132-LB: Implications of Initial EGFR Response to Empagliflozin Treatment Effects
Bibliographic record
Abstract
In EMPA-REG OUTCOME, empagliflozin (EMPA) reduced the risk of CV death by 38% in T2D patients (pts) with CV disease. EMPA induces an initial, reversible dip in estimated glomerular filtration rate (eGFR). We investigated whether this transient initial renal hemodynamic effect was influenced by baseline characteristics or had an impact on the EMPA-induced risk reduction in CV death. In a post-hoc analysis, among the 6,668 pts randomized to EMPA 10 mg, 25 mg or placebo [PBO] with eGFR available, 28.3% of EMPA pts vs. 13.4% PBO experienced an initial eGFR decline >10% from baseline to Week 4 (odds ratio [OR; 95% CI]: 2.7 [2.3-3.0]). Multivariate logistic regression was used to identify baseline characteristics predictive of eGFR dip >10%. The impact of an eGFR dip >10% on the risk reduction in CV death was assessed using Cox regression. Diuretic use and higher KDIGO (Kidney Disease: Improving Global Outcomes) risk category at baseline were predictive of an eGFR dip >10% with EMPA vs. PBO. Serious adverse events were generally lower or similar in EMPA vs. PBO, regardless of predictive baseline factors. EMPA-induced CV death risk reduction was consistent across subgroups below vs. above average eGFR dipping OR (Panel A) and not affected by eGFR dip >10% (Panel B). T2D pts with more advanced kidney disease and/or on diuretic therapy were more likely to experience an eGFR dip >10% with EMPA. EMPA reduced CV death, regardless of an initial eGFR dip >10%. Disclosure S.E. Inzucchi: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lexicon Pharmaceuticals, Inc., Novo Nordisk A/S, Sanofi. Consultant; Self; Abbott, Merck & Co., Inc., vTv Therapeutics. B.J. Kraus: Research Support; Self; Boehringer Ingelheim International GmbH. Speaker’s Bureau; Self; Boehringer Ingelheim International GmbH. M.R. Weir: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Scientific Affairs, LLC., Merck & Co., Inc. G. Bakris: Consultant; Self; Alnylam, Merck & Co., Inc., Relypsa, Inc., Teijin Pharma Limited. Other Relationship; Self; Bayer AG, Novo Nordisk Inc., Vascular Dynamics. M. Mattheus: None. D. Cherney: Research Support; Self; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca and Novo-Nordisk. Other Relationship; Self; from Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, Abbvie, Janssen, Bayer, Prometic, BMS and Novo-Nordisk. N. Sattar: Advisory Panel; Self; Amgen, AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk A/S, Pfizer Inc., Sanofi. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc. H.L. Heerspink: Consultant; Self; AbbVie Inc., AstraZeneca, Boehringer Ingelheim International GmbH, CSL Behring, Gilead Sciences, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Mitsubishi Tanabe Pharma Corporation, Mundipharma International, Retrophin, Inc. I. Ritter: Employee; Self; Boehringer Ingelheim International GmbH. M. von Eynatten: Other Relationship; Self; Boehringer Ingelheim International GmbH. B. Zinman: Advisory Panel; Self; Abbott, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk Inc., Sanofi-Aventis. C. Wanner: Advisory Panel; Self; Eli Lilly and Company, Merck & Co., Inc., Mundipharma International. Consultant; Self; Boehringer Ingelheim (Canada) Ltd., Sanofi Genzyme. Speaker’s Bureau; Self; AstraZeneca. Other Relationship; Self; Boehringer Ingelheim International GmbH. A. Koitka-Weber: Employee; Self; Boehringer Ingelheim International GmbH.
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 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".