Empagliflozin reduces the risk of mortality and hospitalization for heart failure across Thrombolysis In Myocardial Infarction Risk Score for Heart Failure in Diabetes categories: <i>Post hoc</i> analysis of the EMPA‐REG OUTCOME trial
Bibliographic record
Abstract
Abstract Aim To investigate the association of the Thrombolysis In Myocardial Infarction (TIMI) Risk Score for Heart Failure in Diabetes (TRS‐HF DM ) with mortality using data from the EMPA‐REG OUTCOME trial. Materials and Methods In EMPA‐REG OUTCOME, patients with type 2 diabetes and atherosclerotic cardiovascular (CV) disease (N = 7020) received the sodium‐glucose co‐transporter‐2 inhibitor, empagliflozin, 10 or 25 mg or placebo. Post hoc , patients were stratified into risk categories (low‐intermediate, high, very‐high risk scores) using baseline TRS‐HF DM . Cox regression analyses evaluated the association of TRS‐HF DM categories with all‐cause mortality (ACM), CV death, hospitalization for heart failure (HHF) and CV death (excluding fatal stroke) or HHF, and whether empagliflozin reduced the risk of CV outcomes across these risk categories. Results In placebo patients, increasing risk category was associated with a higher risk of ACM, CV death, and HHF. Empagliflozin reduced the risk of ACM (low‐intermediate HR 0.68 [95% CI 0.48, 0.97] and very‐high 0.69 [0.52, 0.91]), CV death (0.75 [0.48, 1.18] and 0.56 [0.41, 0.78]), HHF (0.53 [0.28, 1.01] and 0.67 [0.48, 0.96]), and CV death or HHF (0.69 [0.46, 1.03]) and (0.64 [0.49, 0.82]) across all risk categories versus placebo. Higher absolute risk reductions (ARRs) were observed for CV death in the very‐high versus low‐intermediate category ( P = 0.01). Conclusions Applied to EMPA‐REG OUTCOME, higher TRS‐HF DM was associated with increased HHF and mortality risk. Empagliflozin reduced CV outcomes across TRS‐HF DM categories. Higher ARRs were associated with higher risk scores.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".