Effect of Qualifying Atherosclerotic Cardiovascular Disease Diagnosis Proximity on Cardiovascular Risk and Benefit of Empagliflozin in the EMPA-REG OUTCOME Trial
Bibliographic record
Abstract
Background In patients with type 2 diabetes mellitus (T2DM), a history of an ischemic event is associated with increased risk for cardiovascular disease. Whether patients with T2DM and a recent atherothrombotic diagnosis benefit from early intervention with a sodium-glucose co-transporter 2 (SGLT2) inhibitor is unknown. Methods This is a secondary analysis of EMPA-REG OUTCOME, which compared empagliflozin to placebo in adults with T2DM and atherosclerotic cardiovascular disease (ASCVD). Participants were categorized based on the time since their last qualifying ASCVD diagnosis (≤1 year versus >1 year). Qualifying ASCVD diagnoses included ischemic or hemorrhagic stroke, myocardial infarction (MI), coronary artery disease, or peripheral artery disease. The primary outcome was a composite of cardiovascular death, non-fatal MI, or non-fatal stroke. Results 6,796 participants (n=4,547 empagliflozin, n=2,249 placebo) were included. Median time since the last qualifying ASCVD diagnosis was 3.8 years (Quartile1–Quartile3: 1.5–7.6), and most qualifying diagnoses occurred >1 year before randomization (≤1 year: n=1,214, >1 year n=5,582). Empagliflozin reduced the primary outcome irrespective of the time since the last qualifying ASCVD diagnosis (≤1 year: hazard ratio [HR] 0.82, 95% confidence interval [CI]: 0.57–1.16; versus >1 year : HR 0.85, 95%-CI: 0.72–1.00; p -interaction=0.84). Results were similar for the composite of cardiovascular death or hospitalization for heart failure. Conclusions Empagliflozin improved cardiovascular outcomes in participants with T2DM, irrespective of the time since the last qualifying ASCVD diagnosis at randomization. Prospective trials are necessary to investigate the use of SGLT2 inhibitors at the time of an acute ASCVD event.
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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 | 0.000 |
| 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".