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Cardiorenal outcomes with ertugliflozin by baseline metformin use: post-hoc analyses of the VERTIS CV trial

2021· article· en· W4292495782 on OpenAlexaff
Francesco Cosentino, Chris Cannon, David Z.I. Cherney, Samuel Dagogo‐Jack, RE Pratley, B Charbonnel, Weichung-Joseph Shih, James P. Mancuso, Mario Maldonado, Robert Frederich, Nilo B. Cater, S Wang, Darren K. McGuire

Bibliographic record

VenueEuropean Heart Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineMetforminInternal medicinePropensity score matchingPost-hoc analysisType 2 diabetesPlaceboClinical trialDiabetes mellitusEndocrinologyInsulin

Abstract

fetched live from OpenAlex

Abstract Introduction We analysed data from the VERTIS CV trial that investigated the CV and kidney safety and efficacy of the sodium-glucose cotransporter 2 (SGLT2) inhibitor ertugliflozin (ERTU) vs placebo (PBO) to assess the impact of metformin (MET) use at baseline (BL). These analyses are timely because the recent ESC guidelines recommendation to use SGLT2 inhibitor or GLP-1 RAs as initial glucose-lowering therapy in patients with type 2 diabetes (T2D) with or at high risk for atherosclerotic cardiovascular disease (ASCVD) has been questioned because outcome trials of these drug classes included a large proportion of patients with MET as background therapy, yet MET was not used at BL in approximately 25% of patients in each trial. Purpose These analyses determined cardiorenal endpoints of VERTIS CV according to use of BL MET to assess for evidence of treatment effect modification for ERTU by BL MET use, adjusting for the probability (propensity) of BL MET use. Methods VERTIS CV was an international, double-blind, PBO-controlled trial of 2 doses of ERTU (5 mg; 15 mg) vs PBO in patients with T2DM and ASCVD. As prospectively planned, the 2 ERTU dose groups were combined for all analyses vs PBO. Differences in risk of CV and kidney outcomes between ERTU and PBO across subgroups by BL MET use were conducted using Cox proportional hazards model along with propensity adjustment using inverse probability for treatment weighting to account for differences in patient mix between those with and without BL MET influenced by individual BL characteristics and risk factors. Treatment (categorical), BL MET use (categorical) and the interaction term between treatment and BL MET use subgroup (no or yes) were used in each model to assess effect modification by BL MET use. Hazard ratio and 95% CI are presented along with Pinteraction for evaluation of treatment effect modification by BL MET use. Results In VERTIS CV, 8246 patients were randomised to ERTU 5 mg, 15 mg or PBO. Of these, 6286 (76%) patients used MET (alone or with other glucose-lowering agents [GLA]) at BL. Differences in BL characteristics by BL MET use subgroup (no or yes) included a higher mean UACR (204.4 vs 129.8 mg/g), more patients with eGFR <60 mL/min/1.73 m2 (34.8% vs 17.9%), more patients on a single GLA (76.9% vs 18.3%), higher insulin use (67.6% vs 40.9%), lower sulphonylurea use (32.2% vs 43.8%) and a slightly longer disease duration (14.4 vs 12.5 years) in the subgroup without vs with BL MET, respectively. No significant differences in the relative risk for cardiorenal outcomes were observed with or without BL MET use (Figure; all Pinteraction values >0.05). Conclusions In VERTIS CV, there was no evidence for effect modification by BL MET use on the effects of ERTU on cardiorenal outcomes in patients with T2D and ASCVD. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Sponsored by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, and Pfizer Inc., New York, NY, USA.

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)0.013
metaresearch head score (Gemma)0.016
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 score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.063
GPT teacher head0.325
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
Published2021
Admission routes1
Has abstractyes

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