Machine-learning based calculator for personalized risk assessment following ST-elevation myocardial infarction
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Le résumé fourni par la source
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/m2), 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, p = 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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Machine-learning based calculator for personalized risk assessment following ST-elevation myocardial infarction
- Date Crossref
- 28/08/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Afeka College of Engineering pays non établi dans la noticeUniversité ou école supérieure
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Albert Einstein College of Medicine Department of Internal Medicine pays non établi dans la noticeUniversité ou école supérieure
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Tel Aviv University pays non établi dans la noticeUniversité ou école supérieure
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Rabin Medical Center Cardiology Department pays non établi dans la noticeÉtablissement de santé
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Reichman University pays non établi dans la noticeUniversité ou école supérieure
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Herzliya Medical Center pays non établi dans la noticeÉtablissement de santé
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University of Turin Città della Salute e della Scienza Hospital pays non établi dans la noticeUniversité ou école supérieure
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Beilinson Medical Center Innovation Artificial Intelligence Center pays non établi dans la noticeÉtablissement de santé
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The Faculty of Medicine pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Computer Science pays non établi dans la noticeUniversité ou école supérieure
Afeka College of Engineering, Department of Internal Medicine — Albert Einstein College of Medicine et Tel Aviv University, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.