Cost-effectiveness analysis of microwave ablation versus robot-assisted partial nephrectomy for patients with small renal masses in Australia.

Cost effectiveness analysis Metastasis Microwave ablation Recurrence Renal cell carcinoma Robotic-assisted partial nephrectomy Small renal masses

Journal

Urologic oncology
ISSN: 1873-2496
Titre abrégé: Urol Oncol
Pays: United States
ID NLM: 9805460

Informations de publication

Date de publication:
03 Oct 2024
Historique:
received: 28 03 2024
revised: 30 08 2024
accepted: 14 09 2024
medline: 5 10 2024
pubmed: 5 10 2024
entrez: 4 10 2024
Statut: aheadofprint

Résumé

Microwave ablation (MWA) has gained attention as a minimally invasive and safe alternative to surgical intervention for patients with small renal masses; however, its cost-effectiveness in Australia remains unclear. This study conducted a cost-effectiveness analysis to evaluate the relative clinical and economic merits of MWA compared to robotic-assisted partial nephrectomy (RA-PN) in the treatment of small renal masses. A Markov state-transition model was constructed to simulate the progression of Australian patients with small renal masses treated with MWA versus RA-PN over a 10-year horizon. Transition probabilities and utility data were sourced from comprehensive literature reviews, and cost data were estimated from the Australian health system perspective. Life-years, quality-adjusted life-years (QALYs), and lifetime costs were estimated. Modelled uncertainty was assessed using both deterministic and probabilistic sensitivity analyses. A willingness-to-pay (WTP) threshold of $50,000 per QALY was adopted. All costs are expressed in 2022 Australian dollars and discounted at 3% annually. To assess the broader applicability of our findings, a validated cost-adaptation method was employed to extend the analysis to 8 other high-income countries. Both the base case and cost-adaptation analyses revealed that MWA dominated RA-PN, producing both lower costs and greater effectiveness over 10 years. The cost-effectiveness outcome was robust across all model parameters. Probabilistic sensitivity analyses confirmed that MWA was dominant in 98.3% of simulations at the designated WTP threshold, underscoring the reliability of the model under varying assumptions. For patients with small renal masses in Australia and comparable healthcare settings, MWA is the preferred strategy to maximize health benefits per dollar, making it a highly cost-effective alternative to RA-PN.

Identifiants

pubmed: 39366793
pii: S1078-1439(24)00657-4
doi: 10.1016/j.urolonc.2024.09.016
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Qing Xia (Q)

Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, Queensland, Australia. Electronic address: qing.xia@qut.edu.au.

Sameera Jayan Senanayake (SJ)

Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.

Sanjeewa Kularatna (S)

Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.

David Brain (D)

Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.

Steven M McPhail (SM)

Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, Queensland, Australia; Digital Health and Informatics Directorate, Metro South Health, Brisbane, Queensland, Australia.

Will Parsonage (W)

Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.

Melissa Eastgate (M)

Department of Medical Oncology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.

Annette Barnes (A)

Department of Medical Oncology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.

Nick Brown (N)

The Wesley Hospital, Brisbane, Queensland, Australia; The University of Queensland, St Lucia, Queensland, Australia.

Hannah E Carter (HE)

Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.

Classifications MeSH