Abstract 14960: Empagliflozin Reduces the Total Burden of Cardiovascular Events Including Recurrent Events in the EMPA-REG OUTCOME Trial
Bibliographic record
Abstract
Introduction: In EMPA-REG OUTCOME, empagliflozin (EMPA) reduced the risk of major adverse cardiovascular (CV) events (MACE), CV mortality and hospitalization for heart failure (HHF) in analyses of first events in patients with type 2 diabetes (T2D) and atherosclerotic CV disease (ASCVD). We assessed the effect of EMPA on the total burden of CV events. Methods: Patients were randomized to EMPA 10 mg, EMPA 25 mg, or placebo. We assessed the effects of EMPA pooled vs placebo on any (first plus recurrent) adjudicated CV event (composite of myocardial infarction (MI), stroke, coronary revascularization (CR), hospitalization for unstable angina, transient ischemic attack, HHF, and CV death) using a negative binomial model. Results: Among 7,020 treated patients (mean [SD] age 63 [9] years), there were 2,142 total adjudicated CV events, most frequently CR (585), MI (421), and HHF (321). EMPA reduced the risk of total adjudicated CV events by 24% vs placebo (event rate ratio (95% CI): 0.76 (0.67, 0.87), p<0.0001) (Figure). Risk reductions were driven predominantly by reductions in HHF (0.58 (0.42, 0.81), p=0.0012), MI (0.79 (0.620, 0.998), p=0.0486), and CV death (0.62 (0.49, 0.77), p<0.0001). The estimated number of total CV events prevented with EMPA was 414.4, and the number of patients needed to treat over 3 years to prevent one event was 10.2 (6.6, 22.7). Conclusions: EMPA produced a sizeable risk reduction in the total burden of any adjudicated CV outcome, including HHF, MI and CV death, in patients with T2D and ASCVD.
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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 | 0.001 |
| 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) | 0.004 | 0.001 |
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".