Mohsen Yaghoubi1, Sonya Cressman2, Louisa Edwards3, Steven Shechter4, Mary M Doyle-Waters5, Paul Keown6, Ruth Sapir-Pichhadze7, Stirling Bryan8. 1. Department of Pharmacy Practice, Mercer University College of Pharmacy, Atlanta, USA. 2. Faculty of Health Sciences, Simon Fraser University, School of Population and Public Health, University of British Columbia, Vancouver, Canada. 3. School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, Canada. 4. Sauder School of Business, University of British Columbia, Vancouver, Canada. 5. Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada. 6. Department of Medicine, Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada. 7. Department of Medicine, McGill University, Montreal, Canada. 8. School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, Canada. Stirling.Bryan@ubc.ca.
Abstract
BACKGROUND: Genome-based precision medicine strategies promise to minimize premature graft loss after renal transplantation, through precision approaches to immune compatibility matching between kidney donors and recipients. The potential adoption of this technology calls for important changes to clinical management processes and allocation policy. Such potential policy change decisions may be supported by decision models from health economics, comparative effectiveness research and operations management. OBJECTIVE: We used a systematic approach to identify and extract information about models published in the kidney transplantation literature and provide an overview of the status of our collective model-based knowledge about the kidney transplant process. METHODS: Database searches were conducted in MEDLINE, Embase, Web of Science and other sources, for reviews and primary studies. We reviewed all English-language papers that presented a model that could be a tool to support decision making in kidney transplantation. Data were extracted on the clinical context and modelling methods used. RESULTS: A total of 144 studies were included, most of which focused on a single component of the transplantation process, such as immunosuppressive therapy or donor-recipient matching and organ allocation policies. Pre- and post-transplant processes have rarely been modelled together. CONCLUSION: A whole-disease modelling approach is preferred to inform precision medicine policy, given its potential upstream implementation in the treatment pathway. This requires consideration of pre- and post-transplant natural history, risk factors for allograft dysfunction and failure, and other post-transplant outcomes. Our call is for greater collaboration across disciplines and whole-disease modelling approaches to more accurately simulate complex policy decisions about the integration of precision medicine tools in kidney transplantation.
BACKGROUND: Genome-based precision medicine strategies promise to minimize premature graft loss after renal transplantation, through precision approaches to immune compatibility matching between kidney donors and recipients. The potential adoption of this technology calls for important changes to clinical management processes and allocation policy. Such potential policy change decisions may be supported by decision models from health economics, comparative effectiveness research and operations management. OBJECTIVE: We used a systematic approach to identify and extract information about models published in the kidney transplantation literature and provide an overview of the status of our collective model-based knowledge about the kidney transplant process. METHODS: Database searches were conducted in MEDLINE, Embase, Web of Science and other sources, for reviews and primary studies. We reviewed all English-language papers that presented a model that could be a tool to support decision making in kidney transplantation. Data were extracted on the clinical context and modelling methods used. RESULTS: A total of 144 studies were included, most of which focused on a single component of the transplantation process, such as immunosuppressive therapy or donor-recipient matching and organ allocation policies. Pre- and post-transplant processes have rarely been modelled together. CONCLUSION: A whole-disease modelling approach is preferred to inform precision medicine policy, given its potential upstream implementation in the treatment pathway. This requires consideration of pre- and post-transplant natural history, risk factors for allograft dysfunction and failure, and other post-transplant outcomes. Our call is for greater collaboration across disciplines and whole-disease modelling approaches to more accurately simulate complex policy decisions about the integration of precision medicine tools in kidney transplantation.
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Authors: Joshua Y Kausman; Amanda M Walker; Linda S Cantwell; Catherine Quinlan; Matthew P Sypek; Francesco L Ierino Journal: Pediatr Transplant Date: 2016-09-24
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Authors: Chris Wiebe; David N Rush; Thomas E Nevins; Patricia E Birk; Tom Blydt-Hansen; Ian W Gibson; Aviva Goldberg; Julie Ho; Martin Karpinski; Denise Pochinco; Atul Sharma; Leroy Storsley; Arthur J Matas; Peter W Nickerson Journal: J Am Soc Nephrol Date: 2017-07-20 Impact factor: 10.121
Authors: C Wiebe; D Pochinco; T D Blydt-Hansen; J Ho; P E Birk; M Karpinski; A Goldberg; L J Storsley; I W Gibson; D N Rush; P W Nickerson Journal: Am J Transplant Date: 2013-10-25 Impact factor: 8.086