Cardiorenal outcomes with dapagliflozin by baseline glucose‐lowering agents: Post hoc analyses from <scp>DECLARE‐TIMI</scp> 58
Bibliographic record
Abstract
Abstract Aim To assess the associations between baseline glucose‐lowering agents (GLAs) and cardiorenal outcomes with dapagliflozin versus placebo in the DECLARE‐TIMI 58 study. Materials and methods DECLARE‐TIMI 58 assessed the cardiorenal outcomes of dapagliflozin versus placebo in patients with type 2 diabetes. This post hoc analysis elaborates the efficacy and safety outcomes by baseline GLA for treatment effect and GLA‐based treatment interaction. Results At baseline, 14 068 patients (82.0%) used metformin, 7322 (42.7%) sulphonylureas, 2888 (16.8%) dipeptidyl peptidase‐4 inhibitors, 750 (4.4%) glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) and 7013 (40.9%) insulin. Dapagliflozin reduced the composite of cardiovascular death (CVD) and hospitalization for heart failure (HHF) versus placebo regardless of baseline GLA, with greater benefit in the small group of patients with baseline use of GLP‐1 RAs (HR [95% CI] 0.37 [0.18, 0.78] vs. 0.86 [0.75, 0.98] in GLP‐1 RA users vs. non‐users, P interaction = .03). The overall HR for major adverse cardiovascular events (CVD, myocardial infarction or ischaemic stroke) was 0.93 (95% CI 0.84, 1.03) with dapagliflozin versus placebo, with no interaction by baseline GLA ( P interaction > .05). The renal‐specific outcome was reduced with dapagliflozin versus placebo in the overall cohort (HR [95%CI] 0.53[0.43‐0.66]), with no interaction by baseline GLA ( P interaction > .05). All of these outcomes were similar in those with versus those without baseline metformin use. Conclusions The effects of dapagliflozin on cardiorenal outcomes were generally consistent regardless of baseline GLA, with consistent benefits regardless of baseline metformin use. The potential clinical benefit of combining sodium‐glucose co‐transporter‐2 inhibitors with GLP‐1 RAs, given some evidence of cardiovascular risk reduction with both classes, should be explored further.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".