Comparison of Short-Term Treatment With Atorvastatin Versus Rosuvastatin for Preventing Contrast-Associated Acute Kidney Injury in Patients Undergoing Coronary Angiography/Percutaneous Coronary Intervention: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The effect of atorvastatin compared with rosuvastatin on the prevention of contrast-associated acute kidney injury (CA-AKI) after percutaneous coronary intervention (PCI) has been heterogeneous in different studies, which may be due to the small size of the initial studies. This systematic review and meta-analysis aimed to compare the effect of atorvastatin versus rosuvastatin on preventing CA-AKI in patients undergoing PCI. The databases PubMed, Embase, Google Scholar, Cochrane Library, and Web of Science were searched by two independent investigators from 2000 to 2024 to find articles that evaluated the effect of atorvastatin versus rosuvastatin on the prevention of CA-AKI in cardiac patients undergoing PCI. An absolute increase of serum creatinine (SCr) ≥0.3 mg/dl or an increase of ≥50% from baseline within 48 to 72 hours after contrast exposure was defined as CA-AKI. This systematic review and meta-analysis were conducted according to the PRISMA guidelines. Eight studies involving 3,998 Patients who underwent PCI were included. A pooled estimate of 8 studies showed that the overall incidence of CA-AKI after PCI in patients receiving statins, regardless of type, was 8.5 % (95% CI: 7.6, 9.3%). Subgroup analysis showed that the incidence of CA-AKI in patients receiving atorvastatin and rosuvastatin was 8.5% and 8.7%, respectively. The protective effect of atorvastatin on preventing CA-AKI in cardiac patients undergoing PCI was similar to that of rosuvastatin. Both atorvastatin and rosuvastatin similarly reduced the overall incidence of CA-AKI in patients undergoing PCI. The effects of atorvastatin and rosuvastatin on preventing CA-AKI after PCI were also similar.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.002 | 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".