#4140 DAPRODUSTAT VERSUS RECOMBINANT HUMAN ERYTHROPOIETIN FOR TREATING ANAEMIA OF CHRONIC KIDNEY DISEASE: COST EFFECTIVENESS MODEL METHODOLOGY AND FINDINGS
Notice bibliographique
Résumé
Abstract Background and Aims Anaemia is a common manifestation in patients with chronic kidney disease (CKD).1,2 Daprodustat, a hypoxia-inducible factor prolyl hydroxylase inhibitor, offers an alternative to conventional treatment with recombinant human erythropoietins (rhEPOs). This study reports the development of a cost-effectiveness analysis to support reimbursement discussions for daprodustat in Canada. Method A Markov model was developed to conduct a cost-utility analysis to compare expected costs and outcomes associated with daprodustat, vs two rhEPO treatment comparators (darbepoetin alfa [DA] or epoetin alfa [EA]), which represent standard of care. The model used a lifetime time horizon, and a publicly funded Canadian healthcare payer perspective for adults with anaemia of CKD (on/not on dialysis). Clinical inputs for the model were obtained from two Phase 3 clinical trials: ASCEND-D1 (daprodustat vs DA or EA in 2964 dialysis patients) and ASCEND-ND2 (daprodustat vs DA in 3872 non-dialysis patients). Efficacy and adverse event profiles of both rhEPOs were assumed equal. Patients entering the model (Figure) receive daprodustat, DA or EA. The model includes five health states informed by ASCEND-D and ASCEND-ND: non-dialysis, dialysis, kidney transplant, post-transplantation and terminal care. While in the dialysis or non-dialysis health state, costs and utilities are assigned based on three haemoglobin (Hb) levels: <10g/dL, 10–11.5g/dL or >11.5g/dL. A monthly cycle length is utilised and aligned with the ASCEND study visits and observations up to Week 52. Hb concentrations from ASCEND-D and ASCEND-ND are used to estimate proportions of patients in the target Hb range of 10–11.5g/dL, and non-target Hb levels. They were assumed to have different utility values. Patients could transition from non-dialysis to dialysis; in the latter state, patients could die or receive a kidney transplant, and transition to a post-transplantation state. Transition probabilities were derived from ASCEND-D and ASCEND-ND data and the literature. The median follow-up durations in ASCEND-D and ASCEND-ND were 2.5 and 1.9 years, respectively, necessitating extrapolation to the model time horizon; increases over time in the transition probabilities were applied. It is assumed that patients cannot transition back to non-dialysis from the dialysis state. Patients may transition to death from any health state, with mortality risks informed by the ASCEND trials and Canadian all-cause mortality data. Costs in the model included treatment acquisition and administration, cold-chain storage, dialysis, kidney transplant, transfusions of red blood cells and iron, and adverse events (AEs). All costs were sourced from fee schedules (2022 $CAD) or the literature. Utilities for health states and Hb levels were obtained from the ASCEND trials; AE-associated utility decrements could not be estimated from ASCEND data due to low event rates – hence, disutilities were sourced from the literature. Results In the probabilistic reference case analysis, daprodustat was less costly (comparison with DA: −$8763; EA:−$13,864) and produced more quality-adjusted life years (DA: +0.012; EA: +0.018; Table). The incremental savings with daprodustat versus DA and EA were −$860 and −$1033, respectively, in the non-dialysis state, and −$7904 and −$12,831 in the dialysis state. Contributors to the cost savings with daprodustat include an absence of cold-chain storage costs and reduced administration costs. Conclusion This study indicates that daprodustat is less costly and originates more QALYs than DA and EA in the treatment of anaemia due to CKD. In this setting, daprodustat may provide cost savings from a Canadian public payer perspective. The model described here could be adapted to other perspectives, including European countries.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,001 |
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 ».