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A Systematic Review of Kidney Transplantation Decision Modelling Studies

  • Systematic Review
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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.

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Authors and Affiliations

  1. Department of Pharmacy Practice, Mercer University College of Pharmacy, Atlanta, USA

    Mohsen Yaghoubi

  2. Faculty of Health Sciences, Simon Fraser University, School of Population and Public Health, University of British Columbia, Vancouver, Canada

    Sonya Cressman

  3. School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, Canada

    Louisa Edwards & Stirling Bryan

  4. Sauder School of Business, University of British Columbia, Vancouver, Canada

    Steven Shechter

  5. Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada

    Mary M. Doyle-Waters

  6. Department of Medicine, Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada

    Paul Keown

  7. Department of Medicine, McGill University, Montreal, Canada

    Ruth Sapir-Pichhadze

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  1. Mohsen Yaghoubi
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  3. Louisa Edwards
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  8. Stirling Bryan
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Correspondence to Stirling Bryan.

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Funding

This study was funded by Genome BC (Genome Canada 273AMR) and the Canadian Institutes of Health Research (CIHR GP1-155871).

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The Can-Prevent AMR study was approved by the University of British Columbia’s Research Ethics Board (H18-00090).

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Authors’ contributions

MY, LE, SB, SS, MDW, RSP and PK designed the study. MDW designed and performed the database searches. MY and LE reviewed the studies and extracted the data. SB, MY, LE, SC and SS analysed the results. All authors contributed to drafting the manuscript and preparing it for publication.

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Yaghoubi, M., Cressman, S., Edwards, L. et al. A Systematic Review of Kidney Transplantation Decision Modelling Studies. Appl Health Econ Health Policy 21, 39–51 (2023). https://doi.org/10.1007/s40258-022-00744-x

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