ArticleNature medicine2023
Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.
Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
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Who cites it
71 citing papers in PubMed.
- Type 1 and type 2 NSTEMI in atrial fibrillation: insights from the HERA-FIB registry.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Observational
- Continuous Molecular Monitoring Using Electrochemical Aptamer-Based Sensors: Remaining Challenges for Long-Term In Vivo Deployment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Multi-domain identification of myocardial infarction incidence using explainable AI: the overlooked role of periodontal health.Scientific reports · 2026Article
- Comparative study on the predictive value of PNI and GNRI for all-cause mortality rates of elderly patients in cardiac care unit and cardiovascular intensive care unit.European journal of clinical nutrition · 2026Observational
- Machine learning for diagnosing non-ST-segment elevation myocardial infarction: a derivation and validation study.EClinicalMedicine · 2026Article
- From Electrocardiography to the Catheterization Laboratory: A Multimodal Artificial Intelligence Framework for Acute Coronary Syndrome Detection and Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Review
- High-sensitivity cardiac troponin assays at the point of care.Clinical chemistry and laboratory medicine · 2026Article
- Development of a guideline-based clinical decision support system for emergency department assessment of suspected non-ST-elevation acute coronary syndromes.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Review
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Age Differences in Acute Chest Pain Care in a Multisite US Cohort.Clinical cardiology · 2026Observational
- The role of artificial intelligence in early detection and risk prediction of ischemic heart disease.Annals of medicine and surgery (2012) · 2026Review
- Improving classification of myocardial infarction with machine learning in a diverse population.American journal of epidemiology · 2026Article
- Review
- Considerations for the Adoption of Digital Algorithms and Cardiovascular Decision-Support Tools in Clinical Practice.Critical pathways in cardiology · 2026Review
- Antibody-Empowered Nanomedicine for Precise Biomedical Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- A machine Learning-Based risk stratification using CMR volumetric and strain parameters in patients with hypertrophic cardiomyopathy.The international journal of cardiovascular imaging · 2026Article
- Organ cross-talk: molecular mechanisms, biological functions, and therapeutic interventions for diseases.Signal transduction and targeted therapy · 2026Review
- An easy-to-use model for screening type 2 myocardial infarction in geriatric patients with acute coronary syndrome: a retrospective cohort study.Frontiers in medicine · 2026Article
11 more citing papers are in PubMed but not listed here.
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24 authors.
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Abstract
Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features and compute the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) score (0-100) that corresponds to an individual's probability of myocardial infarction. The models were trained on data from 10,038 patients (48% women), and their performance was externally validated using data from 10,286 patients (35% women) from seven cohorts. CoDE-ACS had excellent discrimination for myocardial infarction (area under curve, 0.953; 95% confidence interval, 0.947-0.958), performed well across subgroups and identified more patients at presentation as low probability of having myocardial infarction than fixed cardiac troponin thresholds (61 versus 27%) with a similar negative predictive value and fewer as high probability of having myocardial infarction (10 versus 16%) with a greater positive predictive value. Patients identified as having a low probability of myocardial infarction had a lower rate of cardiac death than those with intermediate or high probability 30 days (0.1 versus 0.5 and 1.8%) and 1 year (0.3 versus 2.8 and 4.2%; P < 0.001 for both) from patient presentation. CoDE-ACS used as a clinical decision support system has the potential to reduce hospital admissions and have major benefits for patients and health care providers.
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