Influence of cardiovascular drugs on the efficacy and safety of dapagliflozin in patients with type 2 diabetes mellitus in the DECLARE-TIMI 58 trial
Bibliographic record
Abstract
Abstract Background In DECLARE-TIMI 58, the sodium glucose co-transporter 2 inhibitor (SGLT2i) dapagliflozin reduced the risk of the composite of cardiovascular (CV) death or hospitalization for heart failure (HHF) in a broad range of patients with type 2 diabetes mellitus (T2DM). SGLT2i are known to have diuretic and anti-hypertensive effects. However, whether concomitant CV drugs influence the efficacy and safety of dapagliflozin in these populations is less well known. Purpose We examined whether dapagliflozin consistently reduced the risk of CV outcomes and whether the safety of dapagliflozin was similar with or without the concurrent use of various CV drugs. Methods DECLARE–TIMI 58 was a randomized trial of dapagliflozin versus placebo in patients with T2DM and either atherosclerotic cardiovascular disease (ASCVD) or multiple risk factors for CV disease followed for a median of 4.2 years. We stratified patients by the use of CV drugs at baseline commonly used for heart failure: angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers (ACEi/ARB), beta-blockers, diuretics, and mineralocorticoid receptor antagonists (MRA). Efficacy outcomes of interest were the composite of CV death/HHF and HHF alone. We used the Cox proportional-hazard model for these analyses. Results Of 17,160 patients, 13,950 (81%) used ACEi/ARB, 9,030 (53%) used beta-blockers, 6,967 (41%) used diuretics, and 762 (4%) used MRA at baseline. All were balanced by randomized treatment groups. Patients using CV drugs at baseline had a greater prevalence of atherosclerotic risk factors and established CV disease than those without. Dapagliflozin consistently reduced the risk of CV death/HHF regardless of the use of CV medications (Figure). For HHF alone, similar results were seen with no significant interactions for any of the classes. There were no significant treatment interactions by the concomitant use of any of CV drugs for adverse events including symptoms of volume depletion or acute kidney injury. Conclusions In this analysis from the DECLARE–TIMI 58 trial, dapagliflozin consistently reduced the risk of CV death/HHF and HHF alone irrespective of the concurrent use of various CV drugs without any treatment interaction for key safety events. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): The DECLARE–TIMI 58 trial was supported by AstraZeneca.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".