A Common Algorithm for Cardiac Troponin to Rule Out and Rule in Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: A limitation of diagnostic algorithms in patients with suspected myocardial infarction (MI) is the requirement for assay-specific high-sensitivity cardiac troponin (hs-cTn) cutoff concentrations and change criteria. In this study we evaluated a common change criteria algorithm (3C) for hs-cTn and compared it with established algorithms for the rule out and rule in of MI. METHODS: We applied the 3C algorithm in 2 prospective cohort studies (with 3 different hs-cTn assays) of patients who presented to the emergency department with suspected MI who had serial hs-cTn results available. Diagnostic performance measures (sensitivity, specificity, predictive values, likelihood ratios) for MI were obtained for the 3C (change criteria > |3| ng/L for < 10 ng/L, > |30|% between 10 and 100 ng/L, and > |15|% for > 100 ng/L), and the European Society of Cardiology (ESC) algorithms for rule in and rule out. Confusion matrices, net reclassification improvement, and effectiveness (percentage rule in and rule out) analyses were also performed. RESULTS: In 5011 patients, the MI prevalence was 16.12% (n = 811). Comparable diagnostic accuracy in terms of sensitivity, specificity, and predictive values were observed between the 3C and ESC algorithms. Direct comparison of the algorithms via net reclassification improvement showed no decisive advantage for either algorithm. Confusion matrices for all 3 assays for the 0- and/or 1-hour and 0- and/or 2-hour sampling identified that the 3C ruled in more patients with an MI who were ruled out using the ESC algorithm. Effectiveness was higher for 3C (83.2%-88.8%) vs ESC (64.4%-74.5%) for hs-cTnI but not for hs-cTnT (64.5%-71.8% vs 72.4%-80.6%, respectively). CONCLUSIONS: The 3C algorithm offers a uniform, assay agnostic alternative to established algorithms, independent of timing of serial sampling. CLINICAL TRIAL REGISTRATIONS: NCT02355457, ACTR,12611001069943, ANZCTR12610000766011, and ANZCTR12613000745741.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".