Key result
Machine learning predicts NSTE-ACS mortality better than GRACE score, achieving ~0.95 AUC with Random Forest.
Why the study?
Existing risk models may not fully reflect contemporary NSTE-ACS populations, so developing ML-based risk calculators could improve prediction of in-hospital mortality at different diagnostic stages.
Do machine learning-based risk prediction models improve the prediction of in-hospital mortality compared to the GRACE score in patients with NSTE-ACS?
Observational (n=1,144)
No
Do machine learning-based risk prediction models improve the prediction of in-hospital mortality compared to the GRACE score in patients with NSTE-ACS?
Effect estimate: ROC-AUC 0.951 (95% CI 0.946-0.956)
p-value: p=<0.001
Machine learning models incorporating clinical, laboratory, and echocardiographic data at various stages of diagnosis significantly improve the prediction of in-hospital mortality in NSTE-ACS patients compared to the traditional GRACE score.
Should not yet change NSTE-ACS risk stratification; leaves open prospective validation of ML models versus GRACE.
Background Despite advances in understanding and treating non–ST-elevation acute coronary syndrome (NSTE-ACS), patients continue to experience high rates of adverse outcomes, particularly those with non–ST-segment elevation myocardial infarction, which remains a leading cause of cardiovascular mortality. Existing risk models may not fully reflect contemporary patient populations due to substantial changes in clinical profiles. Developing new machine learning (ML)–based risk calculators may improve the prediction of in-hospital mortality (IHM) at different stages of the diagnostic process, and ultimately improve patient outcomes. Objective This study aimed to develop predictive models for IHM in patients with NSTE-ACS using ML methods and predictor sets obtained during the diagnostic process. Methods This retrospective observational study included 1144 patients with NSTE-ACS admitted between 2019 and 2021. IHM occurred in 94 (8.1%) of 1144 patients. Predictive models were developed using multivariable logistic regression, Random Forest, XGBoost (Extreme Gradient Boosting), and CatBoost algorithms. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve, calibration metrics, and decision curve analysis. Results A key feature of the developed models was their applicability at different stages of the diagnostic process, using predictors available at each specific stage. At admission, ML models achieved ROC-AUC values up to 0.93. After incorporating laboratory and echocardiographic data, predictive performance increased to ROC-AUC values of 0.94 to 0.95. The best-performing model demonstrated an ROC-AUC of 0.951 (95% CI 0.946‐0.956), a sensitivity of 0.902 (95% CI 0.889‐0.916), and a specificity of 0.891 (95% CI 0.886‐0.896). The area under the precision-recall curve reached 0.685, and the Brier score was 0.0331, indicating good calibration. Random Forest models demonstrated greater clinical utility than the Global Registry of Acute Coronary Events score ( P <.001). Shapley Additive Explanations analysis identified the most significant predictors of mortality risk, including Killip class of acute heart failure, age, Charlson comorbidity index, creatinine level, and hematocrit level. Conclusions ML-based models enabled accurate prediction of IHM in patients with NSTE-ACS at different stages of the diagnostic process and may improve risk stratification and clinical decision-making in real-world practice.
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Ryabov et al. (2026) conducted an observational in Non-ST-Elevation Acute Coronary Syndrome (NSTE-ACS) (n=1,144). Machine learning-based risk prediction models vs. GRACE risk score was evaluated on In-hospital mortality or death within 30 days of the acute coronary event (ROC-AUC 0.951, 95% CI 0.946-0.956, p=<0.001). Machine learning-based models, particularly Random Forest, accurately predicted in-hospital mortality in NSTE-ACS patients with an ROC-AUC of 0.951, outperforming the GRACE risk score.
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