Dapagliflozin and Diuretic Use in Patients With Heart Failure and Reduced Ejection Fraction in DAPA-HF
Bibliographic record
Abstract
Background: In the DAPA-HF trial (Dapagliflozin and Prevention of Adverse-Outcomes in Heart Failure), the sodium-glucose cotransporter 2 inhibitor dapagliflozin reduced the risk of worsening heart failure and death in patients with heart failure and reduced ejection fraction. We examined the efficacy and tolerability of dapagliflozin in relation to background diuretic treatment and change in diuretic therapy after randomization to dapagliflozin or placebo. Methods: We examined the effects of study treatment in the following subgroups: no diuretic and diuretic dose equivalent to furosemide <40, 40, and >40 mg daily at baseline. We examined the primary composite end point of cardiovascular death or a worsening heart failure event and its components, all-cause death and symptoms. Results: Of 4616 analyzable patients, 736 (15.9%) were on no diuretic, 1311 (28.4%) were on <40 mg, 1365 (29.6%) were on 40 mg, and 1204 (26.1%) were taking >40 mg. Compared with placebo, dapagliflozin reduced the risk of the primary end point across each of these subgroups: hazard ratios were 0.57 (95% CI, 0.36–0.92), 0.83 (95% CI, 0.63–1.10), 0.77 (95% CI, 0.60–0.99), and 0.78 (95% CI, 0.63–0.97), respectively ( P for interaction=0.61). The hazard ratio in patients taking any diuretic was 0.78 (95% CI, 0.68–0.90). Improvements in symptoms and treatment toleration were consistent across the diuretic subgroups. Diuretic dose did not change in most patients during follow-up, and mean diuretic dose did not differ between the dapagliflozin and placebo groups after randomization. Conclusions: The efficacy and safety of dapagliflozin were consistent across the diuretic subgroups examined in DAPA-HF. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03036124.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".