Secondary analysis of the EMPACT-MI trial reveals cardiovascular–kidney efficacy and safety of empagliflozin after acute myocardial infarction
Bibliographic record
Abstract
Abstract Data on the cardiovascular–kidney effects and safety of empagliflozin among patients with acute myocardial infarction are limited. EMPACT-MI (Study to Evaluate the Effect of Empagliflozin on Hospitalization for Heart Failure and Mortality in Patients with Acute Myocardial Infarction) was a double-blind, multicenter clinical trial that randomized 6,522 patients with acute myocardial infarction and risk for heart failure to empagliflozin or placebo. Here we show in this secondary analysis that the mean estimated glomerular filtration rate at baseline was 76.1 ml min−1 1.73 m− 2 (s.d. = 19.9 ml min−1 1.73 m− 2), with longitudinal kidney function data available for 1,152 (17.7%) treated patients from select countries. By 24 months, compared with baseline, the estimated glomerular filtration rate was similar in the empagliflozin group but declined in the placebo group (P = 0.01). Empagliflozin reduced the total adverse events of heart failure or all-cause mortality irrespective of kidney function (P interaction = 0.30). Thirty-day adverse event rates were similar by treatment group and consistent across baseline kidney function. Empagliflozin had kidney-protective effects, reduced heart failure outcomes and was safe to initiate soon after acute myocardial infarction across baseline kidney function.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".