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Record W3032918433 · doi:10.1093/ndt/gfaa146.lb005

LB005KIDNEY IMPLICATIONS OF THE INITIAL EGFR RESPONSE TO SGLT2 INHIBITION WITH EMPAGLIFLOZIN: THE ‘EGFR DIP’ IN EMPA-REG OUTCOME

2020· article· en· W3032918433 on OpenAlexaff
Bettina J. Kraus, Matthew R. Weir, George L. Bakris, Michaela Mattheus, David Cherney, Naveed Sattar, Hiddo J.L. Heerspink, Ivana Ritter, Maximilian von Eynatten, Bernard Zinman, Silvio E. Inzucchi, Christoph Wanner, Audrey Koitka‐Weber

Bibliographic record

VenueNephrology Dialysis Transplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai HospitalUniversity Health Network
Fundersnot available
KeywordsEmpagliflozinMedicineEMPARenal functionInternal medicineDiabetic nephropathyKidney diseaseType 2 diabetesNephropathyUrologyDiabetes mellitusEndocrinologyKidney

Abstract

fetched live from OpenAlex

Abstract Background and Aims Empagliflozin (EMPA) reduces cardiovascular and renal risk in patients with type 2 diabetes (T2D) and established cardiovascular disease (CVD). EMPA induces an initial ‘dip’ in estimated glomerular filtration rate (eGFR). Although considered to be of haemodynamic origin and largely reversible, this needs to be better understood. We investigated whether the initial eGFR dip after EMPA initiation was influenced by baseline characteristics and/or might have an impact on the EMPA-induced risk reduction in kidney outcomes. Method In the EMPA-REG OUTCOME trial, patients with T2D and established CVD were treated (1:1:1) with EMPA 10 mg, 25 mg or placebo (PBO), in addition to standard of care. In this post hoc analysis, 6,668 participants who received at least one dose of study drug and had an available eGFR value at both baseline and Week 4 were categorised by initial percentage eGFR change from baseline. A multivariate logistic regression model was used to identify which baseline characteristics are predictive of an initial eGFR dip >10% in EMPA-treated participants versus PBO. Across these predictive baseline factors, we investigated the occurrence of incident or worsening nephropathy, hard kidney outcomes (defined as doubling of serum creatinine with eGFR [MDRD] ≤45 ml/min/1.73 m2 or initiation of renal replacement therapy or death from renal disease), and kidney safety (narrow standardized MedDRA query acute renal failure). The impact of an eGFR dip >10% on the risk reduction with EMPA for incident or worsening nephropathy was assessed using Cox regression analysis adjusting for such eGFR dip. Results In the EMPA-REG OUTCOME trial cohort, an initial eGFR dip of >10% from baseline at Week 4 occurred in more than twice as many participants on EMPA (28.3%) compared to PBO (13.4%). However, a more pronounced eGFR dip of >30% was uncommon, occurring in only 1.4% and 0.9%, respectively. Within the EMPA group, participants with an eGFR dip >10% were significantly older, had longer diabetes duration and showed a higher KDIGO (Kidney Disease: Improving Global Outcomes) risk category. Diuretic use and/or higher KDIGO risk category at baseline were predictive of an initial eGFR dip of >10% in EMPA vs. PBO. The average odds ratio [OR; 95% CI] for an eGFR dip >10% with EMPA was 2.7 [2.3–3.0]. In subgroups with a dipping odds ratio below vs. above that average, beneficial treatment effects with EMPA on incident or worsening nephropathy and the hard kidney outcome were consistent (panel A). Also, an eGFR dip >10% did not affect risk reduction for the primary kidney outcome (panel B). Acute renal failure rates were generally 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. However, EMPA treatment appeared to be safe and was associated with improved kidney outcomes, regardless of these baseline predictive factors or an initial eGFR dip >10%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.025
GPT teacher head0.289
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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