Validation of presentation and 3 h high-sensitivity troponin to rule-in and rule-out acute myocardial infarction
Bibliographic record
Abstract
OBJECTIVE: International guidelines to rule-in acute myocardial infarction (AMI) in patients presenting with chest pain to the emergency department (ED) recommend an algorithm using high-sensitivity cardiac troponin (hs-cTn) sampling on presentation and 3 h following presentation. We tested the diagnostic accuracy of this algorithm by pooling data from five distinct cohorts from three countries of prospectively recruited patients with independently adjudicated outcomes. METHOD: We measured high-sensitivity cardiac troponin I (hs-cTnI) and high-sensitivity cardiac troponin T (hs-cTnT) on presentation (0 h) and 3 h post-presentation samples in adult patients attending an ED with possible AMI to validate the European Society of Cardiology (ESC) Working Group on Acute Cardiac Care rule-in algorithm (ESC-rule-in). Specifically, (i) in patients with a 0 h hs-cTn concentration ≤99th percentile and a 3 h hs-cTn >99th percentile, positive patients are those with an absolute change in troponin ≥50% of the 99th percentile, and (ii) in patients with a 0 and 3 h hs-cTn >99th percentile, positive patients are those with a relative change in troponin of ≥20%. We concurrently assessed the efficacy of the 0 and 3 h hs-cTn <99th percentile to rule-out AMI. RESULTS: 1061 patients with hs-cTnI and 985 with hs-cTnT were included. The ESC-rule-in positive predictive value (PPV) was 83.5% (95% CI 74.9% to 90.1%) for hs-cTnI and 72.0% (95% CI 62.1% to 80.5%) for hs-cTnT. Forty-six AMIs (34.9%) were not ruled in using hs-cTnI and 62 (46.2%) using hs-cTnT. The sensitivity of the 99th percentile to rule-out AMI was 93.2% (95% CI 87.5% to 96.8%) for hs-cTnI and 94.8% (95% CI 89.5% to 97.9%) for hs-cTnT. CONCLUSIONS: The ESC-rule-in algorithm has good PPV with hs-cTnI and reasonable with hs-cTnT and can rule-in over 50% of AMIs. However, the sensitivity of the 99th percentile to rule-out AMI is too low for clinical use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".