High-Sensitivity Cardiac Troponin I Measurement for Risk Stratification in a Stable High-Risk Population
Bibliographic record
Abstract
BACKGROUND: Past investigations regarding the utility of high-sensitivity cardiac troponin I (cTnI) assays have been focused primarily on the acute coronary syndrome setting. We assessed whether such assays can predict future ischemic cardiovascular events in a stable high-risk population. METHODS: We quantified serum cTnI using an investigational high-sensitivity assay (hs-cTnI IUO, Beckman Coulter) in 2572 participants from the Heart Outcomes Prevention Evaluation (HOPE) study. The derived ROC curve cutoff and the 99th percentile for the hs-cTnI assay were assessed by Kaplan-Meier and Cox analyses for the primary outcome [composite of myocardial infarction (MI), stroke, and cardiovascular death] at 4.5 years of follow-up. We also assessed individual outcomes (MI, stroke, cardiovascular death) and the combined outcome (MI/cardiovascular death) by regression analyses to determine hazard ratios (HRs) and c statistics in models that included established risk factors, C-reactive protein, and N-terminal pro-B-type natriuretic peptide (NT-proBNP). RESULTS: Participants with hs-cTnI >6 ng/L (ROC cutoff) were at higher risk for the primary outcome (HR 1.38, 95% CI 1.09-1.76; P = 0.008, adjusted models). For the individual outcomes, participants with hs-cTnI above the 99th percentile (≥10 ng/L) had higher risk for cardiovascular death (HR 2.15, 95% CI 1.32-3.52; P = 0.002) and MI (HR 1.49, 95% CI 1.05-2.10; P = 0.025) but not stroke (HR 1.38, 95% CI 0.76-2.47; P = 0.288, adjusted models). Addition of hs-cTnI to an established risk model with NT-proBNP also yielded a higher c statistic for the combined outcome of MI/cardiovascular death. CONCLUSIONS: The investigational Beckman Coulter hs-cTnI assay provides prognostic information for future MI and cardiovascular death in a stable high-risk 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".