Comparative Efficacy of Finerenone versus Canagliflozin in Patients with Chronic Kidney Disease and Type 2 Diabetes: A Matching-Adjusted Indirect Comparison
Bibliographic record
Abstract
This study aimed to close an evidence gap concerning the relative efficacy of finerenone versus SGLT2is in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). Canagliflozin was selected as a proxy for the SGLT2i class. Patient-level data of two randomized controlled trials (RCTs) of finerenone (FIDELIO-DKD and FIGARO-DKD) were used alongside aggregated data from CREDENCE, an RCT of canagliflozin. To account for meaningful between-study heterogeneity between each finerenone trial and CREDENCE, a matching-adjusted indirect comparison of a range of efficacy outcomes was undertaken for each finerenone study versus CREDENCE. These results were meta-analyzed, enabling the estimation of the relative effects of finerenone against canagliflozin. For the cardiorenal composite endpoint, the hazard ratio (HR) comparing finerenone to canagliflozin was 1.07 (95% CI: 0.83 to 1.36). The corresponding HRs for all-cause mortality, end-stage kidney disease and cardiovascular death were 0.99 (95% CI: 0.73 to 1.34), 1.03 (95% CI: 0.68 to 1.55) and 0.94 (95% CI: 0.64 to 1.37), respectively. The absence of statistically significant differences was consistent throughout the main analysis and a range of sensitivity analyses. Based on this study, using a large sample of data and adjusted for meaningful differences between the baseline characteristics of the included RCTs, there was no statistically significant evidence indicating a difference in the efficacy of finerenone compared to canagliflozin in the treatment of CKD in patients with T2D.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".