Impact of polyvascular disease with and without co‐existent kidney dysfunction on cardiovascular outcomes in diabetes: A post hoc analysis of <scp>EMPA‐REG OUTCOME</scp>
Bibliographic record
Abstract
Abstract Aim To determine the relationship between polyvascular disease and risk of hospitalization for heart failure (HHF) and cardiovascular (CV) death in the EMPA‐REG OUTCOME population, and the relationship of kidney dysfunction co‐existent with polyvascular disease on CV/heart failure (HF) outcomes. Materials and Methods Patients with type 2 diabetes and atherosclerotic CV (ASCVD) received empagliflozin 10, 25 mg or placebo. Post hoc, subgroups were analyzed by one versus two or more vascular beds, and the estimated glomerular filtration rate ([eGFR] < vs. ≥60 mL/min/1.73 m 2 ) at baseline. The empagliflozin arms were pooled. Time to CV death, HHF, CV death (excluding fatal stroke) or HHF, all‐cause mortality (ACM) and 3‐point major adverse CV events (3P‐MACE) were assessed using multivariable Cox regression models. Results Baseline characteristics (N = 6959) within subgroups were balanced between treatment groups. In the placebo group, two or more versus one vascular bed increased HHF risk (1.59 [95% confidence interval 1.02, 2.49]), CV death (2.17 [1.52, 3.09]), CV death/HHF (1.79 [1.32, 2.43]), ACM (1.95 [1.44, 2.64]) and 3P‐MACE (1.76 [1.36, 2.27]). Hazard ratios for those with polyvascular disease/kidney dysfunction (vs. 1 vascular bed/eGFR ≥60 mL/min/1.73 m 2 ) were HHF 2.80 (1.46, 5.36), CV death 3.10 (1.87, 5.13), CV death/HHF 2.71 (1.74, 4.23), ACM 2.59 (1.67, 4.02) and 3P‐MACE 2.62 (1.82, 3.77). Empagliflozin reduced the risk of all outcomes across subgroups. Conclusions Polyvascular disease with/without kidney dysfunction markedly increases the risk of HF/CV events. Empagliflozin consistently reduces risk, regardless of vascular bed and kidney function status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| 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) | 0.002 | 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".