Characterization and implications of the initial estimated glomerular filtration rate ‘dip’ upon sodium-glucose cotransporter-2 inhibition with empagliflozin in the EMPA-REG OUTCOME trial
Bibliographic record
Abstract
decline in estimated glomerular filtration rate (eGFR). Although considered to be of hemodynamic origin and largely reversible, this 'eGFR dip' may cause concern in clinical practice, which highlights the need to better understand its incidence and clinical implications. In this post hoc analysis of the EMPA-REG OUTCOME trial, 6,668 participants randomized to empagliflozin 10 mg, 25 mg or placebo with eGFR available at baseline and week four were categorized by initial eGFR change into three groups; over 10% decline ('eGFR dipper'), over 0 and up to 10% decline ('eGFR intermediate'), no eGFR decline ('eGFR non-dipper'). Baseline characteristics of 'eGFR intermediate' and 'eGFR non-dipper' were generally comparable. An initial 'eGFR dip' was observed in 28.3% of empagliflozin versus 13.4% of placebo-treated participants; odds ratio 2.7 [95% Confidence Interval 2.3-3.0]. In multivariate logistic regression, diuretic use and higher KDIGO risk category at baseline were independently predictive of an 'eGFR dip' in empagliflozin versus placebo. Safety and beneficial treatment effects with empagliflozin on cardiovascular and kidney outcomes were consistent across subgroups based on these predictive factors. The initial 'eGFR dip' did not have a major impact on the treatment effect of empagliflozin on subsequent cardiovascular death, hospitalization for heart failure, and incident or worsening kidney disease. Thus, patients with type 2 diabetes with more advanced kidney disease and/or on diuretic therapy were more likely to experience an 'eGFR dip' of over 10% with empagliflozin, but reduction in cardiovascular and kidney outcomes was not relevantly modified by such 'eGFR dip.'
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".