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Record W4306290848 · doi:10.1093/eurheartj/ehac544.962

Efficacy of ertugliflozin on hospitalisation for heart failure across the distribution of pre-trial ejection fraction: post hoc analyses of the VERTIS CV trial

2022· article· en· W4306290848 on OpenAlexaff
Ashish Kumar Pandey, Ahmed A. Kolkailah, Francesco Cosentino, Chris Cannon, Robert Frederich, David Z.I. Cherney, Samuel Dagogo‐Jack, RE Pratley, Nilo B. Cater, Ira Gantz, James P. Mancuso, Darren K. McGuire

Bibliographic record

VenueEuropean Heart Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineHeart failureEjection fractionPost-hoc analysisPopulationPlaceboInternal medicineClinical trialPost hocDiabetes mellitusRandomized controlled trialCardiologyEndocrinology

Abstract

fetched live from OpenAlex

Abstract Introduction There is controversy over whether SGLT2 inhibitors have efficacy in high-risk patients with heart failure (HF) and ejection fraction (EF) >60% with or without type 2 diabetes (T2D). The VERTIS CV trial studied a population of patients with T2D and atherosclerotic cardiovascular disease (ASCVD), 23% of whom had a history of HF. In VERTIS CV, ertugliflozin (ERTU) significantly reduced the risk of first and total hospitalisation for heart failure (HHF) vs placebo (PBO). Whether efficacy in the VERTIS CV population is consistent across the spectrum of pre-trial EF, particularly among those with EF >60%, is unknown. Purpose These post hoc analyses explored the effects of ERTU (5 mg; 15 mg) vs PBO on time to first and total HHF among patients in VERTIS CV across the spectrum of pre-trial EF. Methods As prospectively planned, the 2 ERTU dose groups were combined for all analyses vs PBO. Treatment effects of ERTU vs PBO on the risk of first and total HHF were analysed using adjusted Cox models for first and Andersen-Gill models for total (i.e., first + recurrent) events. Data on pre-trial EF were abstracted from the medical record at trial entry. Multiplicative interaction terms (EF × treatment arm) were used to determine if the efficacy of ERTU was modified by pre-trial EF. Results In VERTIS CV, 8246 patients were randomised to ERTU 5 or 15 mg or PBO (mean follow up 3.5 years). Overall, 5006 patients had pre-trial EF data available; 959 had EF ≤45%, 2860 had EF >45–60%, and 1187 had EF >60%. In the overall population, the event rate for first HHF was lower with ERTU vs PBO (hazard ratio [HR] 0.70; 95% CI 0.54–0.90). The findings were generally consistent across pre-trial EF (P-interaction = 0.26; Figure), including patients with pre-trial EF >60% (HR 0.72; 95% CI 0.34–1.55). In the overall population, event rate for total HHF was lower with ERTU vs PBO (HR 0.70; 95% CI 0.56–0.87). A significant interaction was observed between pre-trial EF and treatment arm for the risk of total HHF events (P-interaction = 0.02), with a greater magnitude of risk reduction in patients with a low pre-trial EF (≤45%; HR 0.39; 95% CI 0.26–0.57). However, the 95% CIs for the HR for total HHF for those with EF >45–60% and >60% nearly entirely or entirely contained the 95% CI of the overall population, respectively (Figure). Conclusion In the VERTIS CV trial of patients with T2D and ASCVD, the efficacy of ERTU in preventing first HHF was generally comparable across the spectrum of pre-trial EF. The trend for greater benefit at lower EF was statistically significant for total HHF events. Findings for patients with EF >45–60% and >60% appeared quantitively consistent with the overall findings for both first and total HHF. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): This study was 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)0.015
metaresearch head score (Gemma)0.017
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 score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.010
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.041
GPT teacher head0.344
Teacher spread0.303 · 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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Citations0
Published2022
Admission routes1
Has abstractyes

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