护理研究2026，Vol.40Issue(6)：894-905,12.DOI:10.12102/j.issn.1009-6493.2026.06.002
机器学习模型预测全髋关节置换术后病人谵妄风险的效能研究
Machine learning model predicts risk of postoperative delirium in total hip replacement patients
摘要
Abstract
Objective:To predict the risk of postoperative delirium in patients undergoing total hip arthroplasty using machine learning models.Methods:A total of 622 patients who underwent total hip arthroplasty in Ganzhou People's Hospital were selected as research subjects from January 2020 to December 2024.The Confusion Assessment Method(CAM)was used to assess postoperative delirium.The Boruta algorithm was employed to screen for important feature variables associated with postoperative delirium risk.Patients were randomly divided into training set(442 cases)and testing set(180 cases)at 7∶3 ratio.Nine machine learning models were constructed,trained,and validated using ten-fold cross-validation.The area under the curve(AUC)of receiver operator characteristic was used to evaluate model performance and identify the best machine learning model.Decision curve analysis was used to assess the clinical utility of the model.The SHapley additive explanations(SHAP)method,including bar plots,summary plots,dependence plots,and force plots,was used to interpret and visualize the machine learning models.Results:The incidence of postoperative delirium among the 622 patients undergoing total hip arthroplasty was 30.87%.The Boruta algorithm identified nine important postoperative delirium risk feature variables.Based on the feature importance scores(Z-values),the ranking from highest to lowest was C-reactive protein(CRP),anesthesia duration,albumin(ALB),age,total bilirubin(TB),blood glucose,intraoperative blood loss(IBL),history of diabetes,and cerebrovascular disease(CSD).Multivariate Logistic regression analysis showed that age,ALB,TB,blood glucose,CRP,and anesthesia duration were independent influencing factors for postoperative delirium in patients undergoing total hip arthroplasty(all P<0.05).The XGBoost model demonstrated excellent performance in both the training and test sets,exhibiting the strongest robustness and predictive efficacy for estimating the risk of postoperative delirium in patients undergoing total hip arthroplasty.Interpretation and visualization of the XGBoost model using SHAP revealed that the model could predict postoperative delirium risk in patients undergoing total hip arthroplasty with high accuracy.Conclusions:Age,ALB,TB,blood glucose,CRP,anesthesia duration are independent influencing factors for postoperative delirium in patients undergoing total hip arthroplasty.The XGBoost model demonstrated high predictive value for postoperative delirium in patients undergoing total hip arthroplasty.关键词
全髋关节置换术/术后谵妄/影响因素/机器学习/Boruta算法/SHapley加法解释(SHAP)/XGBoost模型Key words
total hip arthroplasty/postoperative delirium/influencing factors/machine learning/Boruta algorithm/SHapley additive explanations,SHAP/XGBoost model引用本文复制引用
张小英,刘伟,谢美英,周建国,杨佳..机器学习模型预测全髋关节置换术后病人谵妄风险的效能研究[J].护理研究,2026,40(6):894-905,12.基金项目
江西省卫生健康委计划项目,编号:202212440 （）
