Independent Prognostic Importance of Blood Urea Nitrogen to Creatinine Ratio in Heart Failure
Bibliographic record
Abstract
Abstract Aim Blood urea nitrogen (BUN) to creatinine ratio is associated with worse outcomes in acute heart failure (HF) but little is known about its importance in chronic HF. Methods and results We combined individual patient data from clinical trials (HF with reduced ejection fraction [HFrEF]: PARADIGM-HF, ATMOSPHERE and DAPA-HF, and HF with preserved ejection fraction [HFpEF]: PARAGON-HF and I-PRESERVE). The primary outcome examined was a composite time to first HF hospitalization or cardiovascular death; its components and all-cause death were also examined. Each HF phenotype was categorized according to median BUN/creatinine ratio, generating four groups that is, HFpEF ≤ and >median BUN/creatinine ratio and HFrEF ≤ and >median BUN/creatinine ratio. The association between BUN/creatinine ratio and outcomes was evaluated using the Kaplan–Meier estimator and Cox proportional hazard models. Overall, 28 820 patients were analysed. The median (IQR) BUN/creatinine ratio was 20.1 (Q1–Q3 16.7–24.7) in HFpEF and 18.7 (15.2–22.8) in HFrEF. In both HFpEF and HFrEF, higher BUN/creatinine ratio was associated with older age, female sex, and diabetes, but similar estimated glomerular filtration rate (eGFR). The risk of each outcome examined was significantly higher in patients with BUN/creatinine ratio ≥median, compared to Conclusion Higher BUN/creatinine ratio was associated with worse outcomes in patients with chronic HF across the spectrum of left ventricular ejection fraction, independently of eGFR and NT-proBNP. BUN/creatinine ratio may reflect neurohumoral activation (especially increased arginine vasopressin), altered renal blood flow or other pathophysiologic mechanisms not incorporated in conventional prognostic variables.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".