SP259EFFECTS OF EMPAGLIFLOZIN ON RENAL OUTCOMES ACROSS KDIGO RISK CATEGORIES: RESULTS FROM THE EMPA-REG OUTCOME® TRIAL
Bibliographic record
Abstract
INTRODUCTION AND AIMS: In the EMPA-REG OUTCOME® trial, empagliflozin (EMPA) given in addition to standard of care significantly reduced the risk of incident or worsening nephropathy (progression to urine albumin-creatinine ratio [UACR] >300 mg/g, doubling of serum creatinine level, initiation of renal replacement therapy, or death from renal disease) by 39% versus placebo (PBO) in people with type 2 diabetes mellitus (T2DM) and established cardiovascular disease. The Kidney Disease: Improving Global Outcomes (KDIGO) chronic kidney disease (CKD) classification provides a renal risk prediction framework, using estimated glomerular filtration rate (eGFR) and UACR. We assessed renal outcomes in the EMPA-REG OUTCOME® trial in participants across each of the two-dimensional risk categories for progression of CKD. METHODS: Participants were randomized to receive EMPA 10 mg, EMPA 25 mg, or PBO. Renal function was assessed by the creatinine-based GFR estimating equations based on Modification of Diet in Renal Disease (MDRD) formula. The renal outcome of incident or worsening nephropathy and its components 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 incident or worsening nephropathy, progression to UACR >300 mg/g and the composite of hard renal endpoints (doubling of serum creatinine, initiation of renal replacement therapy, or death from renal disease) were consistent across KDIGO risk categories (Figure). SP259 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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) | 0.003 | 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".