Empagliflozin in Heart Failure With Predicted Preserved Versus Reduced Ejection Fraction: Data From the EMPA-REG OUTCOME Trial
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Bibliographic record
Abstract
BACKGROUND: In the EMPA-REG OUTCOME trial, ejection fraction (EF) data were not collected. In the subpopulation with heart failure (HF), we applied a new predictive model for EF to determine the effects of empagliflozin in HF with predicted reduced (HFrEF) vs preserved (HFpEF) EF vs no HF. METHODS AND RESULTS: We applied a validated EF predictive model based on patient baseline characteristics and treatments to categorize patients with HF as being likely to have HF with mid-range EF (HFmrEF)/HFrEF (EF <50%) or HFpEF (EF ≥50%). Cox regression was used to assess the effect of empagliflozin vs placebo on cardiovascular death/HF hospitalization (HHF), cardiovascular and all-cause mortality, and HHF in patients with predicted HFpEF, HFmrEF/HFrEF and no HF. Of 7001 EMPA-REG OUTCOME patients with data available for this analysis, 6314 (90%) had no history of HF. Of the 687 with history of HF, 479 (69.7%) were predicted to have HFmrEF/HFrEF and 208 (30.3%) to have HFpEF. Empagliflozin's treatment effect was consistent in predicted HFpEF, HFmrEF/HFrEF and no-HF for each outcome (HR [95% CI] for the primary outcome 0.60 [0.31-1.17], 0.79 [0.51-1.23], and 0.63 [0.50-0.78], respectively; P interaction = 0.62). CONCLUSIONS: In EMPA-REG OUTCOME, one-third of the patients with HF had predicted HFpEF. The benefits of empagliflozin on HF and mortality outcomes were consistent in nonHF, predicted HFpEF and HFmrEF/HFrEF.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it