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Optimal Vascular Access Choice for Patients on Hemodialysis

Author

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  • M. Reza Skandari

    (Operations and Logistics Division, Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

  • Steven M. Shechter

    (Operations and Logistics Division, Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

  • Nadia Zalunardo

    (Division of Nephrology, Department of Medicine, University of British Columbia, Vancouver, British Columbia V5Z 1M9, Canada)

Abstract

Which vascular access to use is considered one of the most important questions in the care of patients on hemodialysis (HD). An arteriovenous fistula (AVF) is often considered the gold standard for delivering HD due to better patient survival, higher quality of life, and fewer complications. However, AVFs have some limitations: they require surgery, it takes approximately three months to know whether the surgery was successful, and a majority of these surgeries end in failure. Conversely, another common vascular access, the central venous catheter, can be inserted via a simple procedure and used immediately after placement. In this research, we address the question of whether and when to perform AVF surgery on incident and established HD patients, with the aim of finding individualized policies that maximize a patient’s probability of survival and remaining quality-adjusted life expectancy. Using a continuous-time dynamic programming model and under certain data-driven assumptions, we establish structural properties of the optimal policy for each objective. We provide further insights for policy makers through our numerical experiments.

Suggested Citation

  • M. Reza Skandari & Steven M. Shechter & Nadia Zalunardo, 2015. "Optimal Vascular Access Choice for Patients on Hemodialysis," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 608-619, October.
  • Handle: RePEc:inm:ormsom:v:17:y:2015:i:4:p:608-619
    DOI: 10.1287/msom.2015.0552
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    References listed on IDEAS

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    1. Turgay Ayer & Oguzhan Alagoz & Natasha K. Stout, 2012. "OR Forum---A POMDP Approach to Personalize Mammography Screening Decisions," Operations Research, INFORMS, vol. 60(5), pages 1019-1034, October.
    2. Swapnil Hiremath & Greg Knoll & Milton C Weinstein, 2011. "Should the Arteriovenous Fistula Be Created before Starting Dialysis?: A Decision Analytic Approach," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-8, December.
    3. Lisa M. Maillart & Julie Simmons Ivy & Scott Ransom & Kathleen Diehl, 2008. "Assessing Dynamic Breast Cancer Screening Policies," Operations Research, INFORMS, vol. 56(6), pages 1411-1427, December.
    4. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2004. "The Optimal Timing of Living-Donor Liver Transplantation," Management Science, INFORMS, vol. 50(10), pages 1420-1430, October.
    5. Chris P. Lee & Glenn M. Chertow & Stefanos A. Zenios, 2008. "Optimal Initiation and Management of Dialysis Therapy," Operations Research, INFORMS, vol. 56(6), pages 1428-1449, December.
    6. Jingyu Zhang & Brian T. Denton & Hari Balasubramanian & Nilay D. Shah & Brant A. Inman, 2012. "Optimization of Prostate Biopsy Referral Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 529-547, October.
    7. Mehmet U. S. Ayvaci & Oguzhan Alagoz & Elizabeth S. Burnside, 2012. "The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 600-617, October.
    8. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
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    Cited by:

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