Sacubitril/Valsartan Reduces Serum Uric Acid Concentration, An Independent Predictor of Adverse Outcomes in PARADIGM-HF
Bibliographic record
Abstract
Abstract Aims Elevated serum uric acid concentration (SUA) has been associated with an increased risk of cardiovascular disease, but this may be due to unmeasured confounders. We examined the association between SUA and outcomes as well as the effect of sacubitril/valsartan on SUA in patients with heart failure with reduced ejection fraction (HFrEF) in PARADIGM-HF. Methods and results The association between SUA and the primary composite outcome of cardiovascular death or heart failure (HF) hospitalization, its components, and all-cause mortality was examined using Cox regression analyses among 8213 patients using quintiles (Q1–Q5) of SUA adjusted for baseline prognostic variables including estimated glomerular filtration rate (eGFR), diuretic dose, and log N-terminal pro-brain natriuretic peptide. Change in SUA from baseline over 12 months was also evaluated in each treatment group. Patients in Q5 (SUA ≥8.6 mg/dL) compared with Q1 (<5.4 mg/dL) were younger (62.8 vs. 64.2 years), more often male (88.7% vs. 63.1%), had lower systolic blood pressure (119 vs. 123 mmHg), lower eGFR (57.4 vs. 76.6 mL/min/1.73 m2), and greater diuretic use. Higher SUA was associated with a higher risk of the primary outcome (adjusted hazard ratios) Q5 vs. Q1 = 1.28 [95% confidence intervals (1.09–1.50), P = 0.003], cardiovascular death [1.44 (1.11–1.77), P = 0.001], HF hospitalization [1.37 (1.11–1.70), P = 0.004], and all-cause mortality [1.36 (1.13–1.64), P = 0.001]. Compared with enalapril, sacubitril/valsartan reduced SUA by 0.24 (0.17–0.32) mg/dL over 12 months (P < 0.0001). Sacubitril/valsartan improved outcomes, irrespective of SUA concentration. Conclusion Serum uric acid concentration was an independent predictor of worse outcomes after multivariable adjustment in patients with HFrEF. Compared with enalapril, sacubitril/valsartan reduced SUA and improved outcomes irrespective of SUA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".