Temporal Biomarker Profiling Reveals Longitudinal Changes in Risk of Death or Myocardial Infarction in Non–ST-Segment Elevation Acute Coronary Syndrome
Bibliographic record
Abstract
BACKGROUND: There are conflicting data on whether changes in N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity C-reactive protein (hs-CRP) concentrations between time points (delta NT-proBNP and hs-CRP) are associated with a change in prognosis. METHODS: We measured NT-proBNP and hs-CRP at 3 time points in 1665 patients with non-ST-segment elevation acute coronary syndrome (NSTEACS). Cox proportional hazards was applied to the delta between temporal measurements to determine the continuous association with cardiovascular events. Effect estimates for delta NT-proBNP and hs-CRP are presented per 40% increase as the basic unit of temporal change. RESULTS: Median NT-proBNP was 370.0 (25th, 75th percentiles, 130.0, 996.0), 340.0 (135.0, 875.0), and 267.0 (111.0, 684.0) ng/L; and median hs-CRP was 4.6 (1.7, 13.1), 1.9 (0.8, 4.5), and 1.8 (0.8, 4.4) mg/L at baseline, 30 days, and 6 months, respectively. The deltas between baseline and 6 months were the most prognostically informative. Every +40% increase of delta NT-proBNP (baseline to 6 months) was associated with a 14% greater risk of cardiovascular death (adjusted hazard ratio (HR) 1.14, 95% CI, 1.03-1.27) and with a 14% greater risk of all-cause death (adjusted HR 1.14, 95% CI, 1.04-1.26), while every +40% increase of delta hs-CRP (baseline to 6 months) was associated with a 9% greater risk of the composite end point (adjusted HR 1.09, 95% CI, 1.02-1.17) and a 10% greater risk of myocardial infarction (adjusted HR 1.10, 95%, CI 1.00-1.20). CONCLUSIONS: Temporal changes in NT-proBNP and hs-CRP are quantitatively associated with future cardiovascular events, supporting their role in dynamic risk stratification of NSTEACS. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT00699998.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".