ArticleThe American journal of emergency medicine2019
Comparison of clinical risk scores for triaging high-risk chest pain patients at the emergency department.
Article in The American journal of emergency medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Pooled it
- Pooled it
- Selective classification with machine learning uncertainty estimates improves ACS prediction: a retrospective study in the prehospital setting.Scientific reports · 2026Article
- Improving chest pain risk assessment: validation of HEART, TIMI, GRACE, EDACS-ADP, and HET for MACE prediction in the emergency department.BMC emergency medicine · 2025Article
- Multiple biomarkers risk score for accurately predicting the long-term prognosis of patients with acute coronary syndrome.Journal of geriatric cardiology : JGC · 2025Article
- Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome.European heart journal · 2025Observational
- Combined point-of-care biomarkers for risk stratification in patients with non-ST-elevation acute coronary syndrome in the emergency department.Frontiers in cardiovascular medicine · 2025Article
- Cardiac Troponin Levels in Patients with Chronic Kidney Disease: "Markers of High Risk or Just Noise''?Diagnostics (Basel, Switzerland) · 2024Review
- Chest Pain Risk Stratification in the Emergency Department: Current Perspectives.Open access emergency medicine : OAEM · 2024Review
- ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain.PloS one · 2024Article
- 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
- HEART versus GRACE Score in Predicting the Outcomes of Patients with Acute Coronary Syndrome; a Systematic Review and Meta-Analysis.Archives of academic emergency medicine · 2023Review
- Improving the performance of a triage scale for chest pain patients admitted to emergency departments: combining cardiovascular risk factors and electrocardiogram.BMC emergency medicine · 2022Observational
- A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department.BMC emergency medicine · 2021Article
- Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.Nature communications · 2020Article
- Exploring the complex interactions of baseline patient factors to improve nursing triage of acute coronary syndrome.Research in nursing & health · 2020Article
- Unsupervised machine learning identifies symptoms of indigestion as a predictor of acute decompensation and adverse cardiac events in patients with heart failure presenting to the emergency department.Heart & lung : the journal of critical careArticle
- Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities.Journal of electrocardiologyArticle
- The Association Between Patient Outcomes and the Initial Emergency Severity Index Triage Score in Patients With Suspected Acute Coronary Syndrome.The Journal of cardiovascular nursingArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundMany of the clinical risk scores routinely used for chest pain assessment have not been validated in patients at high risk for acute coronary syndrome (ACS). We performed an independent comparison of HEART, TIMI, GRACE, FRISC, and PURSUIT scores for identifying chest pain due to ACS and for predicting 30-day death or re-infarction in patients arriving through Emergency Medical Services (EMS). METHODS AND
resultsWe enrolled consecutive EMS patients evaluated for chest pain at three emergency departments. A reviewer blinded to outcome data retrospectively reviewed patient charts to compute each risk score. The primary outcome was ACS diagnosed during the primary admission, and the secondary outcome was death or re-infarction within 30-days of initial presentation. Our sample included 750 patients (aged 59 ± 17 years, 42% female), of whom 115 (15.3%) had ACS and 33 (4.4%) had 30-day death or re-infarction. The c-statistics of HEART, TIMI, GRACE, FRISC, and PURSUIT for identifying ACS were 0.87, 0.86, 0.73, 0.84, and 0.79, respectively, and for predicting 30-day death or re-infarction were 0.70, 0.73, 0.72, 0.72, and 0.62, respectively. Sensitivity/negative predictive value of HEART ≥ 4 and TIMI ≥ 3 for ACS detection were 0.94/0.98 and 0.87/0.97, respectively.
conclusionsIn chest pain patients admitted through EMS, HEART and TIMI outperform other scores for identifying chest pain due to ACS. Although both have similar negative predictive value, HEART has better sensitivity and lower rate of false negative results, thus it can be used preferentially over TIMI in the initial triage of this population.
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