Effect of empagliflozin on total myocardial infarction events by type and additional coronary outcomes: insights from the randomized EMPA-REG OUTCOME trial
Bibliographic record
Abstract
BACKGROUND: The effect of empagliflozin, a sodium-glucose-co-transporter-2 inhibitor, on risk for myocardial infarction has not been fully characterized. METHODS: This study comprised prespecified and post-hoc analyses of the EMPA-REG OUTCOME trial in which 7020 people with type 2 diabetes (T2D) and cardiovascular disease [mostly atherosclerotic (ASCVD)] were randomized to empagliflozin or placebo and followed for a median 3.1 years. We assessed the effect of empagliflozin on total (first plus recurrent) events of centrally adjudicated fatal and non-fatal myocardial infarction (MI) using a negative binomial model with robust confidence intervals (CI) that preserves randomization and accounts for the within-patient correlation of multiple events. Post hoc, we analyzed types of MI: type 1 (related to plaque-rupture/thrombus), type 2 (myocardial supply-demand imbalance), type 3 (sudden-death related, i.e. fatal MI), type 4 (percutaneous coronary intervention-related), and type 5 (coronary artery bypass graft-related). MIs could be assigned to > 1 type. RESULTS: There were 421 total MIs (including recurrent); 299, 86, 26, 19, and 1 were classified as type 1, 2, 3, 4, and 5 events, respectively. Overall, empagliflozin reduced the risk of total MI events by 21% [rate ratio for empagliflozin vs. placebo, 0.79 (95% CI, 0.620-0.998), P = 0.0486], largely driven by its effect on type 1 [rate ratio, 0.79 (95% CI, 0.61-1.04)] and type 2 MIs [rate ratio, 0.67 (95% CI, 0.41-1.10)]. CONCLUSIONS: In T2D patients with ASCVD, empagliflozin reduced the risk of MIs, with consistent effects across the two most common etiologies, i.e. type 1 and 2. TRAIL REGISTRATION: URL: https://www. CLINICALTRIALS: gov ; Unique identifier: NCT01131676.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| 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) | 0.002 | 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".