Measurement of Troponin and Natriuretic Peptides Shortly After Admission in Patients with Heart Failure—Does it Add Useful Prognostic Information? an Analysis of the Value of Endothelin Receptor Inhibition with Tezosentan in Acute Heart Failure Studies (VERITAS)
Bibliographic record
Abstract
OBJECTIVE: Plasma concentrations of B-type natriuretic peptide (BNP) and troponin are often measured for diagnostic purposes when patients are admitted with heart failure, but their prognostic value when measured soon after admission is uncertain. We aimed to investigate the added prognostic value of admission measurements of BNP and troponins in patients with acute heart failure. METHODS AND RESULTS: Multivariable prognostic models for death or any worsening heart failure (WHF) or rehospitalization for WHF by 30 days, 30-day death or rehospitalization for WHF, and 90-day mortality were constructed using baseline data from the Value of Endothelin Receptor Inhibition with Tezosentan in Acute heart failure Studies (VERITAS) including BNP and troponin I. Of 1347 patients, the median (interquartile range) value of BNP was 422 (156-945) pg/mL and 855 (63%) had measurable troponin I. By 30 days, 432 patients had died or experienced WHF. Clinical variables had only moderate predictive performance that was not substantially improved by BNP or troponin I (c-indices 0.6528 and 0.6595, respectively). By 30 days, 150 patients died or were rehospitalized for WHF. The c-index using clinical variables (0.6855) was not improved by adding biomarkers. By 90 days, 135 patients had died. The c-index for mortality was somewhat better than for composite outcomes (0.7394) but improved little with biomarkers (0.7461). CONCLUSION: Routine clinical data recorded at the time of admission in patients with acute heart failure are poor at predicting recurrent admissions but somewhat better at predicting mortality. Neither BNP nor troponin measured at admission improved predictions; measurement closer to discharge, or of other novel biomarkers, might perform differently.
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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 | 0.005 | 0.009 |
| 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 | 0.000 |
| 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".