Analytic and Clinical Utility of a Next-Generation, Highly Sensitive Cardiac Troponin I Assay for Early Detection of Myocardial Injury
Bibliographic record
Abstract
BACKGROUND: Improvements in cardiac troponin (cTn) assays have increased the rapidity with which clinicians can identify patients with changing cTn concentrations (rise or fall) indicative of acute myocardial injury. The aim of the present study was to characterize a new, high-sensitivity cTnI (hs-cTnI) assay and examine whether increased sensitivity can result in still earlier detection of evolving injury. METHODS: We determined the limit of detection, precision profiles, and preliminary estimates of the 99th percentile for the Beckman Coulter hs-cTnI assay in 125 healthy individuals (age <55 years, 54% male). We compared AccuTnI and hs-cTnI to assess whether change criteria for early concentration changes (i.e., > or =3SD for low concentrations and 20% difference for concentrations >0.10 microg/L) were exceeded in the first 2 specimens (median time between specimens, 1 h; 25th-75th percentile, 1-3 h) from subjects with symptoms suggestive of cardiac ischemia (n = 290). RESULTS: The limit of detection for the hs-cTnI assay was 2.06 ng/L, and the 20% CV and 10% CV concentrations were 2.95 and 8.66 ng/L, respectively. The preliminary 99th percentile estimates in lithium heparin, serum, and EDTA plasma were 9.20, 8.00, and 8.60 ng/L, respectively. In 108 patients with myocardial injury based on the peak AccuTnI concentration, applying the change criteria on the 2 earliest specimens identified 81% (95% CI 73%-88%) of patients using the hs-cTnI assay compared to 62% (53%-71%) using the AccuTnI assay (P < 0.001). CONCLUSIONS: Although more extensive validation studies are required, this Beckman Coulter hs-cTnI assay appears to detect patients with evolving myocardial injury earlier.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".