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Article Abstract
Background: Despite recent therapeutic advancements, a high-risk scenario for subsequent adverse events remains following ST-elevation myocardial infarction (STEMI). Risk scores provide important prognostication data following STEMI. However, the most established risk scores are not contemporary, their prognostic accuracy ranges across populations, and they cannot provide patient-centered recommendations. We aimed to develop a machine learning (ML)-based calculator which provides personalized prognostication following STEMI.
Methods: 3340 patients with STEMI from tertiary care centers in Israel and Italy between the years 2004–2020 were included. The calculator leveraged CatBoost for supervised ML. The goal was to predict rates of one-year mortality and to reduce risk by generating patient-centered recommendations.
Results: 2887 patients were included for calculator training and 453 for testing. Female sex rate (19.3% in the training set vs. 17.8% in the testing set), BMI (29.7±11.3 vs. 28.5±10.0 kg/m), and baseline creatinine (1.1±2.2 vs. 1.1±0.98 mg/dL) were similar between the groups. Age (61.4±12.7 vs. 59.8±12.4 years, = 0.03) was different. The calculator accurately predicted one-year mortality (95.6% accuracy, AUC 0.95, 95% CI: 0.91–0.98 in the Israeli center and 93.2% accuracy, AUC 0.90, 95% CI: 0.79–0.98 in the Italian center). In total, left ventricular ejection fraction post-STEMI demonstrated the strongest contribution towards predicted outcome (mean Shapley value of 0.978). The algorithm also generated a personalized assessment tool of risk for each patient.
Conclusions: The ML-derived STEMI calculator provides the variables of highest predictive value in determining outcomes in a personalized manner. This can be leveraged for prognostication and guidance of patients post-STEMI.
Supplementary Information: The online version contains supplementary material available at 10.1186/s12872-025-04896-1.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12392661 | PMC |
| http://dx.doi.org/10.1186/s12872-025-04896-1 | DOI Listing |
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