Evaluation of the Effect of Sodium–Glucose Co-Transporter 2 Inhibition with Empagliflozin on Morbidity and Mortality of Patients with Chronic Heart Failure and a Reduced Ejection Fraction: Rationale for and Design of the EMPEROR-Reduced Trial
Bibliographic record
Abstract
Drugs that inhibit the sodium-glucose co-transporter 2 (SGLT2) have been shown to reduce the risk of hospitalizations for heart failure in patients with type 2 diabetes. In populations that largely did not have heart failure at the time of enrolment, empagliflozin, canagliflozin and dapagliflozin decreased the risk of serious new-onset heart failure events by ≈30%. In addition, in the EMPA-REG OUTCOME trial, empagliflozin reduced the risk of both pump failure and sudden deaths, the two most common modes of death among patients with heart failure. In none of the three trials could the benefits of SGLT2 inhibitors on heart failure be explained by the actions of these drugs as diuretics or anti-hyperglycaemic agents. These observations raise the possibility that SGLT2 inhibitors could reduce morbidity and mortality in patients with established heart failure, including those without diabetes. The EMPEROR-Reduced trial is enrolling ≈3600 patients with heart failure and a reduced left ventricular ejection fraction (≤ 40%), half of whom are expected not to have diabetes. Patients are being randomized to placebo or empagliflozin 10 mg daily, which is added to all appropriate treatment with inhibitors of the renin-angiotensin system and neprilysin, beta-blockers and mineralocorticoid receptor antagonists. The primary endpoint is the time-to-first event analysis of the combined risk of cardiovascular death and hospitalization for heart failure, but the trial will also evaluate the effects of empagliflozin on renal function, cardiovascular death, all-cause mortality, and recurrent hospitalization events. By adjusting eligibility based on natriuretic peptide levels to the baseline ejection fraction, the trial will preferentially enrol high-risk patients. A large proportion of the participants is expected to have an ejection fraction < 30%, and the estimated annual event rate is expected to be at least 15%. The EMPEROR-Reduced trial is well-positioned to determine if the addition of empagliflozin can add meaningfully to current approaches that have established benefits in the treatment of chronic heart failure with left ventricular systolic dysfunction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".