» Articles » PMID: 34980048

Clinical and Economic Impact of Molecular Testing for BRAF Fusion in Pediatric Low-grade Glioma

Overview
Journal BMC Pediatr
Publisher Biomed Central
Specialty Pediatrics
Date 2022 Jan 4
PMID 34980048
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Treatment personalization via tumor molecular testing holds promise for improving outcomes for patients with pediatric low-grade glioma (PLGG). We evaluate the health economic impact of employing tumor molecular testing to guide treatment for patients diagnosed with PLGG, particularly the avoidance of radiation therapy (RT) for patients with BRAF-fusion.

Methods: We performed a model-based cost-utility analysis comparing two strategies: molecular testing to determine BRAF fusion status at diagnosis against no molecular testing. We developed a microsimulation to model the lifetime health and cost outcomes (in quality-adjusted life years (QALYs) and 2018 CAD, respectively) for a simulated cohort of 100,000 patients newly diagnosed with PLGG after their initial surgery.

Results: The life expectancy after diagnosis for individuals who did not receive molecular testing was 39.01 (95% Confidence Intervals (CI): 32.94;44.38) years and 40.08 (95% CI: 33.19;45.76) years for those who received testing. Our findings indicate that patients who received molecular testing at diagnosis experienced a 0.38 (95% CI: 0.08;0.77) gain in QALYs and $1384 (95% CI: $-3486; $1204) reduction in costs over their lifetime. Cost and QALY benefits were driven primarily by the avoidance of long-term adverse events (stroke, secondary neoplasms) associated with unnecessary use of radiation.

Conclusions: We demonstrate the clinical benefit and cost-effectiveness of molecular testing in guiding the decision to provide RT in PLGG. While our results do not consider the impact of targeted therapies, this work is an example of the value of simulation modeling in assessing the long-term costs and benefits of precision oncology interventions for childhood cancer, which can aid decision-making about health system reimbursement.

References
1.
Guyot P, Ades A, Ouwens M, Welton N . Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol. 2012; 12:9. PMC: 3313891. DOI: 10.1186/1471-2288-12-9. View

2.
Krijkamp E, Alarid-Escudero F, Enns E, Jalal H, Hunink M, Pechlivanoglou P . Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial. Med Decis Making. 2018; 38(3):400-422. PMC: 6349385. DOI: 10.1177/0272989X18754513. View

3.
de Oliveira C, Bremner K, Liu N, Greenberg M, Nathan P, McBride M . Costs of cancer care in children and adolescents in Ontario, Canada. Pediatr Blood Cancer. 2017; 64(11). DOI: 10.1002/pbc.26628. View

4.
Weinstein M . Recent developments in decision-analytic modelling for economic evaluation. Pharmacoeconomics. 2006; 24(11):1043-53. DOI: 10.2165/00019053-200624110-00002. View

5.
Krishnatry R, Zhukova N, Guerreiro Stucklin A, Pole J, Mistry M, Fried I . Clinical and treatment factors determining long-term outcomes for adult survivors of childhood low-grade glioma: A population-based study. Cancer. 2016; 122(8):1261-9. DOI: 10.1002/cncr.29907. View