AI-Based LOS Prediction in Hip Fracture Patients

Prediction of Length of Hospital Stay in Hip Fracture Patients After Post-Anesthesia Care Unit Using Artificial Intelligence

With increasing life expectancy, the elderly population is growing. Hip fractures significantly increase morbidity and mortality, particularly within the first year, among elderly patients. Managing anesthesia in these elderly patients, who often have multiple comorbidities, is challenging. Identifying perioperative factors that can reduce mortality will benefit the perioperative management of these patients.

The aim of this study is to develop and validate a machine learning based model to predict the length of hospital stay for hip fracture patients after PACU. Different machine learning algorithms such as R language Gradient Boosting, Random Forest, Artificial Neural Networks and Logistic Regression will be used in the study and the best performing model will be determined. In addition, the prediction mechanism of the model will be examined with SHAP analysis and its applicability in clinical decision processes will be evaluated. Thus, by predicting the length of hospital stay, clinicians will be enabled to manage patient care processes more effectively.

Inclusion Criteria:

• Patients who underwent hip fracture surgery at our institution between 2017 and 2024
• Patients aged 65 years or older
• Patients with hip fractures resulting from a low-energy trauma (simple fall from standing height)

Exclusion Criteria:

• Patients with pathological hip fractures due to malignancy
• Cancer patients with multiple organ metastases
• Patients who underwent revision hip fracture surgery