Dulaglutide and cardiovascular and heart failure outcomes in patients with and without heart failure: a post‐hoc analysis from the REWIND randomized trial
Bibliographic record
Abstract
AIMS: People with diabetes are at high risk for cardiovascular events including heart failure (HF). We examined the effect of the glucagon-like peptide 1 agonist dulaglutide on incident HF events and other cardiovascular outcomes in those with or without prior HF in the randomized placebo-controlled Researching Cardiovascular Events with a Weekly Incretin in Diabetes (REWIND) trial. METHODS AND RESULTS: The REWIND major adverse cardiovascular event (MACE) outcome was the first occurrence of a composite endpoint of non-fatal myocardial infarction, non-fatal stroke, or death from cardiovascular causes (including unknown causes). In this post-hoc analysis, a HF event was defined as an adjudication-confirmed hospitalization or urgent evaluation for HF. Of the 9901 participants studied over a median follow-up of 5.4 years, 213/4949 (4.3%) randomly assigned to dulaglutide and 226/4952 (4.6%) participants assigned to placebo experienced a HF event (hazard ratio [HR] 0.93, 95% confidence interval [CI] 0.77-1.12; p = 0.456). In the 853 (8.6%) participants with HF at baseline, there was no change in either MACE or HF events with dulaglutide as compared to participants without HF (p = 0.44 and 0.19 for interaction, respectively). Combined cardiovascular death and HF events were marginally reduced with dulaglutide compared to placebo (HR 0.88, 95% CI 0.78-1.00; p = 0.050) but unchanged in patients with and without HF at baseline (p = 0.31). CONCLUSIONS: Dulaglutide was not associated with a reduction in HF events in patients with type 2 diabetes regardless of baseline HF status over 5.4 years of follow-up.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".