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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,007 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».