Development and Validation of a Machine Learning–Based Prediction of Chronic Postsurgical Pain Following Total Hip and Knee Arthroplasty: An Exploratory Study
Yuan Cao, Yinghua Hu, Tianlun Gong, Zaijie Sun, Zijian Wang, Ximing ZhangIntroduction
Chronic postsurgical pain (CPSP) is a concern following total hip arthroplasty (THA) and total knee arthroplasty (TKA), substantially diminishing surgical benefits. We aimed to develop a machine learning (ML) prediction model for risk of developing CPSP.
Methods
This cohort study included 383 patients recruited from a tertiary medical center between March 2022 and July 2024. Dataset was divided into a training set and a testing set. We used Lasso regression for feature selection and developed prediction models using six ML algorithms: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBOOST), support vector machine (SVM), multilayer perceptron (MLP), and k‐nearest neighbor (KNN). Model performance was evaluated using metrics such as receiver operator characteristic (ROC) curve and precision–recall (PR) curves, accuracy, sensitivity, and specificity. The optimal model was deployed on a web application using Shiny, and Shapley additive explanations (SHAP) analysis was used to interpret model predictions.
Results
Among the 383 eligible patients, the median age was 67.4 years, and 58.0% were women. The incidence of CPSP in TKA and THA was 26.0% and 12.7% respectively. Four predictors were selected: preoperative pain catastrophizing scale (PRO‐PCS), preoperative Tampa Scale for Kinesiophobia (PRO‐TSK), preoperative numerical rating scale (PRO‐NRS) at rest, and time to first ambulation. In the testing set, the RF model demonstrated the best performance with ROC‐AUC of 0.872 and PR‐AUC of 0.773. The accuracy, sensitivity, and specificity were 0.90, 0.77, and 0.94, respectively.
Conclusion
This study developed a clinically applicable ML model for CPSP prediction for THA, while the CPSP prediction for TKA was only conducted for exploration and still requires further verification.