SO019EFFECTS OF EMPAGLIFLOZIN ON CARDIOVASCULAR OUTCOMES ACROSS KDIGO RISK CATEGORIES: RESULTS FROM THE EMPA-REG OUTCOME® TRIAL
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Chronic kidney disease (CKD) is a strong risk factor for cardiovascular (CV) disease, causing substantial morbidity and mortality in this population. In the EMPA-REG OUTCOME® trial, empagliflozin (EMPA) given in addition to standard of care significantly reduced 3-point major adverse CV events (3-point MACE: composite of CV death, non-fatal myocardial infarction, or non-fatal stroke), CV death, and hospitalization for heart failure (HHF), versus placebo (PBO) in people with type 2 diabetes mellitus (T2DM) and established CV disease. The Kidney Disease: Improving Global Outcomes (KDIGO) CKD guidelines introduced a risk category framework based on eGFR and urine albumin-creatinine ratio (UACR) values. We investigated CV outcomes in subgroups of participants at different levels of risk in the EMPA-REG OUTCOME® trial. METHODS: Participants were randomized to receive EMPA 10 mg, EMPA 25 mg, or PBO. The outcomes of 3-point MACE, CV death, and HHF were analyzed in subgroups by baseline KDIGO risk category, defined as low risk (eGFR ≥60 ml/min/1.73m2 and UACR <30 mg/g), moderately increased risk (eGFR 45-59 ml/min/1.73m2 and UACR <30 mg/g, or eGFR ≥60 ml/min/1.73m2 and UACR 30-300 mg/g), high risk (eGFR 30-44 ml/min/1.73m2 and UACR <30 mg/g, eGFR 45-59 ml/min/1.73m2 and UACR 30-300 mg/g, or eGFR ≥60 and UACR >300 mg/g) and very high risk (eGFR <30 ml/min/1.73m2 with any UACR, eGFR 30-44 and UACR 30-300 mg/g, or eGFR 45-59 ml/min/1.73m2 and UACR >300 mg/g). A Cox proportional hazards model was used to investigate the consistency of treatment effect across subgroups. RESULTS: Among 7020 participants, baseline eGFR and UACR measurements were available for 6952 patients (99%; EMPA, n=4635; PBO, n=2317). The proportions of participants in the low, moderately increased, high and very high risk KDIGO categories at baseline were 47%, 29%, 15% and 8% respectively. Median observation time was 3.1 years. The observed benefits of EMPA vs PBO on 3-point MACE, CV death, and HHF were consistent across KDIGO risk categories (Figure). SO019 Figure
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".