Empagliflozin in Acute Myocardial Infarction in Patients with and Without Type 2 Diabetes: A Pre-Specified Analysis of the EMPACT-MI Trial
Bibliographic record
Abstract
AIMS: In the EMPACT-MI trial, empagliflozin reduced heart failure (HF) hospitalizations but not mortality in acute myocardial infarction (MI). Contemporary reports of clinical event rates with and without type 2 diabetes mellitus (T2DM) in acute MI trials are sparse. The treatment effect of empagliflozin in those with and without T2DM in acute MI is unknown. METHODS AND RESULTS: A total of 6522 patients with acute MI with newly reduced left ventricular ejection fraction (LVEF) to <45%, congestion, or both, were randomized to empagliflozin 10 mg or placebo. The primary endpoint was time to first HF hospitalization or all-cause death. Rates of endpoints with and without T2DM and the efficacy and safety of empagliflozin according to T2DM status were assessed. Overall, 32% had T2DM; 14% had pre-diabetes; 16% were normoglycaemic; 38% had unknown glycaemic status. Patients with T2DM, compared to those without T2DM, were at higher risk of time to first HF hospitalization or all-cause death (hazard ratio [HR] 1.44; 95% confidence interval [CI] 1.06-1.95) and all-cause death (HR 1.70; 95% CI 1.13-2.56). T2DM did not confer a higher risk of first HF hospitalization (HR 1.22, 95% CI 0.82-1.83). Empagliflozin reduced first and total HF hospitalizations, but not all-cause mortality, regardless of presence or absence of T2DM. The safety profile of empagliflozin was the same with and without T2DM. CONCLUSION: Patients with acute MI, LVEF <45% and/or congestion who had T2DM were at a higher risk of mortality than those without T2DM. Empagliflozin reduced first and total HF hospitalizations regardless of the presence or absence of T2DM.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".