Dapagliflozin and atrial fibrillation in heart failure with reduced ejection fraction: insights from <scp>DAPA‐HF</scp>
Bibliographic record
Abstract
AIMS: Among patients with heart failure (HF) and reduced ejection fraction (HFrEF), those with atrial fibrillation (AF) may respond differently to certain treatments than patients without AF. We investigated the efficacy and safety of dapagliflozin in patients with HFrEF with and without AF in the Dapagliflozin And Prevention of Adverse-outcomes in Heart Failure trial (DAPA-HF). We also examined the effect of dapagliflozin on new-onset AF. METHODS AND RESULTS: The primary outcome was the composite of an episode of worsening HF (HF hospitalization or urgent HF visit requiring intravenous therapy) or cardiovascular death. Of the 4744 patients randomized, 1910 (40.3%) had 'any AF' (history of AF or AF on enrolment electrocardiogram). Compared with placebo, dapagliflozin reduced the risk of worsening HF or cardiovascular death to a similar extent in patients with and without any AF [hazard ratio (HR) 0.75, 95% confidence interval (CI) 0.62-0.92 and 0.74, 95% CI 0.62-0.88, respectively; p for interaction = 0.88]. Consistent benefits were observed for the components of the primary outcome, all-cause mortality, and improvement of Kansas City Cardiomyopathy Questionnaire total symptom score. Among patients without AF at baseline, dapagliflozin did not significantly reduce the risk of new-onset AF compared with placebo (HR 0.86, 95% CI 0.60-1.22). However, patients with new-onset AF had a 5 to 6-fold higher risk of adverse outcomes when compared to those without incident AF. CONCLUSIONS: Dapagliflozin, compared with placebo, reduced the risk of worsening HF events, cardiovascular death, and all-cause death, and improved symptoms, in patients with and without AF. Dapagliflozin did not reduce the risk of new-onset AF.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".