Time to Cardiovascular Benefits of Empagliflozin: A <i>Post Hoc</i> Observation from the EMPA-REG OUTCOME Trial
Bibliographic record
Abstract
AIMS: In the EMPA-REG OUTCOME trial, in patients with type 2 diabetes and established atherosclerotic cardiovascular (CV) disease, empagliflozin vs. placebo reduced the risk of hospitalization for heart failure (HHF) by 35%, CV death/HHF by 34%, and CV death by 38%, with an early separation of the cumulative incidence curves. We explored at what time point after randomization these benefits became apparent. METHODS AND RESULTS: We expressed time trajectories for the effect of pooled empagliflozin doses vs. placebo on HHF, CV death/HHF, and CV death based on hazard ratios (95% confidence interval) and calculated the hazard ratio on the day the effect reached significance using Cox proportional hazards models. Overall, 7020 patients aged ≥18 years were treated with empagliflozin 10 mg (N = 2345), empagliflozin 25 mg (N = 2342), or placebo (N = 2333) once daily in addition to standard of care. Mean age (years ± SD) was 63.1 ± 8.6, and 72% were male. The benefit of empagliflozin on CV death first reached statistical significance on Day 59 (HR [95% confidence interval]) (0.28 [0.08, 0.96], P = 0.0424) and was generally sustained throughout the trial (overall 0.62 [0.49, 0.77], P < 0.0001). Risk reduction with empagliflozin on HHF reached statistical significance on Day 17 (0.10 [0.01, 0.87], P = 0.0372) and was sustained throughout the study (overall 0.65 [0.50, 0.85], P = 0.0017). For the composite outcome of CV death or HHF, risk reduction with empagliflozin reached statistical significance on Day 27 (0.28 [0.08, 0.97], P = 0.0445) and was sustained throughout follow-up (overall 0.66 [0.55, 0.79], P < 0.0001). CONCLUSIONS: In EMPA-REG OUTCOME, the benefit of empagliflozin in reducing the risk of HHF, CV death/HHF, and CV death emerged within weeks after treatment initiation. The earliest benefit appears to be on HHF.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".