ArticleDiagnostics (Basel, Switzerland)2022
Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06464042 (Influence of Air Quality on the Development and Progression of Premature Coronary Artery Disease), which is not on this map. Cited by 9 papers.
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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.
Influence of Air Quality on the Development and Progression of Premature Coronary Artery Disease
Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.Journal of cardiovascular imaging · 2026Review
- Explainable artificial intelligence models in predicting major cardiovascular events: insights from the PolyIran and PolyPars prospective studies.Scientific reports · 2026Article
- Applied machine learning to predict 1-year major adverse cardiovascular events in elderly patients after percutaneous coronary intervention.BMC medical informatics and decision making · 2025Article
- Machine Learning in Predicting Cardiac Events for ESRD Patients: A Framework for Clinical Decision Support.Diagnostics (Basel, Switzerland) · 2025Article
- A Machine Learning Model Integrating Tongue Image Features and Myocardial Injury Markers Predicts Major Adverse Cardiovascular Events in Patients with Coronary Heart Disease.International journal of general medicine · 2025Article
- Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.Scientific reports · 2024Article
- Article
- Clinical Features and Long-Term Outcomes in Very Young Patients with Myocardial Infarction with Non-Obstructive Coronary Arteries.Journal of interventional cardiology · 2022Article
- Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression (LR). Our most effective model for long-term follow-up (60 ± 27 months) was random forest (RF), obtaining an area under the curve (AUC) = 0.79 (95%CI 0.69-0.88), in contrast with LR, obtaining AUC = 0.66 (95%CI 0.53-0.78,
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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.