TPMT testing in azathioprine: a ''cost-effective use of healthcare resources''?. (January 2009)

• Record Type: Journal Article
• Authors: Payne, Katherine; Newman, William G; Gurwitz, David; Ibarreta, Dolores; Phillips, Kathryn A
• Is Part Of: Personalized medicine. Volume 6:Number 1(2009)
• Page Start: 103
• Page End: 113
• Publication Date: 2009-01
• Subjects: azathioprine -- cost––effectiveness analysis -- economic evaluation -- Imuran -- leucopenia -- mercaptopurines -- neutropenia -- pharmacogenetics -- thiopurine S-methyltransferase -- value of information; Pharmacogenomics -- Periodicals; Pharmacogenetics -- Periodicals; 615.19
• DOI: 10.2217/17410541.6.1.103
• Languages: English
• ISSNs: 1741-0541

Abstract

This study aimed to critically appraise the current level of economic evidence available for thiopurine S -methyltransferase (TPMT) testing of thiopurine drugs, such as azathioprine. Six economic evaluations of testing were identified, which all recommended that TPMT testing is a cost-effective use of healthcare resources. Critical appraisal, using published guidelines, showed potential limitations in model structures, approaches to data analysis and input parameters, which were mainly based on expert opinion. Where data did exist these were from retrospective studies. To conduct economic evaluations with more robust findings, decision analysts need good quality data for the following key parameters: current prevalence of profound neutropenia among patients prescribed thiopurine drugs; mean length of related hospitalization and clinical outcome; impact of introducing the test on clinical pathways in terms of resource use; and clinical effectiveness data in terms of number of cases of neutropenia averted and subsequent impact on mortality and health-related quality of life. An iterative approach may be used to stimulate the production of a sufficient evidence base for innovative technologies, such as pharmacogenetic testing. Such an iterative approach involves starting with simple models using available existing clinical and resource use data, as in the case of TPMT testing. The use of formal value of information methods may guide the decision whether prospective studies are required to address uncertainties in the key parameters driving the model results. The results from well-designed prospective studies can then be used to populate more complex economic models.