Observational studyJournal of the American Heart Association2021
In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.
Observational study in Journal of the American Heart Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04237688 (Electrocardiographic Methods for the Prehospital Identification of Non-ST Elevation Myocardial Infarction Events), which is not on this map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
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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.
Electrocardiographic Methods for the Prehospital Identification of Non-ST Elevation Myocardial Infarction Events (EMPIRE)
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Artificial intelligence in cardiovascular diagnostics: a systematic review and descriptive analysis of clinical applications and diagnostic performance.BMC cardiovascular disorders · 2025Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Diagnostic performance of the ECG-SMART AI score for detecting angiographically significant coronary artery disease in patients undergoing elective coronary angiography.PLOS digital health · 2026Article
- Novel machine learning fusion architectures integrating electrocardiogram representations: applications to acute coronary event detection.European heart journal. Digital health · 2026Article
- Adversarial Debiasing for Equitable and Fair Detection of Acute Coronary Syndrome Using 12-Lead ECG.IEEE transactions on bio-medical engineering · 2026Article
- Review
- Multi-modal deep-learning troponin prediction from electrocardiograms and demographic data.European heart journal. Digital health · 2026Article
- Artificial intelligence-driven electrocardiogram analysis for risk stratification in pulmonary embolism.European heart journal. Digital health · 2025Article
- Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome.European heart journal · 2025Observational
- Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety.Archives of public health = Archives belges de sante publique · 2024Article
- Prediction of emergency department presentations for acute coronary syndrome using a machine learning approach.Scientific reports · 2024Article
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
- Medical-informed machine learning: integrating prior knowledge into medical decision systems.BMC medical informatics and decision making · 2024Review
- Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.Nature medicine · 2023Observational
- Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact.Research square · 2023Article
- Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- A clinician's guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML).European heart journal. Digital health · 2022Article
- The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.Cardiology in the young · 2021Article
- In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.Journal of the American Heart Association · 2021Observational
- Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities.Journal of electrocardiologyArticle
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 at 2 institutions in 1 country.
Funding
Abstract
Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.
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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.