Efficacy of Allopurinol in Cardiovascular Diseases: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Given current evidence, the use of allopurinol for the prevention of major cardiovascular events (acute cardiovascular syndrome (ACS) or cardiovascular mortality) in patients undergoing coronary artery bypass graft (CABG), after index ACS or heart failure remains unknown. METHODS: Multiple databases were queried to identify studies comparing the efficacy of allopurinol in patients undergoing CABG, after ACS or heart failure. The unadjusted odds ratio (OR) was calculated using a random effect model. RESULTS: A total of nine studies comprising 850 patients (allopurinol 480, control 370) were identified. The pooled OR of periprocedural ACS (OR: 0.25, 95% confidence interval (CI): 0.06 - 0.96, P = 0.05) and cardiovascular mortality (OR: 0.22, 95% CI: 0.07 - 0.71, P = 0.01) was significantly lower in patients receiving allopurinol during CABG compared to patients in the control group. The overall number needed to treat (NNT) to prevent one ACS event was 11 (95% CI: 7 - 28), while the NNT to prevent one death was 24 (95% CI: 13 - 247). By contrast, the odds of cardiovascular mortality in the allopurinol group were not significantly different from the control group in patients on long-term allopurinol after ACS or heart failure (OR: 0.33, 95% CI: 0.01 - 8.21, P = 0.50) and (OR: 1.12, 95% CI: 0.39 - 3.20, P = 0.83), respectively. Similarly, the use of allopurinol did not reduce the odds of recurrent ACS events at 2 years (OR: 0.32, 95% CI: 0.03 - 3.18, P = 0.33). CONCLUSIONS: Periprocedural use of allopurinol might be associated with a significant reduction in the odds of ACS and cardiovascular mortality in patients undergoing CABG. Allopurinol, however, offers no long-term benefits in terms of secondary prevention of ACS or mortality. Larger scale studies are needed to validate our findings.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".