Aspirin effect on the incidence of major adverse cardiovascular events in patients with diabetes mellitus: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Aspirin has been recommended for the prevention of major adverse cardiovascular events (MACE, composite of non-fatal myocardial infarction, non-fatal stroke, and cardiovascular death) in diabetic patients without previous cardiovascular disease. However, recent meta-analyses have prompted re-evaluation of this practice. The study objective was to evaluate the relative and absolute benefits and harms of aspirin for the prevention of incident MACE in patients with diabetes. METHODS: We performed a systematic review and meta-analysis on seven studies (N=11,618) reporting on the use of aspirin for the primary prevention of MACE in patients with diabetes. Two reviewers conducted a systematic search of electronic databases (MEDLINE, EMBASE, the Cochrane Library, and BIOSIS) and hand searched bibliographies and clinical trial registries. Reviewers extracted data in duplicate, evaluated the quality of the trials, and calculated pooled estimates. RESULTS: A total of 11,618 participants were included in the analysis. The overall risk ratio (RR) for MACE was 0.91 (95% confidence intervals, CI, 0.82-1.00) with little heterogeneity among trials (I2 0.0%). Secondary outcomes of interest included myocardial infarction (RR, 0.85; 95% CI, 0.66-1.10), stroke (RR, 0.84; 95% CI, 0.64-1.11), cardiovascular death (RR, 0.95; 95% CI, 0.71-1.27), and all-cause mortality (RR, 0.95; 95% CI, 0.85-1.06). There were higher rates of hemorrhagic and gastrointestinal events. In absolute terms, these relative risks indicate that for every 10,000 diabetic patients treated with aspirin, 109 MACE may be prevented at the expense of 19 major bleeding events (with the caveat that the relative risk for the latter is not statistically significant). CONCLUSIONS: The studies reviewed suggest that aspirin reduces the risk of MACE in patients with diabetes without cardiovascular disease, while also causing a trend toward higher rates of bleeding and gastrointestinal complications. These findings and our absolute benefit and risk calculations suggest that those with diabetes but without cardiovascular disease lie somewhere between primary and secondary prevention patients on the spectrum of benefit and risk. This underscores the importance of considering individual risk in clinical decision making regarding aspirin in those with diabetes.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.002 | 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".