World journal of emergency medicine · 2026
Development and validation of machine learning-based in-hospital mortality predictive models for acute aortic syndrome in emergency departments.
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Fu Yuanwei, Yang Yilan, Zhang Hua, Wang Daidai, Zhai Qiangrong, Du Lanfang, Muyesai Nijiati, Gao Yanxia, Ma Qingbian
Аннотация
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 simplified 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 simplified model was then derived based on five key variables (Stanford type, pericardial effusion, 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 simplified model incorporating five 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.
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 simplified model was then derived based on five key variables (Stanford type, pericardial effusion, 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 simplified model incorporating five 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.