ArticleClinical research in cardiology : official journal of the German Cardiac Society2023
Personalized diagnosis in suspected myocardial infarction.
Article in Clinical research in cardiology : official journal of the German Cardiac Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 9 registered trials, which are not on this map. Cited by 18 papers.
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.
High-Sensitivity Troponin in the Evaluation of Patients With Acute Coronary Syndrome: A Randomized Controlled Trial
Determining the Optimum Treatment Cutoffs for Cardiac Troponin Assays in Patients Presenting to the Emergency Department With Suspected Cardiac Ischemia
Use of Abbott High Sensitivity Troponin I Assay In Acute Coronary Syndromes
Biomarkers in Acute Cardiac Care
High-Sensitivity Cardiac Troponin T to OPtimize Chest Pain Risk Stratification
Observational Study on the Safety of Accelerated Rule-out Protocols in Patients Admitted With Chest Pain to a Crowded CPU
Study for Evaluation of Newly Onset Chest Pain and Rapid Diagnosis of Myocardial Necrosis (stenoCARDIA)
Evaluation of the Siemens POC HS CTn-I Test System
Safety and Efficacy of Hs-cTnT Protocols
Who cites it
18 citing papers in PubMed, 31 citations in OpenAlex.
- 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
- Diagnostic validation of a novel high-sensitivity cardiac troponin T assay.European heart journal. Acute cardiovascular care · 2026Article
- Machine learning for diagnosing non-ST-segment elevation myocardial infarction: a derivation and validation study.EClinicalMedicine · 2026Article
- 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
- Age Differences in Acute Chest Pain Care in a Multisite US Cohort.Clinical cardiology · 2026Observational
- Review
- Considerations for the Adoption of Digital Algorithms and Cardiovascular Decision-Support Tools in Clinical Practice.Critical pathways in cardiology · 2026Review
- Optimizing Acute Chest Pain Management: Navigating Diagnostic Challenges and Advancing Precision Care.Reviews in cardiovascular medicine · 2025Article
- High-Sensitivity Troponins and Homocysteine: Combined Biomarkers for Better Prediction of Cardiovascular Events.International journal of molecular sciences · 2025Review
- Applications, challenges and future directions of artificial intelligence in cardio-oncology.European journal of clinical investigation · 2025Review
- The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Analytical and Clinical Performance of High-Sensitivity Cardiac Troponin Point-of-Care Assays as an Aid in the Diagnosis of Myocardial Infarction: A Narrative Review.Emergency medicine international · 2025Review
- Machine-Learning for Phenotyping and Prognostication of Myocardial Infarction and Injury in Suspected Acute Coronary Syndrome.JACC. Advances · 2024Article
- Machines Running for Phenotyping of Myocardial Injury: The End to anJACC. Advances · 2024Article
- How to Approach Patients with Acute Chest Pain.Reviews in cardiovascular medicine · 2024Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
38 authors at 19 institutions in 9 countries.
Funding
Abstract
backgroundIn suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays.
methodsIn 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients.
resultsEleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy.
conclusionWe developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care. TRIAL REGISTRATION NUMBERS: Data of following cohorts were used for this project: BACC ( www. CLINICALTRIALS: gov ; NCT02355457), stenoCardia ( www. CLINICALTRIALS: gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www. CLINICALTRIALS: gov ; NCT01852123), LUND ( www. CLINICALTRIALS: gov ; NCT05484544), RAPID-CPU ( www. CLINICALTRIALS: gov ; NCT03111862), ROMI ( www. CLINICALTRIALS: gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www. CLINICALTRIALS: gov ; NCT04772157), STOP-CP ( www. CLINICALTRIALS: gov ; NCT02984436), UTROPIA ( www. CLINICALTRIALS: gov ; NCT02060760).
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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.