Effect of Uric Acid-Lowering Agents on Cardiovascular Outcome in Patients With Heart Failure: A Systematic Review and Meta-Analysis of Clinical Studies
Bibliographic record
Abstract
Several trials have been completed in patients with heart failure (HF) treated with uric acid (UA)-lowering agents with inconsistent results. We aimed to investigate whether lowering UA would have an effect on mortality and cardiovascular (CV) events in patients with HF in a systematic review and meta-analysis. The primary outcome measures were all-cause mortality, CV mortality, CV events, and CV hospitalization in patients with HF. We included 11 studies in our final analysis. Overall, allopurinol treatment was associated with a significant increase in the risk for all-cause mortality (hazard ratio [HR]: 1.24, 95% confidence interval [CI]: 1.04-1.49, P = .02). The trial heterogeneity is high (heterogeneity χ 2 = 37.3, I2 = 73%, P < .001). With regard to CV mortality, allopurinol treatment was associated with a 42% increased risk of CV mortality (HR: 1.42, 95% CI: 1.11-1.81, P = .005). There was a trend toward increased CV hospitalization in the same group (HR: 1.21, 95% CI: 0.95-1.53, P = .12). Uric acid-lowering treatments increase all-cause and CV mortality but did not increase CV hospitalization significantly in this study.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".