Efficacy of empagliflozin in heart failure with preserved versus mid-range ejection fraction: a pre-specified analysis of EMPEROR-Preserved
Bibliographic record
Abstract
Abstract The EMPEROR-Preserved trial showed that the sodium–glucose co-transporter 2 inhibitor empagliflozin significantly reduces the risk of cardiovascular death or hospitalization for heart failure (HHF) in heart failure patients with left ventricular ejection fraction (LVEF) > 40%. Here, we report the results of a pre-specified analysis that separately evaluates these patients stratified by LVEF: preserved (≥ 50%) (n = 4,005; 66.9%) or mid-range (41–49%). In patients with LVEF ≥ 50%, empagliflozin reduced the risk of cardiovascular death or HHF (the primary endpoint) by 17% versus placebo (hazard ratio (HR) 0.83; 95% confidence interval (CI): 0.71–0.98, P = 0.024). For the key secondary endpoint, the HR for total HHF was 0.83 (95%CI: 0.66–1.04, P = 0.11). For patients with an LVEF of 41–49%, the HR for empagliflozin versus placebo was 0.71 (95%CI: 0.57–0.88, P = 0.002) for the primary outcome (Pinteraction = 0.27), and 0.57 (95%CI: 0.42–0.79, P < 0.001) for total HHF (Pinteraction = 0.06). These results, together with those from the EMPEROR-Reduced trial in patients with LVEF < 40%, support the use of empagliflozin across the full spectrum of LVEF in heart failure.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".