Cardiovascular Benefit of Empagliflozin Across the Spectrum of Cardiovascular Risk Factor Control in the EMPA-REG OUTCOME Trial
Bibliographic record
Abstract
CONTEXT: Control of multiple cardiovascular (CV) risk factors reduces CV events in individuals with type 2 diabetes. OBJECTIVE: To investigate this association in a contemporary clinical trial population, including how CV risk factor control affects the CV benefits of empagliflozin, a sodium-glucose cotransporter-2 inhibitor. DESIGN: Post hoc analysis. SETTING: Randomized CV outcome trial (EMPA-REG OUTCOME). PARTICIPANTS: Type 2 diabetes patients with established CV disease. INTERVENTION: Empagliflozin or placebo. MAIN OUTCOME MEASURES: Risk of CV outcomes-including the treatment effect of empagliflozin-by achieving 7 goals for CV risk factor control at baseline: (1) glycated hemoglobin <7.5%, (2) low-density lipoprotein cholesterol <100 mg/dL or statin use, (3) systolic blood pressure <140 mmHg and diastolic blood pressure <90 mmHg, (4) pharmacological renin-angiotensin-aldosterone system blockade, (5) normoalbuminuria, (6) aspirin use, (7) nonsmoking. RESULTS: In the placebo group, the hazard ratio (HR) for CV death was 4.00 (95% CI, 2.26-7.11) and 2.48 (95% CI, 1.52-4.06) for patients achieving only 0-3 or 4-5 risk factor goals at baseline, respectively, compared with those achieving 6-7 goals. Participants achieving 0-3 or 4-5 goals also had increased risk for the composite outcome of hospitalization for heart failure or CV death (excluding fatal stroke) (HR 2.89 [1.82-4.57] and 1.90 [1.31-2.78], respectively) and 3-point major adverse CV events (HR 2.21 [1.53-3.19] and 1.42 [1.06-1.89]). Empagliflozin significantly reduced these outcomes across all risk factor control categories (P > 0.05 for treatment-by-subgroup interactions). CONCLUSIONS: Cardiovascular risk in EMPA-REG OUTCOME was inversely associated with baseline CV risk factor control. Empagliflozin's cardioprotective effect was consistent regardless of multiple baseline risk factor control.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".