Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning
Sara Chopannejad1, Farahnaz Sadoughi2, Rafat Bagherzadeh3
1Student Research Committee, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Insights
Machine learning effectively predicts major adverse cardiovascular events and mortality in acute coronary syndrome patients. However, these models are not yet used in clinical practice, necessitating further research for integration.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Acute coronary syndrome (ACS) is a leading global cause of death.
- Predicting major adverse cardiovascular events (MACE) and cardiovascular deaths in ACS patients is crucial for timely clinical decisions.
Purpose of the Study:
- To review machine learning algorithms and predictor variables for MACE in ACS patients.
- To highlight the current state of machine learning applications in ACS risk prediction.
Main Methods:
- Scoping review following PRISMA guidelines, searching PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore (2005-2021).
- Quality assessment of machine learning studies.
- Narrative synthesis of evidence from 22 included studies.
Main Results:
- 22 studies analyzed, predicting MACE (5 studies) and mortality (17 studies).
- Most studies (63.64%) lacked external validation; common algorithms included Logistic Regression, Random Forest, and Boosting Ensembles.
- High predictive performance (AUC 0.8-0.99) was achieved, using demographic, clinical, and therapeutic predictors. No study reported clinical integration.
Conclusions:
- Machine learning shows promise for predicting MACE and mortality in ACS.
- Current machine learning models for ACS risk prediction are not integrated into clinical practice.
- Further research is needed to develop practical, effective machine learning tools for improving ACS patient care.
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.
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