Abstract 14961: External Applicability of REDUCE-IT in a Large Diabetes Cardiovascular Outcomes Trial: A Post Hoc Analysis of EMPA-REG OUTCOME
Bibliographic record
Abstract
Introduction: In the REDUCE-IT trial, icosapent ethyl (IPE) was shown to reduce major adverse cardiac events (MACE) including cardiovascular (CV) death in patients with elevated triglycerides (TG) and atherosclerotic CV disease (ASCVD) and/or diabetes. There are limited data evaluating the external applicability of REDUCE-IT inclusion criteria in contemporary diabetes trials. In EMPA-REG OUTCOME, empagliflozin (EMPA) reduced the risk of CV death and hospitalization for HF (HHF). We sought to evaluate the benefit of EMPA on CV outcomes across IPE eligibility in the EMPA-REG OUTCOME trial. Methods: In total, 7020 patients with type 2 diabetes and ASCVD were treated with EMPA 10mg, 25 mg, or placebo (PBO). We examined the proportion of patients that had baseline (BL) TG 135 to 499 mg/dl, LDL-C 41 to 100 mg/dl and using a statin (“REDUCE-IT-like patients”). We evaluated the effect of pooled EMPA vs. PBO on CV death, HHF, HHF or CV death (excluding fatal stroke), all-cause death, and 3-point-MACE across the subgroups of patients fulfilling vs not fulfilling the REDUCE-IT criteria at BL using Cox regression. Results: A total of 6935 patients had TG and 6932 had LDL-C available. 1810 patients (25.8%) were REDUCE-IT like with TG 206±69 vs. 158±140 mg/dl, and LDL-C 71±15 vs. 91±39 mg/dl in those who did not meet the criteria. At BL, the former had more often coronary artery disease (84 vs. 73%) and higher BMI (31.7±5.1 kg/m 2 vs. 30.3±5.3) vs. the latter group. CV event rates in PBO were similar in patients who met/did not meet the criteria. Treatment effects of EMPA vs. PBO were consistent in patients who met/did not meet the REDUCE-IT criteria (Fig). Conclusions: In patients with T2D and established ASCVD treated in EMPA-REG OUTCOME, about one-quarter of patients would qualify for IPE according to the REDUCE-IT inclusion criteria. Empagliflozin consistently reduced CV outcomes and mortality in this sub-group, suggesting that IPE and SGLT2 inhibition may be complementary.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".