Ke J, Chen Y, Wang X, Wu Z, Zhang Q, Lian Y, Chen F. Machine learning-based in-hospital mortality prediction models for patients with acute coronary syndrome.
Am J Emerg Med 2022;
53:127-134. [PMID:
35033770 DOI:
10.1016/j.ajem.2021.12.070]
[Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 12/13/2021] [Accepted: 12/31/2021] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES
The purpose of this study is to identify the risk factors of in-hospital mortality in patients with acute coronary syndrome (ACS) and to evaluate the performance of traditional regression and machine learning prediction models.
METHODS
The data of ACS patients who entered the emergency department of Fujian Provincial Hospital from January 1, 2017 to March 31, 2020 for chest pain were retrospectively collected. The study used univariate and multivariate logistic regression analysis to identify risk factors for in-hospital mortality of ACS patients. The traditional regression and machine learning algorithms were used to develop predictive models, and the sensitivity, specificity, and receiver operating characteristic curve were used to evaluate the performance of each model.
RESULTS
A total of 6482 ACS patients were included in the study, and the in-hospital mortality rate was 1.88%. Multivariate logistic regression analysis found that age, NSTEMI, Killip III, Killip IV, and levels of D-dimer, cardiac troponin I, CK, N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-density lipoprotein (HDL) cholesterol, and Stains were independent predictors of in-hospital mortality. The study found that the area under the receiver operating characteristic curve of the models developed by logistic regression, gradient boosting decision tree (GBDT), random forest, and support vector machine (SVM) for predicting the risk of in-hospital mortality were 0.884, 0.918, 0.913, and 0.896, respectively. Feature importance evaluation found that NT-proBNP, D-dimer, and Killip were top three variables that contribute the most to the prediction performance of the GBDT model and random forest model.
CONCLUSIONS
The predictive model developed using logistic regression, GBDT, random forest, and SVM algorithms can be used to predict the risk of in-hospital death of ACS patients. Based on our findings, we recommend that clinicians focus on monitoring the changes of NT-proBNP, D-dimer, Killip, cTnI, and LDH as this may improve the clinical outcomes of ACS patients.
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