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Record W3035357525 · doi:10.2337/db20-28-or

28-OR: How Early after Treatment Initiation Are the CV Benefits of Empagliflozin Apparent? A Post Hoc Analysis of EMPA-REG OUTCOME

2020· article· en· W3035357525 on OpenAlexaffabout
Subodh Verma, Lawrence A. Leiter, Abhinav Sharma, Bernard Zinman, Michaela Mattheus, David Fitchett, Jyothis T. George, Anne Pernille Ofstad, Christoph Wanner, Silvio E. Inzucchi

Bibliographic record

VenueDiabetes · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsBoehringer Ingelheim (Canada)
Fundersnot available
KeywordsEmpagliflozinMedicineHazard ratioPlaceboInternal medicinePost-hoc analysisType 2 diabetesEMPAProportional hazards modelCumulative incidenceDiabetes mellitusEndocrinologyConfidence intervalPathologyCohort

Abstract

fetched live from OpenAlex

In the EMPA-REG OUTCOME trial, in patients with type 2 diabetes (T2D) with established atherosclerotic cardiovascular disease (ASCVD), empagliflozin reduced the risk of hospitalization for HF (HHF) by 35% (HR [95%CI] 0.65 [0.50-0.85]), CV death/HHF by 34% (0.66 [0.55-0.79]), and CV death by 38% (0.62 [0.49-0.77]) with an early separation of the cumulative incidence curves. We aimed to explore at what time point after randomization the benefits became apparent. Overall, 7020 patients were treated with empagliflozin 10, 25 mg or placebo. We expressed time trajectories for the effect of pooled empagliflozin doses vs. placebo on HHF, CV death/HHF and CV death based on Hazard Ratios (HRs) (95% CI), and calculated the HR on the day the effect reached significance using Cox proportional hazards models. The reduction in risk with empagliflozin vs. placebo reached significance at day 17 for HHF [0.10 [0.01, 0.87), p=0.0372], and day 27 for CV death/HHF [HR 0.28 (0.08, 0.97), p=0.0445], and sustained significant throughout follow-up (Figure). The benefit on CV death reached significance for the first time at day 59 [HR 0.28 (0.08, 0.96)]. HRs stabilized as the number of patients with events increased over time. The benefit of empagliflozin in reducing the risk of HHF, CV death/HHF and CV death emerged within weeks after treatment initiation in EMPA-REG OUTCOME. The earliest effect appears to be on HHF. Disclosure S. Verma: Advisory Panel; Self; Amgen, AstraZeneca, Bayer AG, Boehringer Ingelheim (Canada) Ltd., Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Research Support; Self; Amgen, AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb, Janssen Pharmaceuticals, Inc., Merck & Co., Inc. Other Relationship; Self; AstraZeneca, AstraZeneca, Bayer AG, Boehringer Ingelheim (Canada) Ltd., Boehringer Ingelheim International GmbH, Eli Lilly and Company, Eli Lilly and Company, EOCI Pharmacomm, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Canada Inc., Novo Nordisk A/S, Novo Nordisk A/S, Sanofi, Sanofi, Sun Pharmaceuticals, Toronto Knowledge Translation Working Group. L.A. Leiter: Advisory Panel; Self; Abbott, Amgen, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Boehringer Ingelheim International GmbH, Eli Lilly and Company, HLS Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi, Servier. Research Support; Self; Amgen, AstraZeneca, Kowa Pharmaceuticals America, Inc., Medicines Company. Speaker’s Bureau; Self; Amgen, AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, HLS Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Medscape, Merck & Co., Inc., Novo Nordisk Inc., Sanofi, Servier. A. Sharma: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Roche Pharma. Research Support; Self; Bristol-Myers Squibb, Merck & Co., Inc. Speaker’s Bureau; Self; Novartis Pharmaceuticals Corporation. 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. M. Mattheus: None. D.H. Fitchett: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH. Speaker’s Bureau; Self; Lilly Diabetes. Other Relationship; Self; Novo Nordisk Inc. J.T. George: Employee; Self; Boehringer Ingelheim International GmbH. A. Ofstad: Employee; Self; Boehringer Ingelheim International GmbH. 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. 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. Funding Boehringer Ingelheim and Eli Lilly and Company Diabetes Alliance

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)0.007
metaresearch head score (Gemma)0.006
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.038
GPT teacher head0.262
Teacher spread0.225 · 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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Citations3
Published2020
Admission routes2
Has abstractyes

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