Mathematical Optimization and Simulation Analyses for Optimal Liver Allocation Boundaries

Koizumi, Naoru; Gentili, Monica; Ganesan, Rajesh; DasGupta, Debasree; Patel, Amit; Chen, Chun‐Hung; Waters, Nigel; Melancon, Keith

doi:10.1002/9781118919408.ch15

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…In contrast to liver allocation, 8‐10 there are few proposals describing design solutions for geographic allocation units for kidneys. We and other researchers have used mathematical modeling to design systems for liver and kidney allocation to reduce geographic disparities 9,11,12 …”

Section: Discussionmentioning

confidence: 99%

Allocating kidneys in optimized heterogeneous circles

Karami

Kernodle

Ishaque

et al. 2021

American Journal of Transplantation

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Recently, the Organ Procurement and Transplant Network approved a plan to allocate kidneys within 250‐nm circles around donor hospitals. These homogeneous circles might not substantially reduce geographic differences in transplant rates because deceased donor kidney supply and demand differ across the country. Using Scientific Registry of Transplant Recipients data from 2016‐2019, we used an integer program to design unique, heterogeneous circles with sizes between 100 and 500 nm that reduced supply/demand ratio variation across transplant centers. We weighted demand according to wait time because candidates who have waited longer have higher priority. We compared supply/demand ratios and average travel distance of kidneys, using heterogeneous circles and 250 and 500‐nm fixed‐distance homogeneous circles. We found that 40% of circles could be 250 nm or smaller, while reducing supply/demand ratio variation more than homogeneous circles. Supply/demand ratios across centers for heterogeneous circles ranged from 0.06 to 0.13 kidneys per wait‐year, compared to 0.04 to 0.47 and 0.05 to 0.15 kidneys per wait‐year for 250‐nm and 500‐nm homogeneous circles, respectively. The average travel distance for kidneys using heterogeneous, and 250‐nm and 500‐nm fixed‐distance circles was 173 nm, 134 nm, and 269 nm, respectively. Heterogeneous circles reduce geographic disparity compared to homogeneous circles, while maintaining reasonable travel distances.

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Section: Discussionmentioning

confidence: 99%

Allocating kidneys in optimized heterogeneous circles

Karami

Kernodle

Ishaque

et al. 2021

American Journal of Transplantation

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“…Some these studies that redrew the OPO and UNOS boundaries without adding new transplant centers include the work by Gentry (2013) and Demicri (2012), all of which have attempted to balance efficiency and equity of allocation. Another research studied by Koizumi et al (2014aKoizumi et al ( , 2014b) performs a location allocation analysis that determines new locations of liver transplant centers, and converts some of the kidney transplant centers into both kidney and liver transplant centers, which have partially alleviated geographical disparity but adds to the infrastructure costs of expanding the number of TX in the USA. It is generally believed that, in reality, changing administration and jurisdiction by redrawing UNOS and OPO boundaries cannot be easily implemented due to the physical infrastructure costs, political and state issues and mandates, new staffing and training requirements, and equity issues from new boundaries that affect the allocation of livers for already registered candidates.…”

Section: Related Literaturementioning

confidence: 99%

“…The number of liver transplantations is on the rise in recent years, increasing from 13% in 1988 to 23% of all transplants in 2008, and has since been about 25% (Koizumi et al 2014a). The annual number of recovered deceased donor livers has decreased nationwide from 7017 in 2006 to 6608 in 2010, according to data collected by the Organ Procurement and Transplantation Network (OPTN) as stated in Wertheim et al (2011).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Liver‐Candidate Transportation System to Improve Accessibility and Extend Organ Life in Liver Transplantation

Ganesan

Hung

Chen

et al. 2018

INCOSE International Symp

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In the USA, access to liver transplant is determined by the geographical organ allocation boundaries. The current allocation system involves a three‐tier hierarchical boundary system consisting of Organ Procurement Organization (OPO), the United Network for Organ Sharing (UNOS), and the National (USA) boundary. The search for a matching candidate when a liver is available begins within OPOs and moves up to the UNOS and National levels. The boundary‐based allocation system results in several issues such as geographical disparity in access, long system waiting times, liver wastages, high post‐graft failure, and prioritization of less severe candidates that live close to a transplant center over more needy ones from farther away. The paper investigates the conflict between attempts that reduce geographical disparity and those that optimize organ life (results in geographical disparity) through the application of Modeling and Simulation (M&S) to System Engineering. To resolve the above conflict, the paper presents the system architecture of a new hybrid liver‐candidate transport system instead of a liver‐only transport system, and uses discrete event simulation to model and validate the new system.

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“…The current boundary based allocation system gives rise to several issues such as geographical disparity in access, long system waiting times, possible liver wastages due to long transportation time, and giving access to a candidate lower in the priority list than the neediest candidate. By applying simulation optimization, Koizumi et al (2013Koizumi et al ( , 2014 and Feng et al (2013) have demonstrated the effectiveness of mitigating those issues and offering better allocation.…”

Section: Using Simulation Optimization To Manage Liver Transplantmentioning

confidence: 99%

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Huang

Chen

et al. 2015

Asia Pac. J. Oper. Res.

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165

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Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss * Corresponding author. how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.

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Mathematical Optimization and Simulation Analyses for Optimal Liver Allocation Boundaries

Cited by 4 publications

References 28 publications

Allocating kidneys in optimized heterogeneous circles

Allocating kidneys in optimized heterogeneous circles

A Hybrid Liver‐Candidate Transportation System to Improve Accessibility and Extend Organ Life in Liver Transplantation

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

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