193Qualifying event proximity, cardiovascular risk, and benefit of empagliflozin in patients with type 2 diabetes and stable atherosclerosis in the EMPA-REG OUTCOME trial
Bibliographic record
Abstract
Abstract Background In type 2 diabetes, the temporal proximity of an atherosclerotic cardiovascular (CV) event can impact prognosis, but whether timing influences sodium glucose co-transporter 2 inhibitor effects is unknown. We explored the association of time from last qualifying CV event before randomisation (myocardial infarction [MI], stroke, coronary artery disease or peripheral arterial disease) with CV outcomes and benefit of empagliflozin (EMPA) in EMPA-REG OUTCOME. Methods Patients (pts) were randomised to EMPA 10 mg, 25 mg or placebo and followed for 3.1 years (median). Risk of major adverse CV events (3P MACE: CV death, MI, stroke), CV death or hospitalisation for heart failure (HHF) were evaluated using Cox regression in subgroups of ≤1/>1 year since last qualifying CV event. Qualifying event stratification was possible in 6796 (97%) pts. Results In the overall population, N=6796 (4547 EMPA and 2249 placebo pts), median (Q1, Q3) time from last CV event was 3.8 (1.5–7.6) years. Overall, 1214 (EMPA 841; placebo 373) and 5582 (EMPA 3706; placebo 1876) pts had a last qualifying CV event ≤1 and >1 year, respectively. Pts with more recent events had similar risk for CV outcomes compared with pts >1 year from qualifying event (Figure). Moreover, the benefit of EMPA on CV outcomes was consistent between pts enrolled ≤1 or >1 year from the qualifying CV event (all p-interaction >0.05; Figure). Conclusion Although most pts had a qualifying CV event >1 year before randomisation in EMPA-REG OUTCOME, the benefits of EMPA appear to extend to pts with more recent CV events. Acknowledgement/Funding Boehringer Ingelheim & Eli Lilly and Company Diabetes Alliance
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".