南京医科大学学报（自然科学版）2026，Vol.46Issue(2)：247-255,9.DOI:10.7655/NYDXBNSN250166
基于多种机器学习算法构建并验证维持性血液透析患者全因死亡风险预测模型
Development and validation of an all-cause mortality risk prediction model utilizing multiple machine learning algorithms for maintenance hemodialysis patients
摘要
Abstract
Objective:To construct and validate prediction models for all-cause mortality in maintenance hemodialysis(MHD)patients using diverse machine learning algorithms.Methods:Clinical data were collected from 694 patients across four hemodialysis centers in Jiangsu Province,including 591 MHD patients from three tertiary Grade A hospitals in Wuxi City(January 2017-December 2023)and 103 patients from one secondary Grade A hospital in Yancheng City(January-December 2024).The 591 cases were randomly divided into a training set(n=414)and a validation set(n=177)at a 7∶3 ratio for model development and internal validation,while the remaining 103 cases served as a test set for external validation.Predictors were selected via the least absolute selection and shrinkage operator(LASSO)method.Patients were randomly divided into training(n=414)and validation(n=177)sets.Ten machine learning algorithms were employed to develop risk prediction models.Receiver operating characteristic(ROC)curves were plotted to evaluate predictive performance.The calibration accuracy of model-predicted probabilities was assessed using calibration curves,while decision curve analysis(DCA)was employed to quantify the clinical net benefit across varying decision thresholds.External validation utilized the area under the curve(AUC)to assess the generalization capability of the optimal model.Shapley Additive exPlanations(SHAP)were applied to rank variable importance.Results:The all-cause mortality rate was 42.6%(252/591).Among the 10 models,the support vector machine(SVM)exhibited optimal performance,the AUC was 0.928,the sensitivity was 89.47%,and the accuracy was 0.919,and the evaluation of calibration curve and DCA showed that the consistency and benefit of the model are still good,the Brier score of 0.089 indicates that the model demonstrates low predictive error and favorable calibration performance on the internal validation dataset,suggesting its reliability in probabilistic forecasting.External validation yielded an AUC of 0.835,indicating robust generalization capability of the model.The SHAP plot showed that the importance ranking of the influencing factors for all-cause mortality was living alone,tunneled cuffed catheter(TCC),prealbumin,albumin,Charlson comorbidity index(CCI)score,iPTH＜300 pg/mL,age,junior high school education or lower,blood urea nitrogen-to-creatinine ratio,diabetic nephropathy,college degree or higher education and sex.Conclusion:The SVM-based prediction model demonstrates robust performance in forecasting all-cause mortality among MHD patients,facilitating early identification of high-risk individuals and supporting clinical decision-making.关键词
机器学习算法/血液透析/全因死亡/预测模型/验证Key words
machine learning algorithms/hemodialysis/all-cause mortality/prediction model/validation分类
医药卫生引用本文复制引用
王娇,周怡君,孙文娟,周静怡,王依娜..基于多种机器学习算法构建并验证维持性血液透析患者全因死亡风险预测模型[J].南京医科大学学报（自然科学版）,2026,46(2):247-255,9.基金项目
无锡市护理学会科研项目(Q202303) （Q202303）
无锡市青年科技人才托举项目(TJXD-2024-210) （TJXD-2024-210）
江苏医药职业学院校本课题(20229JH35) （20229JH35）
