Clinical and radiographic factors associated with surgical approach selection in total hip arthroplasty: a preliminary machine learning analysis

Objective: To investigate the clinical and radiographic factors associated with surgeon selection of the direct anterior approach (DAA) versus the posterolateral approach (PLA) in total hip arthroplasty (THA), and to explore whether machine learning methods can characterize historical surgical selection patterns. Methods: A retrospective analysis was performed on 98 patients who underwent primary THA at two institutions, including 61 patients in the PLA group and 37 patients in the DAA group. Patient demographics and preoperative radiographic parameters were collected. Baseline comparisons and univariate logistic regression analyses were conducted using SPSS. A machine learning classification model reflecting historical surgeon-selected approaches was constructed using the XGBoost algorithm, and its performance was evaluated via receiver operating characteristic (ROC) curves, five-fold cross-validation, SHAP analysis, and calibration curves. Results: < 0.05). Univariate logistic regression revealed that age, NSA, and femoral offset were significantly associated with DAA selection. The XGBoost model achieved an area under the curve (AUC) of 0.938 and an accuracy of 90.0% on the test set, with a mean AUC of 0.897 via five-fold cross-validation. SHAP analysis identified osteoporosis, Dorr classification, age, and NSA as key contributors to model predictions. The model exhibited good calibration, as indicated by a Brier score of 0.0898. Conclusion: The present study identified several clinical and radiographic factors associated with historical surgeon selection of DAA versus PLA in THA. The machine learning model demonstrated the ability to characterize real-world surgical selection patterns, although it should not be interpreted as recommending the optimal surgical approach for individual patients.