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Predicting Factors Associated with Extended Hospital Stay After Postoperative ICU Admission in Hip Fracture Patients
Volkan Alparslan1, Sibel Balcı2, Ayetullah Gök3
1Department of Anaesthesiology and Reanimation, Faculty of Medicine, Kocaeli University, 41001 Kocaeli, Turkey.
Insights
Machine learning models can predict extended hospital stays for hip fracture patients after ICU care. Key predictors include time to surgery, low albumin, and ASA score, aiding in resource management.
Area of Science:
- Geriatric Medicine
- Intensive Care Medicine
- Health Informatics
Background:
- Hip fractures are prevalent in the elderly, often necessitating intensive care unit (ICU) admission due to comorbidities.
- Prolonged hospital stay post-ICU impacts patient outcomes, complication rates, and healthcare expenditures.
- Predicting extended hospital stays is crucial for optimizing resource allocation and patient management.
Purpose of the Study:
- To develop and validate a machine learning model for predicting extended hospital stays (>7 days) in hip fracture patients post-ICU.
- To identify key factors associated with prolonged hospitalization in this patient cohort.
- To enhance clinical decision-making and ICU bed utilization strategies.
Main Methods:
- Retrospective analysis of 366 hip fracture patients admitted to a tertiary ICU.
- Application of Least Absolute Shrinkage and Selection Operator (LASSO) regression for variable selection.
- Training and evaluation of machine learning models (logistic regression, random forest, extreme gradient boosting, decision tree) using metrics like AUROC, F1 score, and accuracy.
- Utilizing SHAP values for interpreting feature importance in the best-performing model (XGBoost).
Main Results:
- The extreme gradient boosting (XGBoost) model demonstrated strong predictive performance (AUROC 0.80, recall 0.92, F1 score 0.78).
- Significant predictors for extended hospital stay included time from fracture to surgery, hypoalbuminemia, and American Society of Anesthesiologists (ASA) score.
- The model effectively classified patients into short and extended hospital stay groups.
Conclusions:
- Machine learning models can accurately predict extended hospital stays in post-ICU hip fracture patients.
- Identified predictors offer valuable insights for clinical management and resource planning.
- The developed model shows potential for improving patient flow and clinical decision support in intensive care settings.
Abstract:
Background: Hip fractures are common in the elderly and often require ICU admission post-surgery due to high ASA scores and comorbidities. Length of hospital stay after ICU is a crucial indicator affecting patient recovery, complication rates, and healthcare costs. This study aimed to develop and validate a machine learning-based model to predict the factors associated with extended hospital stay (>7 days from surgery to discharge) in hip fracture patients requiring postoperative ICU care. The findings could help clinicians optimize ICU bed utilization and improve patient management strategies. Methods: In this retrospective single-centre cohort study conducted in a tertiary ICU in Turkey (2017-2024), 366 ICU-admitted hip fracture patients were analysed. Conventional statistical analyses were performed using SPSS 29, including Mann-Whitney U and chi-squared tests. To identify independent predictors associated with extended hospital stay, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied for variable selection, followed by multivariate binary logistic regression analysis. In addition, machine learning models (binary logistic regression, random forest (RF), extreme gradient boosting (XGBoost) and decision tree (DT)) were trained to predict the likelihood of extended hospital stay, defined as the total number of days from the date of surgery until hospital discharge, including both ICU and subsequent ward stay. Model performance was evaluated using AUROC, F1 score, accuracy, precision, recall, and Brier score. SHAP (SHapley Additive exPlanations) values were used to interpret feature contributions in the XGBoost model. Results: The XGBoost model showed the best performance, except for precision. The XGBoost model gave an AUROC of 0.80, precision of 0.67, recall of 0.92, F1 score of 0.78, accuracy of 0.71 and Brier score of 0.18. According to SHAP analysis, time from fracture to surgery, hypoalbuminaemia and ASA score were the variables that most affected the length of stay of hospitalisation. Conclusions: The developed machine learning model successfully classified hip fracture patients into short and extended hospital stay groups following postoperative intensive care. This classification model has the potential to aid in patient flow management, resource allocation, and clinical decision support. External validation will further strengthen its applicability across different settings.