#4140 DAPRODUSTAT VERSUS RECOMBINANT HUMAN ERYTHROPOIETIN FOR TREATING ANAEMIA OF CHRONIC KIDNEY DISEASE: COST EFFECTIVENESS MODEL METHODOLOGY AND FINDINGS
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".