Key result
Machine learning models show high prognostic accuracy after revascularization, frequently outperforming traditional risk scores.
Why the study?
Machine learning methods are increasingly used to stratify cardiovascular risk, prompting an analysis of models predicting immediate and long-term adverse events after myocardial revascularization.
Do machine learning models accurately predict immediate and long-term outcomes of myocardial revascularization?
Systematic Review
Do machine learning models accurately predict immediate and long-term outcomes of myocardial revascularization?
Machine learning models show promise in predicting adverse events after myocardial revascularization, potentially improving clinical decision support systems.
Answers are grounded in this paper and cite their sources.
Machine learning may enhance post-revascularization risk stratification; extends traditional scores with higher accuracy in systematic evidence.
Machine learning (ML) is among the main tools of artificial intelligence and are increasingly used in population and clinical cardiology to stratify cardiovascular risk. The systematic review presents an analysis of literature on using various ML methods (artificial neural networks, random forest, stochastic gradient boosting, support vector machines, etc.) to develop predictive models determining the immediate and long-term risk of adverse events after coronary artery bypass grafting and percutaneous coronary intervention. Most of the research on this issue is focused on creation of novel forecast models with a higher predictive value. It is emphasized that the improvement of modeling technologies and the development of clinical decision support systems is one of the most promising areas of digitalizing healthcare that are in demand in everyday professional activities.
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Geltser et al. (2021) conducted a systematic review in Coronary artery disease (patients undergoing CABG or PCI). Machine learning predictive models vs. Traditional clinical risk scores (e.g., EuroSCORE II, STS, GRACE) was evaluated on Prognostic accuracy (AUC) for predicting adverse events and mortality. Machine learning models demonstrated high prognostic accuracy for predicting short- and long-term adverse events after myocardial revascularization, frequently outperforming traditional clinical risk scores.
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