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Machine Learning to Predict the 1-Year Mortality Rate After Acute Anterior Myocardial Infarction in Chinese Patients

Authors Li Y, Jiang L, He J, Jia K, Peng Y, Chen M

Received 29 October 2019

Accepted for publication 20 December 2019

Published 9 January 2020 Volume 2020:16 Pages 1—6

DOI https://doi.org/10.2147/TCRM.S236498

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Deyun Wang


Yi-ming Li, 1,* Li-cheng Jiang, 2,* Jing-jing He, 1 Kai-yu Jia, 1 Yong Peng, 1 Mao Chen 1

1Department of Cardiology, West China Hospital, Sichuan University, Chengdu, People’s Republic of China; 2Department of Cardiology, The First Affiliated Hospital, Chengdu Medical College, Chengdu, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yong Peng; Mao Chen
Department of Cardiology, West China Hospital, Sichuan University, 37 Guoxue Street, Chengdu 610041, People’s Republic of China
Email pengyongcd@126.com; hmaochen@vip.sina.com

Abstract: A formal risk assessment for identifying high-risk patients is essential in clinical practice and promoted in guidelines for the management of anterior acute myocardial infarction. In this study, we sought to evaluate the performance of different machine learning models in predicting the 1-year mortality rate of anterior ST-segment elevation myocardial infarction (STEMI) patients and to compare the utility of these models to the conventional Global Registry of Acute Coronary Events (GRACE) risk scores. We enrolled all of the patients aged > 18 years with discharge diagnoses of anterior STEMI in the Western China Hospital, Sichuan University, from January 2011 to January 2017. A total of 1244 patients were included in this study. The mean patient age was 63.8± 12.9 years, and the proportion of males was 78.4%. The majority (75.18%) received revascularization therapy. In the prediction of the 1-year mortality rate, the areas under the curve (AUCs) of the receiver operating characteristic curves (ROCs) of the six models ranged from 0.709 to 0.942. Among all models, XGBoost achieved the highest accuracy (92%), specificity (99%) and f1 score (0.72) for predictions with the full variable model. After feature selection, XGBoost still obtained the highest accuracy (93%), specificity (99%) and f1 score (0.73). In conclusion, machine learning algorithms can accurately predict the rate of death after a 1-year follow-up of anterior STEMI, especially the XGBoost model.

Keywords: machine learning, prediction model, acute anterior myocardial infarction

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