Cardio/Kidney Composite End Points: A Post Hoc Analysis of the EMPA‐REG OUTCOME Trial
Bibliographic record
Abstract
Background Cardio/kidney composite end points are clinically relevant but rarely analyzed in cardiovascular trials. This post hoc analysis of the EMPA-REG OUTCOME (Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients) trial evaluated cardio/kidney composite end points by 2 statistical approaches. Methods and Results A total of 7020 patients with type 2 diabetes mellitus and established cardiovascular disease were treated with empagliflozin 10 or 25 mg (n=4687) or placebo (n=2333) on top of standard care. Cardio/kidney composite end points studied were: (1) cardiac or kidney death, kidney failure, hospitalization for heart failure, sustained decline in estimated glomerular filtration rate ≥40% from baseline, or sustained progression to macroalbuminuria; (2) cardiac or kidney death, kidney failure, hospitalization for heart failure, or sustained estimated glomerular filtration rate decline ≥40% from baseline; and (3) cardiac or kidney death, kidney failure, hospitalization for heart failure, or sustained doubling in serum creatinine from baseline. Cox regression using time-to-first-event analysis and win ratio (WR) using hierarchical order of events were applied. Empagliflozin reduced the risk of all cardio/kidney composites. The results varied only slightly between Cox and WR (eg, composite 1: hazard ratio, 0.56 [95% CI, 0.49-0.64]; WR, 1.76 [95% CI, 1.53-2.02]. WR prioritizes events by clinical importance; in particular, all fatal events are evaluated, whereas Cox regression ignores deaths when preceded by nonfatal events. Of the 285 cardio/kidney deaths in the analysis, 44 to 56 (15%-20%), depending on the composite, occurred after a nonfatal event and were not evaluated in Cox regression but evaluated by the WR. Conclusions By considering the clinical relevance of different event types, the WR represents an appropriate method to complement the traditional time-to-first-event analysis in cardio/kidney outcomes. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01131676.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".