基于XGBoost-SHAP方法的急性主动脉夹层患者术后死亡风险模型的构建
Construction of a postoperative mortality risk model for patients with acute aortic dissection based on XGBoost-SHAP method
摘要
Abstract
Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.关键词
机器学习/预测模型/急性主动脉夹层/术后死亡Key words
machine learning/prediction model/acute aortic dissection/postoperative mortality分类
临床医学引用本文复制引用
张新,王楷文,韩铁生,赵永波,马冬,方敏,曹轶,李婷婷,刘献孔,党嘉毅,赵雪森,任洪钦,耿佳泽..基于XGBoost-SHAP方法的急性主动脉夹层患者术后死亡风险模型的构建[J].解放军医学杂志,2025,50(10):1226-1234,9.基金项目
国家自然科学基金(82270508) （82270508）
河北省自然科学基金面上项目(H2022206279) （H2022206279）
神经与血管生物学教育部重点实验室主任基金青年基金项目(NV20210006) （NV20210006）
河北省高等学校科学技术研究项目(QN2022164) （QN2022164）
河北省医学科学研究重点项目(20221293) （20221293）
河北省卫生健康委政府资助临床医学优秀人才培养项目(ZF2025226).This work was supported by the National Natural Science Foundation of China(82270508),the Hebei Provincial Natural Science Foundation General Project(H2022206279),the Youth Fund Project of the Director's Fund of the Key Laboratory of Neurobiology and Vascular Biology of the Ministry of Education(NV20210006),the Science and Technology Research Project of Higher Education Institutions in Hebei Province(QN2022164),the Key Project of Medical Science Research of Hebei Province(20221293),and the Hebei Provincial Health Commission Government-Funded Project for Cultivating Outstanding Talents in Clinical Medicine(ZF2025226) （ZF2025226）
