Adverse events associated with individual statin treatments for cardiovascular disease: an indirect comparison meta-analysis
Bibliographic record
Abstract
BACKGROUND: Statins are the most widely prescribed drug available. Due to this reason, it is important to understand the risks involved with the drug class and individual statins. AIM: We conducted a meta-analysis and employed indirect comparisons to identify differing risk effects across statins. DESIGN: We included any randomized clinical trial (RCT) of atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin and simvastatin used for cardiovascular disease event prevention. The main outcome was adverse events [all-cause mortality, cancers, rhabdomylosis, diabetes, aspartate and alanine aminotransferase (AST/ALT), and creatinine kinase (CK) increases beyond the upper limit of normal]. In order to evaluate the relative effects of each drug on adverse events, we calculated adjusted indirect comparisons of the adverse-event outcomes. RESULTS: Seventy-two trials involving 159,458 patients met our inclusion criteria. Overall, statin treatments significantly increased the rate of diabetes when compared to controls (OR: 1.09; 95% CI: 1.02-1.16) and elevated AST (OR: 1.31; 95% CI: 1.04-1.66) and ALT (OR: 1.28; 95% CI: 1.11-1.48) levels when compared to controls. Using indirect comparisons, we also found that atorvastatin significantly elevated AST levels compared to pravastatin (OR: 2.21; 95% CI: 1.13-4.29) and simvastatin significantly increased CK levels when compared to rosuvastatin (OR: 4.39; 95% CI: 1.01-19.07). Higher dose studies had increased risk of AST elevations. DISCUSSION: Although statins are generally well tolerated, there are risks associated with almost all drugs. With few exceptions, statins appear to exert a similar risk across individual drugs.
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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.063 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".