Efficacy and safety of empagliflozin in older patients in the EMPA-REG OUTCOME® trial
Bibliographic record
Abstract
OBJECTIVE: The risks of cardio-renal complications of diabetes increase with age. In the EMPA-REG OUTCOME® trial, empagliflozin reduced cardiovascular (CV) mortality by 38% in patients with type 2 diabetes (T2D) and CV disease. Here we compare outcomes with empagliflozin in older patients in EMPA-REG OUTCOME. METHODS: Patients with T2D and CV disease were randomised to empagliflozin 10 or 25 mg, or placebo plus standard of care. In post hoc analyses, risks of 3-point major adverse CV events (3P-MACE: composite of CV death, non-fatal myocardial infarction (MI) or non-fatal stroke), CV death, hospitalisation for heart failure, all-cause mortality, all-cause hospitalisation and incident/worsening nephropathy were evaluated for empagliflozin versus placebo by baseline age (<65, 65 to <75, ≥75 years). Adverse events (AEs) were analysed descriptively. RESULTS: Effect of empagliflozin on all outcomes was consistent across age categories (P ≥ 0.05 for interactions) except 3P-MACE. The 3P-MACE hazard ratios (HRs) were 1.04 (95% confidence interval [CI] 0.84, 1.29), 0.74 (0.58, 0.93) and 0.68 (0.46, 1.00) in patients aged <65, 65 to <75, and ≥75 years, respectively (P = 0.047 for treatment-by-age group interaction). Corresponding CV death HRs were 0.72 (95% CI 0.52, 1.01), 0.54 (0.37, 0.79) and 0.55 (0.32, 0.94), respectively (P = 0.484 for treatment-by-age group interaction). Across age categories, empagliflozin AEs reflected its known safety profile. Rates of bone fractures, renal AEs and diabetic ketoacidosis were similar between empagliflozin and placebo across age categories. CONCLUSIONS: In the EMPA-REG OUTCOME trial, empagliflozin reduced risks of CV mortality, heart failure and renal outcomes, supporting its cardio-renal benefits in older patients.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".