Personalized treatment of women with early breast cancer: a risk-group specific cost-effectiveness analysis of adjuvant chemotherapy accounting for companion prognostic tests OncotypeDX and Adjuvant!Online

BMC Cancer

Research Article

Rebecca Miksad¹  Michael Hubalek²  David Stenehjem³  Marvin Bundo⁴  Beate Jahn⁵  Ursula Rochau⁵  Gaby Sroczynski⁵  Uwe Siebert⁶  Diana Brixner⁷  Christina Kurzthaler⁸  Murray Krahn⁹  Mike Paulden^1,10 

[1] Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, 02215, Boston, MA, USA;Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 101 Merrimac St., 10th FL, 02114, Boston, MA, USA;Department of Obstetrics and Gynecology, Medical University of Innsbruck, Christoph-Probst-Platz, Innrain 52, A-6020, Innsbruck, Austria;Department of Pharmacotherapy, University of Utah, 30 South 2000 East Room 4781, 84108, Salt Lake City, UT, USA;Huntsman Cancer Institute, University of Utah Hospitals & Clinics, 2000 Cir of Hope Dr, 84112, Salt Lake City, UT, USA;Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnöfer-Zentrum 1, A-6060, Hall i.T, Austria;Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnöfer-Zentrum 1, A-6060, Hall i.T, Austria;Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Karl-Kapferer-Straße 5, A-6020, Innsbruck, Austria;Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnöfer-Zentrum 1, A-6060, Hall i.T, Austria;Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Karl-Kapferer-Straße 5, A-6020, Innsbruck, Austria;Center for Health Decision Science, Department of Health Policy and Management, Harvard T.H Chan School of Public Health, 718 Huntington Ave. 2nd Floor, 02115, Boston, MA, USA;Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 101 Merrimac St., 10th FL, 02114, Boston, MA, USA;Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnöfer-Zentrum 1, A-6060, Hall i.T, Austria;Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Karl-Kapferer-Straße 5, A-6020, Innsbruck, Austria;Department of Pharmacotherapy, University of Utah, 30 South 2000 East Room 4781, 84108, Salt Lake City, UT, USA;Program in Personalized Health, University of Utah, 15 North 2030 East, Room 2160, 84112, Salt Lake City, UT, USA;Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnöfer-Zentrum 1, A-6060, Hall i.T, Austria;Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Karl-Kapferer-Straße 5, A-6020, Innsbruck, Austria;Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21A, A-6020, Innsbruck, Austria;Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto General Hospital, 10EN, Room 249, 200 Elizabeth Street, M5G 2C4, Toronto, ON, Canada;Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto General Hospital, 10EN, Room 249, 200 Elizabeth Street, M5G 2C4, Toronto, ON, Canada;Department of Emergency Medicine, University of Alberta, 116 St. and 85 Ave., T6G 2R3, Edmonton, AB, Canada;

关键词: Cost-effectiveness analysis;    Breast cancer;    Adjuvant chemotherapy;    Adjuvant!Online;    OncotypeDX;    Discrete event simulation;    Personalized medicine;    Decision analysis;    Cost-utility analysis;   

DOI  :  10.1186/s12885-017-3603-z

 received in 2016-08-26, accepted in 2017-08-23,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundDue to high survival rates and the relatively small benefit of adjuvant therapy, the application of personalized medicine (PM) through risk stratification is particularly beneficial in early breast cancer (BC) to avoid unnecessary harms from treatment. The new 21-gene assay (OncotypeDX, ODX) is a promising prognostic score for risk stratification that can be applied in conjunction with Adjuvant!Online (AO) to guide personalized chemotherapy decisions for early BC patients. Our goal was to evaluate risk-group specific cost effectiveness of adjuvant chemotherapy for women with early stage BC in Austria based on AO and ODX risk stratification.MethodsA previously validated discrete event simulation model was applied to a hypothetical cohort of 50-year-old women over a lifetime horizon. We simulated twelve risk groups derived from the joint application of ODX and AO and included respective additional costs. The primary outcomes of interest were life-years gained, quality-adjusted life-years (QALYs), costs and incremental cost-effectiveness (ICER). The robustness of results and decisions derived were tested in sensitivity analyses. A cross-country comparison of results was performed.ResultsChemotherapy is dominated (i.e., less effective and more costly) for patients with 1) low ODX risk independent of AO classification; and 2) low AO risk and intermediate ODX risk. For patients with an intermediate or high AO risk and an intermediate or high ODX risk, the ICER is below 15,000 EUR/QALY (potentially cost effective depending on the willingness-to-pay). Applying the AO risk classification alone would miss risk groups where chemotherapy is dominated and thus should not be considered. These results are sensitive to changes in the probabilities of distant recurrence but not to changes in the costs of chemotherapy or the ODX test.ConclusionsBased on our modeling study, chemotherapy is effective and cost effective for Austrian patients with an intermediate or high AO risk and an intermediate or high ODX risk. In other words, low ODX risk suggests chemotherapy should not be considered but low AO risk may benefit from chemotherapy if ODX risk is high. Our analysis suggests that risk-group specific cost-effectiveness analysis, which includes companion prognostic tests are essential in PM.

【 授权许可】

CC BY   
© The Author(s). 2017