A Machine Learning–Based Risk Stratification Model for Predicting Perioperative Blood Transfusion in Fracture Neck of Femur Surgery

Background Hip fracture surgery in older adults is frequently associated with substantial blood loss, often necessitating perioperative blood transfusion. While transfusion can be life-saving, it is associated with increased morbidity, prolonged hospitalization, and higher healthcare costs. Accurate preoperative prediction of transfusion risk may improve patient blood management and perioperative planning. This study aimed to develop and internally validate a machine learning–based model to predict transfusion risk in patients undergoing hip fracture surgery. Methods We retrospectively studied 139 patients aged ≥65 years who underwent surgery for fracture neck of femur. Preoperative variables including age, packed cell volume (PCV), American Society of Anesthesiologists (ASA) grade, comorbidities, and surgical procedure type were used to develop a Random Forest model. Model performance was assessed using five-fold cross-validation and an independent test set. A multivariable logistic regression model was developed as a comparator. Patients were stratified into low-, intermediate-, and high-risk groups based on predicted probabilities. Results The Random Forest model demonstrated good discrimination during cross-validation (mean AUC–ROC 0.821) and excellent performance on the test set (AUC–ROC 0.966). At an optimal threshold of 0.60, sensitivity was 100% and specificity was 88.9%. Risk stratification revealed transfusion rates of 0%, 25%, and 85.7% in the low-, intermediate-, and high-risk groups, respectively. Age and preoperative PCV were the most influential predictors. The logistic regression model showed lower discriminative performance. Conclusion A Random Forest machine learning model using routinely available preoperative variables accurately predicts perioperative transfusion risk following fracture neck of femur surgery. This internally validated risk stratification approach supports individualized patient blood management and optimized perioperative resource allocation.