Rationale, design, and baseline characteristics of a randomized, placebo-controlled cardiovascular outcome trial of empagliflozin (EMPA-REG OUTCOME™)
Bibliographic record
Abstract
BACKGROUND: Evidence concerning the importance of glucose lowering in the prevention of cardiovascular (CV) outcomes remains controversial. Given the multi-faceted pathogenesis of atherosclerosis in diabetes, it is likely that any intervention to mitigate this risk must address CV risk factors beyond glycemia alone. The SGLT-2 inhibitor empagliflozin improves glucose control, body weight and blood pressure when used as monotherapy or add-on to other antihyperglycemic agents in patients with type 2 diabetes. The aim of the ongoing EMPA-REG OUTCOME™ trial is to determine the long-term CV safety of empagliflozin, as well as investigating potential benefits on macro-/microvascular outcomes. METHODS: Patients who were drug-naïve (HbA1c ≥7.0% and ≤9.0%), or on background glucose-lowering therapy (HbA1c ≥7.0% and ≤10.0%), and were at high risk of CV events, were randomized (1:1:1) and treated with empagliflozin 10 mg, empagliflozin 25 mg, or placebo (double blind, double dummy) superimposed upon the standard of care. The primary outcome is time to first occurrence of CV death, non-fatal myocardial infarction, or non-fatal stroke. CV events will be prospectively adjudicated by an independent Clinical Events Committee. The trial will continue until ≥691 confirmed primary outcome events have occurred, providing a power of 90% to yield an upper limit of the adjusted 95% CI for a hazard ratio of <1.3 with a one-sided α of 0.025, assuming equal risks between placebo and empagliflozin (both doses pooled). Hierarchical testing for superiority will follow for the primary outcome and key secondary outcomes (time to first occurrence of CV death, non-fatal myocardial infarction, non-fatal stroke or hospitalization for unstable angina pectoris) where non-inferiority is achieved. RESULTS: Between Sept 2010 and April 2013, 592 clinical sites randomized and treated 7034 patients (41% from Europe, 20% from North America, and 19% from Asia). At baseline, the mean age was 63 ± 9 years, BMI 30.6 ± 5.3 kg/m2, HbA1c 8.1 ± 0.8%, and eGFR 74 ± 21 ml/min/1.73 m2. The study is expected to report in 2015. DISCUSSION: EMPA-REG OUTCOME™ will determine the CV safety of empagliflozin in a cohort of patients with type 2 diabetes and high CV risk, with the potential to show cardioprotection. TRIAL REGISTRATION: Clinicaltrials.gov NCT01131676.
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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 | 0.048 | 0.033 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".