Abstract

Background: Prolonged intensive care unit (ICU) stays are associated with increased morbidity, resource utilization, and cost. Early identification of patients at risk for extended ICU length of stay (LOS) can support clinical decision-making and improve resource management.
Objective: To develop and evaluate a machine learning model to predict ICU LOS using routine laboratory tests available early during admission.
Methods: We conducted a retrospective study using electronic health record data from adult ICU patients. Evaluated the predictive performance of four machine learning (ML) models to choose the best model, which was trained on a set of demographic and laboratory tests’ results to predict LOS category. Model performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity on a validation set.
Results: The XGBoost model demonstrated the highest accuracy (90%) and Kappa (79%) among the four evaluated models. On the testing data, XGBoost had an accuracy of 87.5%, sensitivity 88%, specificity 87.1%, and AUC of 95.3%. The top five important predictor variables were blood glucose, arterial partial pressure of oxygen (PaO2), arterial partial pressure of carbon dioxide (PaCO2), body mass index (BMI), and age. Diagnostic accuracy measures on the validation data were: Accuracy = 83.9%, sensitivity = 79.4%, specificity = 88%, and AUC = 92.5%
Conclusion: Machine learning can effectively predict ICU length of stay early in the course of admission. Such models could aid clinicians in identifying patients at risk for prolonged ICU stays, facilitating proactive discharge planning and ICU resource optimization. Future studies should focus on external validation and real-world implementation.

Keywords

Machine learning prediction