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: Patients in the DAA group exhibited significantly higher age, soft tissue thickness, neck-shaft angle (NSA), and femoral offset compared to those in the PLA group (all 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.