Short- and Long-Term Risk Stratification Using a Next-Generation, High-Sensitivity Research Cardiac Troponin I (hs-cTnI) Assay in an Emergency Department Chest Pain Population
Bibliographic record
Abstract
BACKGROUND: The next-generation, high-sensitivity cardiac troponin assays can measure quantifiable concentrations of cTn in a majority of individuals, but there are few studies assessing these assays for risk stratification. The present study was undertaken to determine if a research hs-cTnI assay can be useful for predicting death/myocardial infarction (MI), both short- and long-term, in an emergency department acute coronary syndrome (ACS) population. METHODS: In a cohort of 383 subjects, originally recruited in 1996, presenting to the emergency department with symptoms suggestive of ACS, the heparin plasma obtained at initial presentation was thawed and measured in 2007 with a research hs-cTnI assay. AccuTnI (Beckman Coulter) measurements were made on these same samples in 2003. The population was divided into 4 groups by hs-cTnI: <5.00, 5.00-9.99, 10.00-40.00, and >40.00 ng/L. Kaplan-Meier, Cox proportional hazards, ROC curves, and logistic regression analyses were used to identify which hs-cTnI concentrations were predictive of death/MI within 10 years after presentation. RESULTS: There were significant differences between the hs-cTnI groups for the probability of death/MI up to 10 years after presentation (P < 0.05). At 6 months, patients with hs-cTnI > or =10.00 ng/L were at higher risk for death/MI (hazard ratio >3.7; P < 0.05) compared with those having hs-cTnI <5.00 ng/L. ROC curve analysis for death/MI at 30 days with the hs-cTnI assay had an area under the curve of 0.74 (95% CI 0.65-0.82), with logistic models yielding an optimal assay threshold of 12.68 ng/L. CONCLUSIONS: This research hs-cTnI assay appears useful for risk stratification for death/MI in an ACS population.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".