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Appl Clin Inform 2022; 13(03): 720-740
DOI: 10.1055/a-1863-1589
Review Article

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches

Sara Chopannejad
1   Student Research Committee, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
,
Farahnaz Sadoughi
2   School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
,
Rafat Bagherzadeh
3   English Language Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
,
Sakineh Shekarchi
2   School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
› Author Affiliations Funding None.
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Abstract

Background Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions.

Objective The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome.

Methods To predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. In doing so, PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The checklist “Quality assessment of machine learning studies” was used to assess the quality of eligible studies. The findings of the studies are presented in the form of a narrative synthesis of evidence.

Results In total, among 2,558 retrieved articles, 22 studies were qualified for analysis. Major adverse cardiovascular events and mortality were predicted in 5 and 17 studies, respectively. According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N = 20) achieved a high area under the ROC curve between 0.8 and 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice.

Conclusion Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.

Keywords

machine learning - mortality - acute coronary syndrome - myocardial infarction - prediction - major adverse cardiac event

Author Contributions

All authors made significant contributions to the manuscript. S.C. developed the design of the scoping review and was involved in the data screening and extraction with S.S.. S.C. conducted the medical evaluation of the included studies, and wrote the manuscript. F.S. and R.B. were involved in the medical assessment of the included studies. F.S. supervised and guided the project. S.S. and S.C. categorized the biomarkers and variables that extracted from findings. All authors provided critical revision and approved the manuscript.


Protection of Human and Animal Subjects

The current study was approved by the Human Research Ethics Committee (ethics code IR.IUMS.REC.1398.948), Iran University of Medical Sciences.


Supplementary Material



Publication History

Received: 06 November 2021

Accepted: 24 May 2022

Accepted Manuscript online:
26 May 2022

Article published online:
27 July 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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