Effects of dapagliflozin in DAPA-HF according to background heart failure therapy
Bibliographic record
Abstract
AIMS: In the DAPA-HF trial, the SGLT2 inhibitor dapagliflozin reduced the risk of worsening heart failure (HF) and death in patients with HF and reduced ejection fraction. We examined whether this benefit was consistent in relation to background HF therapy. METHODS AND RESULTS: In this post hoc analysis, we examined the effect of study treatment in the following yes/no subgroups: diuretic, digoxin, mineralocorticoid receptor antagonist (MRA), sacubitril/valsartan, ivabradine, implanted cardioverter-defibrillating (ICD) device, and cardiac resynchronization therapy. We also examined the effect of study drug according to angiotensin-converting enzyme inhibitor/angiotensin receptor blocker dose, beta-blocker (BB) dose, and MRA (≥50% and <50% of target dose). We analysed the primary composite endpoint of cardiovascular death or a worsening HF event. Most randomized patients (n = 4744) were treated with a diuretic (84%), renin-angiotensin system (RAS) blocker (94%), and BB (96%); 52% of those taking a BB and 38% taking a RAS blocker were treated with ≥50% of the recommended dose. Overall, the dapagliflozin vs. placebo hazard ratio (HR) was 0.74 [95% confidence interval (CI) 0.65-0.85] for the primary composite endpoint (P < 0.0001). The effect of dapagliflozin was consistent across all subgroups examined: the HR ranged from 0.57 to 0.86 for primary endpoint, with no significant randomized treatment-by-subgroup interaction. For example, the HR in patients taking a RAS blocker, BB, and MRA at baseline was 0.72 (95% CI 0.61-0.86) compared with 0.77 (95% CI 0.63-0.94) in those not on all three of these treatments (P-interaction 0.64). CONCLUSION: The benefit of dapagliflozin was consistent regardless of background therapy for HF.
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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".