A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms

作者：Yixiang Zhang, Sen He, Tao Yang, Haoliang Li, Chunlei Wu, Lei Wang, Xiaoquan Wang, Jun Liu · 发表于：Frontiers in Medicine · 年份：2025 · DOI：10.3389/fmed.2025.1699842 · 被引用次数：2 · 研究领域：Total Knee Arthroplasty Outcomes、Obstructive Sleep Apnea Research、Sleep and related disorders

Background: Postoperative sleep disturbance (PSD) is a common complication following total knee arthroplasty (TKA), which negatively impacts patient recovery. Despite the critical need for early detection and management, there is limited research on predictive models for early PSD, particularly those integrating machine learning (ML) techniques. Objective: This study aimed to develop a predictive model for early PSD following TKA using ML algorithms, identify key predictive factors, and provide an interpretable model to guide clinical decision-making. Methods: The study included 505 patients who underwent TKA. Clinical data were collected at three stages: preoperatively, intraoperatively, and postoperatively. Ten MLa models, including logistic regression, support vector machine (SVM), and XGBoost, were trained and evaluated using a test set. Performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were used to evaluate the efficacy of the models. Key features influencing PSD were identified through SHapley Additive Explanations (SHAP) analysis to enhance model interpretability. Results: Gradient Boosting Machine (GBM) demonstrated the highest AUC (0.906), accuracy (0.834), and sensitivity (0.879), establishing it as the optimal model for predicting PSD. Key predictors identified included age, smoking, living alone, living in the city, VAS 1 month postoperative, and anxiety 1 month postoperative. SHAP analysis revealed that postoperati...