ArticleBMC cardiovascular disorders2025
Machine-learning based calculator for personalized risk assessment following ST-elevation myocardial infarction.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Machine Learning-Based Prediction of Early Left Ventricular Function After STEMI.Journal of clinical medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite 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.
methods3340 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.
results2887 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.
conclusionsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.