Dapagliflozin in heart failure with improved ejection fraction: a prespecified analysis of the DELIVER trial
Bibliographic record
Abstract
With modern treatments for heart failure with reduced ejection fraction (EF), indicative of impaired cardiac systolic function, patients may exhibit an increase in EF. Limited data are available regarding the clinical management of this growing population, categorized as heart failure with improved EF (HFimpEF), which has a high event rate and has been excluded from virtually all prior heart failure outcomes trials. In a prespecified analysis of the DELIVER trial ( NCT03619213 ), of a total of 6,263 participants with symptomatic heart failure and a left ventricular EF >40%, 1,151 (18%) had HFimpEF, defined as patients whose EF improved from ≤40% to >40%. Participants were randomized to 10 mg dapagliflozin or placebo daily and the primary outcome of the trial was a composite of cardiovascular death or worsening heart failure (heart failure hospitalization or an urgent heart failure visit). Participants with HFimpEF had similar event rates to those with an EF consistently >40%. In participants with HFimpEF, dapagliflozin reduced the primary composite outcome (hazard ratio (HR) = 0.74, 95% confidence interval (CI) = 0.56-0.97), first worsening heart failure events (HR = 0.78, 95% CI = 0.61-1.14), cardiovascular death (HR = 0.62, 95% CI = 0.41-0.96) and total worsening heart failure events (rate ratio = 0.68, 95% CI = 0.50-0.94) to a similar extent as for individuals with an EF consistently >40%. These data suggest that patients with HFimpEF who are symptomatic may benefit from the addition of a sodium/glucose cotransporter 2 inhibitor to previously instituted guideline-directed medical therapy to further reduce morbidity and mortality.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".