The Comparison of the Effectiveness of Dapagliflozin and Empagliflozin in the Prevention of Cardiovascular Outcomes in Patients With Type 2 Diabetes: A Network Meta-Analysis
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) is a significant risk factor for cardiovascular diseases, prompting research into treatments that can mitigate this risk. Sodium-glucose cotransporter 2 (SGLT2) inhibitors, particularly dapagliflozin and empagliflozin, have shown promising cardiovascular benefits in T2DM patients. This meta-analysis aimed to directly compare the cardiovascular outcomes of these two drugs. To achieve this, we conducted a comprehensive literature search across multiple databases up to August 5, 2024, including both randomized controlled trials (RCTs) and observational studies. The primary outcomes of interest were major adverse cardiovascular events (MACE), atrial fibrillation (AF), cardiovascular mortality, myocardial infarction (MI), and hospitalization for heart failure (HF). Twelve studies met the inclusion criteria for this meta-analysis. The pooled analysis revealed several key findings. Notably, dapagliflozin demonstrated superior efficacy in preventing atrial fibrillation compared to empagliflozin. However, no significant differences were observed between the two drugs in terms of MACE, cardiovascular mortality, hospitalization for heart failure (HHF), or myocardial infarction. When compared to placebo, both dapagliflozin and empagliflozin showed greater effectiveness in preventing adverse cardiovascular outcomes in T2DM patients. These results reinforce the cardiovascular benefits of both dapagliflozin and empagliflozin in patients with T2DM. The comparable efficacy in most outcomes suggests that clinicians have flexibility in prescribing either of these SGLT2 inhibitors. However, the lower risk of atrial fibrillation associated with dapagliflozin may be a crucial factor in treatment decisions, especially for patients with a history of or at high risk for atrial fibrillation.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.060 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".