Diagnostic performance of a common algorithm for cardiac troponin in acute myocardial infarction
Bibliographic record
Abstract
Abstract Background A major limitation of diagnostic algorithms for suspected myocardial infarction (MI) is their reliance on assay-specific high-sensitivity cardiac troponin (hs-cTn) cut-off concentrations and change criteria. Recently, the common change criteria (3C) have been proposed as an approach to risk stratification which utilizes universal, assay-independent criteria for hs-cTn assays Purpose We sought to evaluate a 3C algorithm for hs-cTn and compare its performance to established algorithms for the rule-out and rule-in of MI. Methods We used data from two prospective cohort studies (with 3 different hs-cTn assays) of patients presenting to the emergency department with suspected MI who had serial hs-cTn results available: the Biomarkers in Acute Cardiac Care (BACC) cohort from Germany and the Christchurch cohort from New Zealand. The 3C algorithm was applied and compared to established ESC algorithms (Figure 1). Diagnostic performance measures (sensitivity, specificity, predictive values, likelihood ratios) for MI were obtained for both 3C (change criteria >|3| for under 10 ng/L, >|30|% between 10-100 ng/L and >|15|% for above 100 ng/L) and the ESC algorithms for rule-in and rule-out. Confusion matrices, net reclassification improvement (NRI), 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 NRI showed no decisive advantage for either algorithm (Figure 2). Confusion matrices for all three assays for both 0/1h and 0/2h sampling identified that the 3C ruled-in more patients with a MI who were ruled-out with the ESC. Conclusions The 3C algorithm performs similarly to the ESC 0/1h and 0/2h algorithms for risk stratifying patients for possible MI across three different hs-cTn assays. The 3C algorithm has the advantage of being assay-agnostic and has demonstrated comparable performance in the settings of both the 0/1h and 0/2h sampling.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".