Cardiovascular and kidney implications of the initial response in estimated glomerular filtration rate to sodium glucose cotransporter-2 inhibition with empagliflozin: the ‘eGFR dip’ in EMPA-REG OUTCOME
Bibliographic record
Abstract
Background Empagliflozin (EMPA) reduces cardiovascular and kidney risk in patients with type 2 diabetes (T2D) and cardiovascular disease (CVD). EMPA induces an initial ‘dip’ in mean estimated glomerular filtration rate (eGFR). Methods 6,668 Participants of EMPA-REG OUTCOME treated with EMPA 10mg, 25mg or placebo (PBO) and eGFR available were categorised by initial percentage eGFR change from baseline at week 4. Impact of an ‘eGFR dip’ >10% on risk reduction with EMPA for CV and kidney outcomes was assessed in a Cox regression landmark analysis adjusting for such ‘eGFR dip’. Results ‘eGFR dip’ of >10% from baseline at Week 4 occurred in 28.3% participants on EMPA versus 13.4% on PBO; an eGFR decline of >30% in 1.4% and 0.9%, respectively. Odds ratio [OR; 95% CI] for an ‘eGFR dip’ with EMPA vs. PBO was 2.7 [2.3–3.0]. Diuretic use and/or higher KDIGO risk category at baseline were predictive of an ‘eGFR dip’ in EMPA vs. PBO. ‘eGFR dip’ did not affect risk reduction for CV death, hospitalization for heart failure (HHF) or the primary kidney outcome. Adverse events, especially acute renal failure rates, were lower or similar in EMPA vs. PBO, regardless of baseline predictive factors for an ‘eGFR dip’. Conclusion T2D patients with more advanced kidney disease and/or on diuretic therapy at baseline were more likely to have an initial ‘eGFR dip’ >10% with EMPA. EMPA treatment was safe and associated with improved CV death, HHF and kidney outcomes, regardless of baseline predictive factors or such initial ‘eGFR dip’. Publication History Publication Date: 06 May 2021 (online) © 2021. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".