Evaluation of NT-proBNP and High Sensitivity C-Reactive Protein for Predicting Cardiovascular Risk in Patients with Arthritis Taking Longterm Nonsteroidal Antiinflammatory Drugs
Bibliographic record
Abstract
OBJECTIVE: Patients with arthritis frequently are at increased risk for future cardiovascular (CV) events. We investigated the performance of the cardiac biomarkers N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high sensitivity C-reactive protein (hsCRP) for predicting CV events in patients with arthritis taking chronic nonsteroidal antiinflammatory drugs (NSAID). METHODS: We evaluated 2-year CV outcomes in a prospective, nested biomarker study among patients (N = 6273) with rheumatoid arthritis and osteoarthritis treated with NSAID in the MEDAL (Multinational Etoricoxib and Diclofenac Arthritis Long-term) trial. Patients were stratified by quartiles of baseline NT-proBNP and established cutpoints of NT-proBNP and hsCRP. RESULTS: NT-proBNP demonstrated a strong graded relationship with CV outcomes, including CV death (p for trend < 0.0001), myocardial infarction (MI) (p for trend = 0.02), heart failure (HF) (p for trend < 0.0001), and a composite of thrombotic events (CV death, MI, stroke) or HF (p for trend < 0.0001). Baseline levels of hsCRP were not associated with CV events (CV death/MI/stroke/HF; p for trend = 0.65). NT-proBNP remained strongly predictive of CV events after adjustment for age, sex, diabetes, hypertension, hyperlipidemia, smoking, type of arthritis, body mass index, creatinine clearance, history of CV disease, and hsCRP (CV death/MI/stroke/HF: Q4 vs Q1 hazard ratio 3.53, 95% CI 1.89-6.58). Patients with a NT-proBNP level below 100 pg/ml had a 0.94% rate of thrombotic events or heart failure at 2 years. CONCLUSION: NT-proBNP is a simple and robust noninvasive indicator of CV risk in patients with arthritis. Risk stratification based on NT-proBNP may facilitate identification of patients with arthritis who are at low CV risk during chronic NSAID treatment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".