Impact of Empagliflozin in Heart Failure With Reduced Ejection Fraction in Patients With Ischemic Versus Nonischemic Cause
Bibliographic record
Abstract
Background Outcomes and treatment effects of therapy may vary according to the cause of heart failure (HF). Methods and Results In this post hoc analysis of the EMPEROR‐Reduced (Empagliflozin Outcome Trial in Patients With Chronic Heart Failure With Reduced Ejection Fraction) trial, the effect of empagliflozin on cardiovascular and renal outcomes was assessed according to the cause of HF. The cause of HF was investigator reported and stratified as ischemic or nonischemic. Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% CIs. Of the 3730 patients enrolled, 1929 (51.7%) had ischemic cause. In the placebo arm, patients with ischemic cause of HF did not have a significantly higher risk of cardiovascular mortality (HR, 1.21 [95% CI, 0.90–1.63]) and hospitalization for HF (HR, 0.90 [95% CI, 0.72–1.12]) compared with nonischemic cause. Empagliflozin compared with placebo significantly reduced the risk of cardiovascular death or hospitalization for HF in patients with ischemic and nonischemic cause (HR, 0.82 [95% CI, 0.68–0.99] for ischemic and HR, 0.67 [95% CI, 0.55–0.82] for nonischemic cause; P interaction=0.15). The benefit of empagliflozin on HF hospitalization, the renal composite end point, estimated glomerular filtration slope changes, and health status scores were also consistent in both groups without treatment by cause modification. Conclusions Empagliflozin offers cardiovascular and renal benefits in patients with heart failure with reduced ejection fraction regardless of the cause of HF. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03057977.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".