Comparative Evaluation of 2-Hour Rapid Diagnostic Algorithms for Acute Myocardial Infarction Using High-Sensitivity Cardiac Troponin T
Bibliographic record
Abstract
BACKGROUND: Symptoms of acute coronary syndrome account for a large proportion of emergency department (ED) visits and hospitalizations. High-sensitivity troponin can rapidly rule out or rule in acute myocardial infarction (AMI) within a short time of ED arrival. We sought to validate test characteristics and classification performance of 2-hour high-sensitivity troponin T (hsTnT) algorithms for the rapid diagnosis of AMI. METHODS: We included consecutive patients from 4 academic EDs with suspected cardiac chest pain who had hsTnT assays performed 2 hours apart (± 30 minutes) as part of routine care. The primary outcome was AMI at 7 days. Secondary outcomes included major adverse cardiac events (mortality, AMI, and revascularization). Test characteristics and classification performance for multiple 2-hour algorithms were quantified. RESULTS: Seven hundred twenty-two patients met inclusion criteria. Seven-day AMI incidence was 10.9% and major adverse cardiac event incidence was 13.7%. A 2-hour rule-out algorithm proposed by Reichlin and colleagues ruled out AMI in 59.4% of patients with 98.7% sensitivity and 99.8% negative predictive value (NPV). The 2-hour rule-out algorithm proposed by the United Kingdom National Institute for Health and Care Excellence ruled out AMI in 50.3% of patients with similar sensitivity and NPV. Other exploratory algorithms had similar sensitivity but marginally better classification performance. According to Reichlin et al., the 2-hour rule-in algorithm ruled in AMI in 16.5% of patients with 92.4% specificity and 58.5% positive predictive value. CONCLUSIONS: Two-hour hsTnT algorithms can rule out AMI with very high sensitivity and NPV. The algorithm developed by Reichlin et al. had superior classification performance. Reichlin and colleagues' 2-hour rule-in algorithm had poor positive predictive value and might not be suitable for early rule-in decision-making.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".