Development and validation of machine learning-based in-hospital mortality predictive models for acute aortic syndrome in emergency departments

doi:10.5847/wjem.j.1920-8642.2026.022

Abstract

Abstract:

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.

Key words: Emergency department, Acute aortic syndrome, Mortality, Predictive model, Machine learning, Algorithms

Yuanwei Fu, Yilan Yang, Hua Zhang, Daidai Wang, Qiangrong Zhai, Lanfang Du, Nijiati Muyesai, Yanxia Gao, Qingbian Ma. Development and validation of machine learning-based in-hospital mortality predictive models for acute aortic syndrome in emergency departments[J]. World Journal of Emergency Medicine, 2026, 17(1): 43-49.

Figures/Tables 4

Figure 1.

Table 1.

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Variables	Internal validation				External validation
Variables	LR	MLP	GNB	RF	LR	MLP	GNB	RF	EM
AUC	0.90	0.85	0.87	0.87	0.85	0.76	0.73	0.76	0.67
Classification threshold	0.092	0.059	0.115	0.119	0.026	0.022	0.055	0.056
Sensitivity	0.91	0.91	0.82	0.82	0.95	0.80	0.75	0.85	0.90
Specificity	0.78	0.72	0.77	0.85	0.62	0.66	0.73	0.62	0.40
PPV	0.20	0.20	0.18	0.26	0.21	0.20	0.22	0.19	0.14
NPV	0.99	0.99	0.99	0.99	0.99	0.97	0.97	0.98	0.97
F1	0.33	0.29	0.30	0.39	0.34	0.32	0.34	0.31	0.24
Accuracy	0.78	0.73	0.77	0.85	0.65	0.67	0.73	0.64
Youden Index	0.69	0.63	0.59	0.67	0.57	0.46	0.48	0.47

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