EPMC11753924 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature