Machine learning-based prediction of myocardial injury after hip replacement surgery: a single-center study with internal validation

This study aimed to develop and internally validate a preoperative machine learning model for predicting myocardial injury after hip replacement surgery in middle-aged and elderly patients. A total of 453 middle-aged and elderly patients were included. The clinical outcome was myocardial injury after non-cardiac surgery (MINS). Feature selection was performed within the training set using recursive feature elimination. Based on the selected variables, nine machine learning models were constructed and compared. Model performance was evaluated using cross-validated AUC, repeated random split validation, calibration analysis, decision curve analysis, and clinical impact curves. SHAP analysis was used to interpret feature contributions. Eight preoperative features were ultimately retained for model construction. Among the evaluated models, LightGBM showed the best overall performance. The mean AUC from cross-validation and repeated random split validation was approximately 0.79, indicating moderate discriminative ability and providing the primary estimate of internal generalizability. In the single held-out internal test set, LightGBM achieved an AUC of 0.9468 (95% CI: 0.897–0.992), although this result was interpreted cautiously because of the limited sample size and small number of positive events. The model also showed a relatively low Brier score of 0.061 and favorable net benefit on decision curve analysis within clinically relevant threshold ranges. SHAP analysis indicated that age was the most influential predictor of MINS. This single-center study developed and internally validated a preoperative machine learning model for predicting myocardial injury after hip replacement surgery, which may support early risk stratification before surgery. Not applicable.