Outcome prediction for adult mechanically ventilated patients using machine learning models and comparison with conventional statistical methods: a single-centre retrospective study

Abstract

Abstract In this retrospective single-centre study spanning five years (2016–2021) and involving 2,368 adult Intensive Care Unit (ICU) patients requiring over four hours of mechanical ventilation (MV) in a tertiary care hospital, we investigated the feasibility and accuracy of using machine learning (ML) models in predicting outcomes post-ICU discharge compared to conventional statistical methods (CSM). The study aimed to identify associated risk factors impacting these outcomes. Poor outcomes, defined as ICU readmission, mortality, and prolonged hospital stays, affected 40.2% of the discharged MV patients. The Extreme Gradient Boost (XGBoost) ML model showed superior performance compared to CSM (Area under the receiver operating characteristic curve: 0.693 vs. 0.667; p-value = 0.03). At 95% specificity, XGBoost displayed enhanced sensitivity (30.6% vs. 23.8%) and accuracy (0.706 vs. 0.703) compared to CSM. Risk factors such as Glasgow Coma Score at ICU discharge, GCS best motor score during ICU admission, MV duration, ICU length of stay, and Charlson Comorbidity Index were identified. While both ML and CSM exhibited moderate accuracy, the study suggests ML algorithms have the potential for better predictive capabilities and individual risk factor identification, potentially aiding in the improvement of patient outcomes by identifying high-risk patients requiring closer monitoring. Further validation in larger studies is necessary, but the study underscores the potential for real-time application of ML algorithms developed from the increasing availability of electronic medical records (EMR).