FC083: Finerenone and Canagliflozin in the Treatment of Chronic Kidney Disease and Type 2 Diabetes: Matching-Adjusted Indirect Treatment Comparison of Fidelio-DKD and Credence
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS Finerenone (FIN: an oral, nonsteroidal mineralocorticoid receptor antagonist) and canagliflozin (CAN: a sodium-glucose cotransporter 2 inhibitor) demonstrated cardiorenal efficacy in patients with chronic kidney disease and type 2 diabetes on top of renin–angiotensin system blockade in phase III placebo-controlled studies (FIDELIO-DKD [NCT02540993] for FIN; CREDENCE [NCT02065791] for CAN) [3,2]. In the absence of head-to-head studies, we assessed FIN and CAN in this patient population while appropriately accounting for meaningful differences between the trials. METHOD We performed an anchored matching-adjusted indirect comparison (MAIC), with placebo (PBO) as a common comparator, to generate measures of efficacy and safety while accounting for differences between the two study populations [3]. Individual patient-level data from FIDELIO-DKD and published data from CREDENCE were used [1,[2]. Weights were calculated and assigned to each patient in FIDELIO-DKD so the weighted population of FIDELIO-DKD matched that of CREDENCE for selected baseline characteristics, e.g. mean estimated glomerular filtration rate (eGFR; 44.3 and 56.2 mL/min/1.73 m2 in FIDELIO-DKD and CREDENCE, respectively). Weights were obtained from a logistic regression model of the odds of enrolment in CREDENCE and FIDELIO-DKD for baseline characteristics believed to be effect modifiers. Hazard ratios (HRs) with 95% confidence intervals (CIs) comparing FIN and PBO for time-to-event endpoints were estimated based on the weighted population of FIDELIO-DKD. HRs with 95% CIs comparing FIN and CAN were then calculated from results of the previous step and published data from CREDENCE [2]. This analysis evaluated the cardiorenal composite endpoint from CREDENCE (kidney failure [dialysis, transplantation or sustained eGFR < 15 mL/min/1.73 m2), a doubling of serum creatinine level or death from kidney or cardiovascular disease) [2] and hyperkalaemia. A sensitivity analysis that matched patients based on their history of heart failure was performed. RESULTS Calculation of the weights for the FIDELIO-DKD population (N = 5674) resulted in an effective sample size of 1288 for the pseudo-population formed by the weighting to compare with the CREDENCE population (N = 4401). For the cardiorenal composite endpoint, the HR (95% CI) for FIN and PBO based on reweighted FIDELIO-DKD data was 0.72 (0.59–0.90) and the MAIC-based HR (95% CI) for FIN and CAN was 1.03 (0.79–1.35) (P = 0.802). For hyperkalaemia, the MAIC-based HR (95% CI) for FIN and CAN was 2.25 (1.67–3.03) (P < 0.001). Similar efficacy and safety results were demonstrated in the sensitivity analysis. CONCLUSION The MAIC for FIDELIO-DKD and CREDENCE enabled more robust assessment of FIN and CAN when a similar patient population was considered. There was no evidence of a significant difference between FIN and CAN in the cardiorenal composite endpoint as assessed in CREDENCE. These results are consistent with a recent analysis using a different payer-accepted method that also accounted for differences between the FIDELIO-DKD and CREDENCE inclusion criteria and endpoints [3,4].
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".