Impact of SGLT2 Inhibitors on Renal Function in Type 2 Diabetic Patients with Coronary Artery Disease Undergoing Percutaneous Intervention: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Contrast agents directly cause kidney toxicity in patients who are candidates for percutaneous intervention having cardiovascular disease with type 2 diabetes. AIMS: This meta-analysis aims to assess the effects of SGLT2i on renal function in individuals undergoing percutaneous intervention. METHODS: The databases used for the search included Google Scholar, PubMed, Cochrane Central Registry of Controlled Trials, and Scopus. We considered randomized controlled and observational studies published from January, 2013, to August, 2023. Eligibility to include the studies was assessed independently. The Cochrane modified data extraction form and Joanna Briggs Institute were used to extract the data. The quality of the studies was evaluated using the Cochrane risk of bias tool and the Newcastle-Ottawa scale. The GradePro software was used to measure the certainty of the evidence. RESULTS: The pooled estimate showed a substantial reduction in serum creatinine levels at 48 and 72 hours post-PCI who received SGLT2i (MD -9.57; 95% CI -18.36, -0.78; p-value 0.03 and MD - 14.40; 95% CI -28.57, -0.22; p-value 0.05). There was a decrease in the occurrence of the CI-AKI among SGT2i users (RR: 0.46; 95% CI: 0.32, 0.67; p value< 0.0001). No substantial difference was observed in the number of patients requiring hemodialysis; however, a lower proportion of patients among SGLT2i users required hemodialysis (RR: 0.88; 95% CI: 0.19, 4.07; p-value = 0.87). CONCLUSION: The use of SGLT2i confers substantial beneficial effects on kidney function and reduces the occurrence of contrast-induced acute kidney injury among diabetes patients undergoing PCI procedures with cardiovascular disease.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.003 | 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".