Effects of Canagliflozin (CANA) on Kidney Outcomes: Pooled Analyses from the CANVAS Program and CREDENCE
Bibliographic record
Abstract
Background: CANA reduced the risk of sustained loss of kidney function in patients with type 2 diabetes mellitus (T2DM) and high cardiovascular (CV) risk or nephropathy. We analyzed the effects of CANA on time to first occurrence of doubling of serum creatinine (SCr) and end-stage kidney disease (ESKD) events using pooled data from the CANVAS Program and CREDENCE. Methods: This post hoc analysis included integrated data from the CANVAS Program and CREDENCE trials. The effects of CANA compared with placebo (PBO) on doubling of SCr and ESKD were examined in subgroups by baseline estimated glomerular filtration rate (eGFR; <45, 45-60, and >60 mL/min/1.73 m2). Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using a Cox proportional hazards model, stratified by trial. Results: A total of 14,543 participants from the CANVAS Program (N = 10,142) and CREDENCE (N = 4,401) were included. Among participants with baseline eGFR measurements, 1919 (13.2%) had eGFR <45 mL/min/1.73 m2, 2972 (20.4%) had eGFR 45-60 mL/min/1.73 m2, and 9649 (66.3%) had eGFR >60 mL/min/1.73 m2. CANA delayed the time to first doubling of SCr event and first ESKD event relative to PBO. Compared with PBO, CANA reduced the risk of doubling SCr (HR, 0.58; 95% CI, 0.47-0.71) consistently across eGFR subgroups (interaction P = 0.78; Figure). Reduced risk of ESKD was also seen with CANA versus PBO (HR, 0.69; 95% CI, 0.55-0.87), irrespective of baseline eGFR (interaction P = 0.86).Figure.: Effects of CANA on Doubling of SCr and ESKD.Conclusions: In patients with T2DM and high CV risk or nephropathy, CANA reduced the risk of doubling of SCr and ESKD, with consistent benefits observed across baseline chronic kidney disease stage, including those with preserved eGFR >60 mL/min/1.73 m2.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".