Risk-Based Implementation of SGLT2 Inhibitors: Insights from the CANVAS Program and CREDENCE Trial
Bibliographic record
Abstract
Background: The Kidney Disease Improving Global Outcomes (KDIGO) 2024 guideline for the evaluation and management of chronic kidney disease (CKD) recommends using a validated risk score to estimate absolute risk of kidney failure (1A recommendation). Given that those at highest risk of CKD progression are also at highest risk for cardiovascular outcomes, we sought to determine if a risk-based approach would identify those who benefit most from a cardio-kidney perspective with SGLT2 inhibition. Methods: In this post-hoc individual participant data analysis of the CANVAS program and CREDENCE trial, we categorized participants according to risk of CKD progression using the KDIGO classification of CKD, Klinrisk algorithm, and the Kidney Failure Risk Equation (restricted to participants with eGFR <60 mL/min1/1.73m2). Effects of canagliflozin on a cardio-kidney composite outcome of 40% decline in eGFR, kidney failure or death due to cardiovascular or kidney disease were analyzed using Cox and Poisson regression models. Results: Across higher kidney risk categories, participants were more likely to have lower eGFR, higher urine albumin:creatinine ratio, higher systolic blood pressure, and longer duration of diabetes (all p<0.0001). Overall, canagliflozin reduced the relative risk of the cardio-kidney composite outcome by 26% (HR 0.74, 95% CI 0.67-0.82). The relative benefits of canagliflozin were at least as large across higher kidney risk categories (Figure; Panel A). Absolute risk reductions were largest in participants at highest baseline risk (Figure; Panel B). Conclusion: The use of validated kidney risk scores can accurately identify those who benefit most from SGLT2 inhibition with canagliflozin.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".