Troponin is more useful than creatine kinase in predicting one-year mortality among acute coronary syndrome patients
Bibliographic record
Abstract
AIMS: To compare the long-term prognostic value of troponins (Tn) vs. conventional cardiac biomarker creatine kinase (CK) and CK-MB across the spectrum of acute coronary syndromes (ACS). METHODS AND RESULTS: In the prospective, observational Canadian ACS Registry, 4627 patients with ACS were enrolled from 51 centres. The CK, CK-MB, Tn samples were analysed in each hospital clinical laboratory and the results related to the reference levels of the individual laboratories. The study cohort comprised 3138 (67.8%) patients who had both CK (or CK-MB) and Tn measurements during the first 24 h of hospitalisation. Vital status at one-year was determined by standardized telephone interview. 61.2% and 59.0% of patients had abnormal Tn and CK (or CK-MB) levels, respectively. Vital status at one-year was ascertained for 2950 patients (6% lost to follow-up). Among patients with normal CK (or CK-MB) levels, elevated Tn was associated with increased one-year mortality (odds ratio [OR] 2.06; 95% CI 1.37-3.11; P=0.001). Similarly, among patients with abnormal CK (or CK-MB) levels, abnormal Tn predicted higher one-year mortality (OR 1.83; 95% CI 1.14-2.93; P=0.01). In contrast, abnormal CK (or CK-MB) was not predictive of mortality after stratification by Tn status. In multivariable analysis controlling for other known prognosticators including creatinine, abnormal Tn (adjusted OR 1.78; 95% CI 1.30-2.44; P<0.001) but not CK/CK-MB was independently associated with increased one-year mortality. CONCLUSIONS: Elevated Tn was independently associated with worse outcome at one-year, while CK or CK-MB status did not provide incremental prognostic information. Our findings support the use of Tn in the risk stratification of unselected ACS patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| 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 | 0.000 |
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".