ArticleScientific reports2017
Machine Learning Improves Risk Stratification After Acute Coronary Syndrome.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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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.
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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.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis.European journal of medical research · 2023Pooled it
- Pooled it
- Influence of the Residual Gensini Score on Prognosis of Patients With ST-Elevation Myocardial Infarction.Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions · 2026Observational
- Machine learning to predict long-term cardiovascular death following myocardial infarction: incremental value of echocardiographic data.European heart journal. Digital health · 2026Article
- Personalized Treatment of Patients with Coronary Artery Disease: The Value and Limitations of Predictive Models.Journal of cardiovascular development and disease · 2025Review
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- QTc interval prolongation impact on in-hospital mortality in acute coronary syndromes patients using artificial intelligence and machine learning.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2024Article
- Article
- Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait.Scientific reports · 2024Article
- Article
- ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain.PloS one · 2024Article
- Use of Artificial Intelligence in the Advancement of Breast Surgery and Implications for Breast Reconstruction: A Narrative Review.Journal of clinical medicine · 2023Review
- A bibliometric analysis of acute myocardial infarction in women from 2000 to 2022.Frontiers in cardiovascular medicine · 2023Article
- Risk Stratification of COVID-19 Using Routine Laboratory Tests: A Machine Learning Approach.Infectious disease reports · 2022Article
- Reply: More Than Meets the AI: Electrocardiograms in Heart Failure Prognosis.JACC. Advances · 2022Article
- A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Robust prognostic prediction model developed with integrated biological markers for acute myocardial infarction.PloS one · 2022Article
- Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease.Frontiers in cardiovascular medicine · 2022Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate assessment of a patient's risk of adverse events remains a mainstay of clinical care. Commonly used risk metrics have been based on logistic regression models that incorporate aspects of the medical history, presenting signs and symptoms, and lab values. More sophisticated methods, such as Artificial Neural Networks (ANN), form an attractive platform to build risk metrics because they can easily incorporate disparate pieces of data, yielding classifiers with improved performance. Using two cohorts consisting of patients admitted with a non-ST-segment elevation acute coronary syndrome, we constructed an ANN that identifies patients at high risk of cardiovascular death (CVD). The ANN was trained and tested using patient subsets derived from a cohort containing 4395 patients (Area Under the Curve (AUC) 0.743) and validated on an independent holdout set containing 861 patients (AUC 0.767). The ANN 1-year Hazard Ratio for CVD was 3.72 (95% confidence interval 1.04-14.3) after adjusting for the TIMI Risk Score, left ventricular ejection fraction, and B-type natriuretic peptide. A unique feature of our approach is that it captures small changes in the ST segment over time that cannot be detected by visual inspection. These findings highlight the important role that ANNs can play in risk stratification.
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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.