Key result
Explainable machine learning with 12 risk factors outperforms continuous models predicting in-hospital post-PCI STEMI mortality.
Why the study?
The study was designed to develop an explainable machine learning model for predicting in-hospital mortality in patients with STEMI after PCI.
Can an explainable machine learning model accurately predict in-hospital mortality in patients with STEMI after PCI?
Observational (n=4,681)
Can an explainable machine learning model accurately predict in-hospital mortality in patients with STEMI after PCI?
An explainable machine learning model using 12 identified risk factors can accurately predict in-hospital mortality in STEMI patients post-PCI, potentially aiding in clinical decision-making.
The aim of this study was to develop an explainable machine learning model for predicting in-hospital mortality (IHF) in patients with ST-elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI). We analyzed data from 4681 electronic medical records of patients with STEMI and identified 12 risk factors for IHF. The predictive models were developed based on multivariate logistic regression, random forest, and stochastic gradient boosting methods. The search for threshold values on the grid while maximizing the area under the ROC-curve and their validation by Shapley’s additive explanation method made it possible to verify the risk factors for IHF. The model, whose parameters were risk factors, was superior in accuracy to the best model with continuous predictors based on stochastic gradient boosting. The use of IHF risk factors as predictors makes it possible to explain the obtained prognosis and reduce the risk of adverse events after PCI.
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Shakhgeldyan et al. (2024) conducted an observational in ST-elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI) (n=4,681). Machine learning model using 12 risk factors vs. Model with continuous predictors was evaluated on In-hospital mortality (IHF). An explainable machine learning model using 12 risk factors was superior in accuracy to the best model with continuous predictors for predicting in-hospital mortality in STEMI patients after PCI.
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