Effects of Ertugliflozin on Kidney End Points in Patients with Non-Albuminuric Diabetic Kidney Disease in VERTIS CV
Bibliographic record
Abstract
Background: Non-albuminuric diabetic kidney disease (NA-DKD) is an increasingly recognised condition. Data from VERTIS CV (NCT01986881) were analyzed to study the impact of ertugliflozin on kidney outcomes in patients with NA-DKD. Methods: Patients with type 2 diabetes mellitus and atherosclerotic cardiovascular disease were randomized (1:1:1) to ertugliflozin 5 mg, 15 mg (both doses were pooled for analyses) and placebo. Subgroups were defined by baseline eGFR (mL/min/1.73 m2) and UACR (mg/g): No DKD (N-DKD), eGFR ≥60 + UACR <30 (n=3916); NA-DKD, eGFR <60 + UACR <30 (n=867); albuminuric-DKD (A-DKD), UACR ≥30 (n=3247). eGFR slopes (chronic from week [W]6 to W260 and total from W0 to W260) and Cox proportional hazards for the time to first event of a kidney composite were assessed. Results: The NA-DKD subgroup had the slowest rate of total eGFR decline and the A-DKD subgroup the fastest rate of decline (Figure). The effect of ertugliflozin to slow the rate of eGFR decline vs placebo did not significantly differ across the subgroups. The hazard ratio for ertugliflozin showing reduction in the risk of the composite kidney outcome vs placebo was consistent across subgroups, Pinteraction = 0.26 (Table).FigureConclusions: In VERTIS CV, participants with NA-DKD had the slowest rate of eGFR decline over time and lower kidney composite outcome event rates. Funding: Commercial Support - The study and this analysis were funded by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, in collaboration with Pfizer Inc. Medical writing and/or editorial assistance was provided by Moamen Hammad, PhD, and Ian Norton, PhD, both of Scion, London, UK. This assistance was funded by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA and Pfizer Inc., New York, NY, USA.Table
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".