Empagliflozin Improves Kidney Outcomes in Patients With or Without Heart Failure
Bibliographic record
Abstract
Background In EMPA-REG OUTCOME (Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients) empagliflozin significantly reduced the risk of cardiovascular and kidney outcomes in patients with type 2 diabetes mellitus and established cardiovascular disease. Post hoc, we evaluated empagliflozin on kidney outcomes in patients with or without heart failure (HF). Methods and Results Individuals were randomized to empagliflozin 10 mg, 25 mg, or placebo. Prespecified analyses by baseline HF status included risk of incident or worsening nephropathy and estimated glomerular filtration rate slope analyses. Cox proportional hazards models assessed consistency of treatment effect across subgroups. Safety evaluations included kidney-related adverse events. At baseline, 244 (10.5%) and 462 (9.9%) patients had HF in the placebo and empagliflozin groups, respectively. Overall, the incidence of kidney outcome events was numerically higher in patients with than without HF. In the HF group, empagliflozin reduced risk of incident or worsening nephropathy or cardiovascular death by 43% (hazard ratio, 0.57 [95% CI, 0.42-0.77]) and progression to macroalbuminuria by 50% (hazard ratio, 0.50 [0.33-0.75]). After an initial transient decrease, estimated glomerular filtration rate stabilized over time with empagliflozin but gradually declined with placebo. Kidney effects in patients with HF were consistent with those in the overall study population (all P values for interaction >0.05). Across groups, the incidence rate of kidney-related adverse events/100 patient-years was higher in patients with than without HF; however, overall rates were comparable between groups. Conclusions These findings from EMPA-REG OUTCOME support the hypothesis that empagliflozin could reduce the risk of clinically relevant kidney events and may slow progression of chronic kidney disease in individuals with type 2 diabetes mellitus regardless of HF status. Clinical Trial Registration URL: https://www.clinicaltrials.gov . Unique identifier: NCT01131676.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".