Efficacy and safety of statin treatment for cardiovascular disease: a network meta-analysis of 170 255 patients from 76 randomized trials
Bibliographic record
Abstract
BACKGROUND: Statins represent the largest selling class of cardiovascular drug in the world. Previous randomized trials (RCTs) have demonstrated important clinical benefits with statin therapy. AIM: We combined evidence from all RCTs comparing a statin with placebo or usual care among patients with and without prior coronary heart disease (CHD) to determine clinical outcomes. DESIGN: We searched independently, in duplicate, 12 electronic databases (from inception to August 2010), including full text journal content databases, to identify all statin versus inert control RCTs. We included RCTs of any statin versus any non-drug control in any populations. We abstracted data in duplicate on reported major clinical events and adverse events. We performed a random-effects meta-analysis and meta-regression. We performed a mixed treatment comparison using Bayesian methods. RESULTS: We included a total of 76 RCTs involving 170,255 participants. There were a total of 14,878 deaths. Statin therapy reduced all-cause mortality, Relative Risk (RR) 0.90 [95% confidence interval (CI) 0.86-0.94, P ≤ 0.0001, I(2)=17%]; cardiovascular disease (CVD) mortality (RR 0.80, 95% CI 0.74-0.87, P<0.0001, I(2)=27%); fatal myocardial infarction (MI) (RR 0.82, 95% CI 0.75-0.91, P<0.0001, I(2)=21%); non-fatal MI (RR 0.74, 95% CI 0.67-0.81, P ≤ 0.001, I(2)=45%); revascularization (RR 0.76, 95% CI 0.70-0.81, P ≤ 0.0001); and a composite of fatal and non-fatal strokes (0.86, 95% CI 0.78-0.95, P=0.004, I(2)=41%). Adverse events were generally mild, but 17 RCTs reported on increased risk of development of incident diabetes [Odds Ratio (OR) 1.09; 95% CI 1.02-1.17, P=0.001, I(2)=11%]. Studies did not yield important differences across populations. We did not find any differing treatment effects between statins. DISCUSSION: Statin therapies offer clear benefits across broad populations. As generic formulations become more available efforts to expand access should be a priority.
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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 | 0.035 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".