Empagliflozin After Myocardial Infarction with or without diabetes and chronic kidney disease: Insights from EMPACT-MI
Bibliographic record
Abstract
BACKGROUND: In the EMPACT-MI trial, empagliflozin did not reduce the primary endpoint of all-cause mortality or hospitalization for heart failure (HHF) following acute myocardial infarction (AMI) but was associated with a risk reduction for HF events. OBJECTIVES: This study aimed to evaluate whether the effect of empagliflozin on HF events is consistent in patients with and without type 2 diabetes and/or chronic kidney disease enrolled in the EMPACT-MI trial. METHODS: Post hoc analysis assessing the effect of empagliflozin on the primary endpoint and on HF events in AMI patients with and without an established recommendation for a sodium-glucose cotransporter-2 inhibitor (SGLT2i) (type 2 diabetes or chronic kidney disease). RESULTS: Of 6522 participants, 3489 (53%) did not have type 2 diabetes and/or chronic kidney disease. Those without these conditions were younger and with fewer comorbidities. No differences were observed for the primary endpoint. Empagliflozin reduced time to first HHF, total HHF, time to adverse event (AE) of HF (including outpatient HF events) and total AEs of HF similarly in patients with and without type 2 diabetes or chronic kidney disease. Total HHFs were 50 and 63 [adjusted event rate 1.74 and 2.31 events per 100 patient-years; rate ratio (RR) 0.75; 95% confidence interval (CI) 0.48, 1.18] in patients without and 98 and 144 (adjusted event rate 3.91 and 6.04 events per 100 patient-years; RR 0.65; 95% CI 0.45, 0.94; P for interaction = 0.61) in those with type 2 diabetes or chronic kidney disease in the empagliflozin and placebo arms, respectively. Any AEs, serious AEs and AEs leading to permanent study drug discontinuation were similar between treatment groups in both subgroups. CONCLUSIONS: Empagliflozin improved HF outcomes similarly in patients after AMI with or without type 2 diabetes or chronic kidney disease.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".