Early Rule-Out Strategies in the Emergency Department Utilizing High-Sensitivity Cardiac Troponin Assays
Bibliographic record
Abstract
BACKGROUND: Over the past decade, intense collaboration between academic investigators and the diagnostic industry have allowed the integration of high-sensitivity cardiac troponin (hs-cTn) assays into clinical practice worldwide. The hs-cTn assays, with their increased diagnostic accuracy for acute myocardial infarction (AMI), have facilitated the maturation of early rule-out strategies. The first iteration was complex and required the combination of a biomarker panel, the electrocardiogram, and a clinical risk score and allowed the safe rule-out of AMI in only 10% of patients with acute chest pain. In contrast, the latest iterations, including the European Society of Cardiology (ESC) 0/1-h algorithm, are simple. They are based on hs-cTn concentrations only and allow the safe rule-out or rule-in of AMI in up to 75% of patients. CONTENT: The purposes of this minireview are (a) to describe the best validated hs-cTn-based strategies for early rule-out of AMI, (b) to discuss the advantages and limitations of the different strategies, (c) to identify patient subgroups requiring particular attention, (d) to recognize challenges for widespread clinical implementation, and (e) to provide guidance on strategies for their safe and effective clinical implementation. SUMMARY: Physicians and institutions may choose among several well-validated rule-out algorithms. The ESC 0/1-h algorithm for hs-cTnT or hs-cTnI seems to be the most attractive option today. It best balances safety and efficacy, and it has been derived and validated for all currently available hs-cTnT/I assays, facilitating widespread clinical implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".