4.6 Article

Cost-Effectiveness of Molecularly Guided Treatment in Diffuse Large B-Cell Lymphoma (DLBCL) in Patients under 60

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CANCERS
卷 14, 期 4, 页码 -

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MDPI
DOI: 10.3390/cancers14040908

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cost-effectiveness analysis; diffuse large b-cell lymphoma; markov model; gene expression profiling; molecular subtyping

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  1. Genome British Columbia/Genome Canada [141LYM, 271LYM, G05CHS]

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This study examined the cost-effectiveness of adding ibrutinib to the treatment of patients under the age of 60 with ABC-DLBCL. The analysis found that genetic testing to diagnose ABC-DLBCL and providing ibrutinib along with standard care treatment had a low to moderate probability of being cost-effective, depending on decision maker willingness to pay and the cost of the genetic test.
Simple Summary Diffuse large B cell Lymphoma (DLBCL) can be categorized into cell of origin (COO) subtypes. Genetic tests are used to detect which subtype of DLBCL a patient has. One subtype of DLBCL, activated B-cell like (ABC), is associated with a comparatively poorer prognosis. New evidence suggests that patients under 60 with ABC-DLBCL may benefit from ibrutinib added to their standard care treatment regimen. Genetic testing and treatments like ibrutinib can be costly to publicly funded healthcare systems. Here, we report a cost-effectiveness analysis of the addition of Ibrutinib to treat patients under the age of 60 with ABC-DLBCL. Our analysis found that the use of genetic testing to diagnose ABC-DLBCL and providing ibrutinib along with standard care treatment has a low to moderate probability of being cost-effective, depending on decision maker willingness to pay and the cost of the genetic test. Background: Classifying diffuse large B-cell lymphoma (DLBCL) into cell-of-origin (COO) subtypes could allow for personalized cancer control. Evidence suggests that subtype-guided treatment may be beneficial in the activated B-cell (ABC) subtype of DLBCL, among patients under the age of 60. Methods: We estimated the cost-effectiveness of age- and subtype-specific treatment guided by gene expression profiling (GEP). A probabilistic Markov model examined costs and quality-adjusted life-years gained (QALY) accrued to patients under GEP-classified COO treatment over a 10-year time horizon. The model was calibrated to evaluate the adoption of ibrutinib as a first line treatment among patients under 60 years with ABC subtype DLBCL. The primary data source for efficacy was derived from published estimates of the PHOENIX trial. These inputs were supplemented with patient-level, real-world data from BC Cancer, which provides comprehensive cancer services to the population of British Columbia. Results: We found the cost-effectiveness of GEP-guided treatment vs. standard care was $77,806 per QALY (24.3% probability of cost-effectiveness at a willingness-to-pay (WTP) of $50,000/QALY; 53.7% probability at a WTP of $100,000/QALY) for first-line treatment. Cost-effectiveness was dependent on assumptions around decision-makers' WTP and the cost of the assay. Conclusions: We encourage further clinical trials to reduce uncertainty around the implementation of GEP-classified COO personalized treatment in this patient population.

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