Development and validation of machine learningbased in-hospital mortality predictive models for acute aortic syndrome in emergency departmentsOA
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.
Yuanwei Fu;Yilan Yang;Hua Zhang;Daidai Wang;Qiangrong Zhai;Lanfang Du;Nijiati Muyesai;YanxiaGao;Qingbian Ma
Department of Emergency Medicine,Peking University Third Hospital,Beijing 100191,China Key Laboratory of Molecular Cardiovascular Sciences,Ministry of Education,Beijing 100191,ChinaDepartment of Emergency Medicine,Peking University Third Hospital,Beijing 100191,China Key Laboratory of Molecular Cardiovascular Sciences,Ministry of Education,Beijing 100191,ChinaResearch Center of Clinical Epidemiology,Peking University Third Hospital,Beijing 100191,ChinaDepartment of Emergency Medicine,Peking University Third Hospital,Beijing 100191,China Key Laboratory of Molecular Cardiovascular Sciences,Ministry of Education,Beijing 100191,ChinaDepartment of Emergency Medicine,Peking University Third Hospital,Beijing 100191,China Key Laboratory of Molecular Cardiovascular Sciences,Ministry of Education,Beijing 100191,ChinaDepartment of Emergency Medicine,Peking University Third Hospital,Beijing 100191,China Key Laboratory of Molecular Cardiovascular Sciences,Ministry of Education,Beijing 100191,ChinaDepartment of Emergency Medicine,People''s Hospital of Xinjiang Uygur Autonomous Region,Urumqi 830001,ChinaDepartment of Emergency Medicine,the First Affi liated Hospital of Zhengzhou University,Zhengzhou 450052,ChinaDepartment of Emergency Medicine,Peking University Third Hospital,Beijing 100191,China Key Laboratory of Molecular Cardiovascular Sciences,Ministry of Education,Beijing 100191,China
医药卫生
Emergency departmentAcute aortic syndromeMortalityPredictive modelMachine learningAlgorithms
《World Journal of Emergency Medicine》 2026 (1)
P.43-49,7
supported by the special fund of the National Clinical Key Specialty Construction Program[(2022)301-2305].
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