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Record W3100058299 · doi:10.1093/eurheartj/ehaa777

Similar cardiovascular outcomes in patients with diabetes and established or high risk for coronary vascular disease treated with dulaglutide with and without baseline metformin

2020· article· en· W3100058299 on OpenAlexaff
Giulia Ferrannini, Hertzel C. Gerstein, Helen M. Colhoun, Gilles R. Dagenais, Rafael Díaz, Leanne Dyal, Mark Lakshmanan, Linda Mellbin, Jeffrey L. Probstfield, Matthew C. Riddle, Jonathan E. Shaw, Álvaro Avezum, Jan Basile, William C. Cushman, Petr Janský, Mátyás Keltai, Fernando Laņas, Lawrence A. Leiter, Patricio López‐Jaramillo, Prem Pais, Valdis Pīrāgs, Nana Pogosova, Peter Raubenheimer, Wayne Huey‐Herng Sheu, Lars Rydén

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's HospitalInstitut universitaire de cardiologie et de pneumologie de QuébecPopulation Health Research InstituteUniversité LavalUniversity of TorontoHamilton Health SciencesMcMaster University
FundersAstraZenecaEli Lilly and Company
KeywordsMedicineDulaglutideMetforminHazard ratioInternal medicineType 2 diabetesMyocardial infarctionDiabetes mellitusProportional hazards modelSitagliptinStroke (engine)Clinical endpointCardiologyConfidence intervalRandomized controlled trialInsulinEndocrinologyLiraglutide

Abstract

fetched live from OpenAlex

OBJECTIVE: Recent European Guidelines for Diabetes, Prediabetes and Cardiovascular Diseases introduced a shift in managing patients with type 2 diabetes at high risk for or established cardiovascular (CV) disease by recommending GLP-1 receptor agonists and SGLT-2 inhibitors as initial glucose-lowering therapy. This is questioned since outcome trials of these drug classes had metformin as background therapy. In this post hoc analysis, the effect of dulaglutide on CV events was investigated according to the baseline metformin therapy by means of a subgroup analysis of the Researching Cardiovascular Events with a Weekly Incretin in Diabetes (REWIND) trial. RESEARCH DESIGN AND METHODS: Patients in REWIND (n = 9901; women: 46.3%; mean age: 66.2 years) had type 2 diabetes and either a previous CV event (31%) or high CV risk (69%). They were randomized (1:1) to sc. dulaglutide (1.5 mg/weekly) or placebo in addition to standard of care. The primary outcome was the first of a composite of nonfatal myocardial infarction, nonfatal stroke, and death from cardiovascular or unknown causes. Key secondary outcomes included a microvascular composite endpoint, all-cause death, and heart failure. The effect of dulaglutide in patients with and without baseline metformin was evaluated by a Cox regression hazard model with baseline metformin, dulaglutide assignment, and their interaction as independent variables. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by a Cox regression model with adjustments for factors differing at baseline between people with vs. without metformin, identified using the backward selection. RESULTS: Compared to patients with metformin at baseline (n = 8037; 81%), those without metformin (n = 1864; 19%) were older and slightly less obese and had higher proportions of women, prior CV events, heart failure, and renal disease. The primary outcome occurred in 976 (12%) participants with baseline metformin and in 281 (15%) without. There was no significant difference in the effect of dulaglutide on the primary outcome in patients with vs. without metformin at baseline [HR 0.92 (CI 0.81-1.05) vs. 0.78 (CI 0.61-0.99); interaction P = 0.18]. Findings for key secondary outcomes were similar in patients with and without baseline metformin. CONCLUSION: This analysis suggests that the cardioprotective effect of dulaglutide is unaffected by the baseline use of metformin therapy.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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 designRandomized trial
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".

Quick stats

Citations27
Published2020
Admission routes1
Has abstractyes

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