Efficacy of Empagliflozin on Heart Failure and Renal Outcomes in Patients with Atrial Fibrillation: Data from the EMPA-REG OUTCOME Trial
Bibliographic record
Abstract
Abstract Aims Atrial fibrillation (AF) is common in patients with diabetes and heart failure (HF) and increases the future risk of adverse cardiovascular (CV) outcomes. This analysis from the EMPA-REG OUTCOME trial explores CV and renal outcomes in patients with vs. without AF at baseline and assesses the benefits of empagliflozin. Methods and results Analyses were conducted on patients distinguished by the presence (n = 389) or absence (n = 6631) of AF at baseline. Outcome events were more frequent in patients with AF than those without AF. Empagliflozin compared to placebo reduced CV death or HF hospitalisation consistently in patients with AF [hazard ratio (HR) 0.58, 95% confidence interval (CI) 0.36–0.92] and without AF (HR 0.67, 95% CI 0.55–0.82, Pinteraction = 0.56). Similar results were observed for the components of this endpoint, all-cause mortality, new or worsening nephropathy, first introduction of loop diuretics, or occurrence of oedema. The absolute number of prevented events was higher in patients with AF, resulting in larger absolute treatment effects of empagliflozin. New loop diuretics or oedema were associated with increased rates of subsequent events, and rates appeared lower in those randomised to empagliflozin. Conclusions In patients with type 2 diabetes mellitus and established CV disease, those with AF at baseline had higher rates of adverse HF outcomes than those without AF. Irrespective of the presence of AF, empagliflozin reduced HF-related and renal events. The absolute number of prevented events is higher in patients with AF than without AF. Patients with diabetes, CV disease and AF may especially benefit from use of empagliflozin.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".