Cardiovascular effects of urate-lowering therapies in patients with chronic gout: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: To determine if urate-lowering treatment (ULT) in gout can reduce cardiovascular (CV) outcomes. Methods: Randomized trials were searched for treatment with ULT in gout. Eligible trials had to report CV safety of a ULT. Potential medications included allopurinol, febuxostat, pegloticase, rasburicase, probenecid, benzbromarone, sulphinpyrazone, losartan, fenofibrate and sodium-glucose linked transporter 2 inhibitors. Results: A total of 3084 citations were found, with 642 duplicates. After the primary screen, 35 studies were selected for review. Several trials did not report CV events. Six were not randomized controlled trials (RCTs). Four studies reported no events in either intervention arm while the other four had 40 events in the febuxostat group ( n = 3631) and 5 in allopurinol group ( n = 1154). Overall, the pooled analysis did not show a significant difference between the two [febuxostat vs allopurinol: relative risk (RR) 1.69 (95% CI 0.54, 5.34), P = 0.37]. CV events did not decrease over time. Comparing shorter studies (<52 weeks) to longer ones did not reveal any statistical differences. However, in long-term studies with febuxostat vs allopurinol, results were nearly significant, with more CVE occurring with febuxostat treatment. Comparing any ULT to placebo (eight studies, n = 2221 patients) did not demonstrate a significant difference in non-Anti-Platelet Trialists' Collaboration events [any ULT vs placebo: RR 1.47 (95% CI 0.49, 4.40), P = 0.49] or all-cause mortality [any ULT vs placebo: RR 1.45 (95% CI 0.35, 5.77), P = 0.60]. Conclusion: RCT data do not suggest differences in CV events among ULTs in gout. Trials had few events despite high-risk patients being enrolled and may have been too short to show CV reduction by controlling inflammatory attacks and lowering uric acid.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.004 | 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".