Effects of empagliflozin on risk for cardiovascular death and heart failure hospitalization across the spectrum of heart failure risk in the EMPA-REG OUTCOME® trial
Bibliographic record
Abstract
Aims: Empagliflozin reduced the risk of cardiovascular (CV) death and heart failure (HF) hospitalizations in patients with type 2 diabetes (T2D) and established CV disease (CVD) in the EMPA-REG OUTCOME® trial. We investigated whether the benefit of empagliflozin was observed across the spectrum of HF risk. Methods and results: Seven thousand and twenty patients with T2D (HbA1c 7-10% and eGFR > 30 mL/min/1.73 m2) were treated with empagliflozin 10 or 25 mg, or placebo once daily and followed for median 3.1 years. In patients without HF at baseline (89.9%), we derived the 5-year risk for incident HF using the 9-variable Health ABC HF Risk score [classified as low-to-average (<10%), high (10-20%), and very high (≥ 20%)]. Overall, 67.2% of the population had low-to-average, 24.2% high, and 5.1% very high 5-year HF risk. Across these groups, the effect on CV death and HF hospitalization with empagliflozin was consistent [hazard ratio 0.71 (95% confidence interval: 0.52, 0.96), 0.52 (0.36, 0.75), and 0.55 (0.30, 1.00), respectively]. Effects on CV death in the ostensibly highest HF risk group (HF at baseline and/or incident HF during the trial) in whom 37.9% of the overall CV deaths occurred, was also beneficial [0.67 (0.47, 0.97)], yet, similar benefits were seen in the lower risk patients. Conclusion: In patients with T2D and established CVD, a sizeable proportion without HF at baseline are at high or very high risk for HF outcomes, indicating the need for active case finding in this patient population. Empagliflozin consistently improved HF outcomes both in patients at low or high HF risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".