Time to Clinical Benefit of Eplerenone Among Patients with Heart Failure and Reduced Ejection Fraction: A Subgroups Analysis from the EMPHASIS-HF Trial
Bibliographic record
Abstract
AIM: Eplerenone reduces the risk of cardiovascular death or first hospitalization for heart failure (HF) in patients with HF and a reduced ejection fraction (HFrEF), but it is still frequently underused in routine practice. We evaluated the time course of benefits of eplerenone after its initiation in HFrEF patients from the EMPHASIS-HF trial. METHODS AND RESULTS: The EMPHASIS-HF trial was a double-blind randomized clinical trial assessing the effect of eplerenone in patients (n = 2737, mean age 68.6 ± 7.6 years, 22.3% women) with HFrEF and mild symptoms. The time trajectories for the effect of eplerenone versus placebo on the primary composite endpoint (cardiovascular death or first hospitalization for HF) were investigated using Cox proportional hazards models with truncated data at each day post-randomization. A significant reduction in the primary composite endpoint was observed 26 days after randomization (hazard ratio 0.58; 95% confidence interval, 0.34-1.00, p = 0.049). Eplerenone was first associated with a significant reduction in the primary endpoint in 35 days or less in most subgroups, including patients with HF history ≥18 months (day 24), estimated glomerular filtration rate <60 ml/min (day 12), ischaemic HF aetiology (day 28), age ≥65 years (day 28), narrow QRS (day 30), higher MAGGIC score (day 35), lower potassium (day 30), left ventricular ejection fraction ≥30% (day 28) or already treated with beta-blockers (day 25). CONCLUSIONS: Eplerenone provides statistically significant and clinically meaningful benefits shortly after treatment initiation in most patients, irrespective of clinical profile. This result reinforces the need for an early initiation of eplerenone in HFrEF, as part of rapidly instituting guideline-directed medical therapy.
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 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".