Safety of Empagliflozin in Patients With Type 2 Diabetes and Chronic Kidney Disease: Pooled Analysis of Placebo-Controlled Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: To assess the safety of empagliflozin in patients with type 2 diabetes and moderate to severe chronic kidney disease (CKD) (category G3-4) enrolled in clinical trials. RESEARCH DESIGN AND METHODS: This analysis pooled data from 19 randomized, placebo-controlled, phase 1-4 clinical trials and 1 randomized, placebo-controlled extension study in which patients received empagliflozin 10 mg or 25 mg daily. Time to first occurrence of adverse events (AEs) was evaluated using Kaplan-Meier analysis and multivariable Cox regression models. RESULTS: Among a total of 15,081 patients who received at least one study drug dose, 1,522, 722, and 123 were classified as having G3A, G3B, and G4 CKD, respectively, at baseline. Demographic and clinical characteristics were similar between treatment groups across CKD categories. Rates of serious AEs, AEs leading to discontinuation, and events of special interest (including lower limb amputations and acute renal failure [ARF]) were also similar between empagliflozin and placebo across CKD subgroups. In adjusted Cox regression analyses, risks for volume depletion and ARF were similar for empagliflozin and placebo in the combined group with CKD categories G3B and G4 and the G3A group. Notably lower risks were observed in both groups for hyperkalemia (hazard ratio 0.59 [95% CI 0.37-0.96, P = 0.0323] and 0.48 [0.26-0.91, P = 0.0243], respectively) and edema (0.47 [0.33-0.68, P < 0.0001] and 0.44 [0.28-0.68, P = 0.0002], respectively). CONCLUSIONS: Use of empagliflozin in patients with type 2 diabetes and advanced CKD raised no new safety concerns and may have beneficial effects on the development of hyperkalemia and edema.
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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 | 0.055 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".