90 research outputs found

    Estimating the price of privacy in liver transplantation

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    In the United States, patients with end-stage liver disease must join a waiting list to be eligible for cadaveric liver transplantation. However, the details of the composition of this waiting list are only partially available to the patients. Patients currently have the prerogative to reject any offered livers without any penalty. We study the problem of optimally deciding which offers to accept and which to reject. This decision is significantly affected by the patient's health status and progression as well as the composition of the waiting list, as it determines the chances a patient receives offers. We evaluate the value of obtaining the waiting list information through explicitly incorporating this information into the decision making process faced by these patients. We define the concept of the patient's price of privacy, namely the number of expected life days lost due to a lack of perfect waiting list information.We develop Markov decision process models that examine this question. Our first model assumes perfect waiting list information and, when compared to an existing model from the literature, yields upper bounds on the true price of privacy. Our second model relaxes the perfect information assumption and, hence, provides an accurate representation of the partially observable waiting list as in current practice. Comparing the optimal policies associated with these two models provides more accurate estimates for the price of privacy. We derive structural properties of both models, including conditions that guarantee monotone value functions and control-limit policies, and solve both models using clinical data.We also provide an extensive empirical study to test whether patients are actually making their accept/reject decisions so as to maximize their life expectancy, as this is assumed in our previous models. For this purpose, we consider patients transplanted with living-donor livers only, as considering other patients implies a model with enormous data requirements, and compare their actual decisions to the decisions suggested by a nonstationary MDP model that extends an existing model from the literature

    Optimizing Equitable Resource Allocation in Parallel Any-Scale Queues with Service Abandonment and its Application to Liver Transplant

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    We study the problem of equitably and efficiently allocating an arriving resource to multiple queues with customer abandonment. The problem is motivated by the cadaveric liver allocation system of the United States, which includes a large number of small-scale (in terms of yearly arrival intensities) patient waitlists with the possibility of patients abandoning (due to death) until the required service is completed (matched donor liver arrives). We model each waitlist as a GI/MI/1+GI queue, in which a virtual server receives a donor liver for the patient at the top of the waitlist, and patients may abandon while waiting or during service. To evaluate the performance of each queue, we develop a finite approximation technique as an alternative to fluid or diffusion approximations, which are inaccurate unless the queue's arrival intensity is large. This finite approximation for hundreds of queues is used within an optimization model to optimally allocate donor livers to each waitlist. A piecewise linear approximation of the optimization model is shown to provide the desired accuracy. Computational results show that solutions obtained in this way provide greater flexibility, and improve system performance when compared to solutions from the fluid models. Importantly, we find that appropriately increasing the proportion of livers allocated to waitlists with small scales or high mortality risks improves the allocation equity. This suggests a proportionately greater allocation of organs to smaller transplant centers and/or those with more vulnerable populations in an allocation policy. While our motivation is from liver allocation, the solution approach developed in this paper is applicable in other operational contexts with similar modeling frameworks.Comment: 48 Page

    Unequal Racial Access to Kidney Transplantation

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    ELICITING PATIENT PREFERENCES AND PLACING EXPEDITED ORGANS

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    Liver transplantation plays a crucial role in saving lives when no other alternatives exist. Each year approximately 5,500 liver transplants are performed in the US. However, annually still 2,000 lives are lost due to lack of livers. Much effort has been spent on improving the organ allocation system. In this dissertation, we focus on patient preference elicitation which is an essential component of medical decision models and expedited organ placement which is relatively unexplored component of the organ allocation system. When livers become available, they are offered to patients according to an order (match list) specified by a set of rules. Each patient can accept/reject the offer. Other researchers have considered this accept/decline decision. Estimating patient preferences over health states is an important component of these decision making models. Direct approaches, which involve asking patients abstract uestions, have significant drawbacks. We propose a new approach that infers patient preferences based on observed decisions via inverse optimization techniques. We illustrate our method on the timing of a living-donor liver transplant. If it appears that the standard allocation procedure will not result in a match before the organ becomes nonviable, the liver’s placement can be expedited, meaning that it is offered to a transplant center instead of an individual patient. We study the subsequent decision problem faced by a transplant center, namely which, if any, of its patients should receive the organ independent of their positions on the match list. We develop a simulation model and compare different policies for expedited liver placement. Our study indicates that a policy which gives higher priorities to patients whose likelihood of death is higher performs the best based on several metrics. We also formulate the transplant center’s decision problems as an average reward Markov Decision Process (MDP). Due to the complexity of the model, traditional methods used to solve MDP problems cannot be utilized for our model. Thus, we approximate the solution via Least Square Policy Iteration (LSPI) method. Despite the extensive search on basis functions, the LSPI method yields promising, yet not better outcomes than the policies found to be the best via simulation

    Designing the Liver Allocation Hierarchy: Incorporating Equity and Uncertainty

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    Liver transplantation is the only available therapy for any acute or chronic condition resulting in irreversible liver dysfunction. The liver allocation system in the U.S. is administered by the United Network for Organ Sharing (UNOS), a scientific and educational nonprofit organization. The main components of the organ procurement and transplant network are Organ Procurement Organizations (OPOs), which are collections of transplant centers responsible for maintaining local waiting lists, harvesting donated organs and carrying out transplants. Currently in the U.S., OPOs are grouped into 11 regions to facilitate organ allocation, and a three-tier mechanism is utilized that aims to reduce organ preservation time and transport distance to maintain organ quality, while giving sicker patients higher priority. Livers are scarce and perishable resources that rapidly lose viability, which makes their transport distance a crucial factor in transplant outcomes. When a liver becomes available, it is matched with patients on the waiting list according to a complex mechanism that gives priority to patients within the harvesting OPO and region. Transplants at the regional level accounted for more than 50% of all transplants since 2000.This dissertation focuses on the design of regions for liver allocation hierarchy, and includes optimization models that incorporate geographic equity as well as uncertainty throughout the analysis. We employ multi-objective optimization algorithms that involve solving parametric integer programs to balance two possibly conflicting objectives in the system: maximizing efficiency, as measured by the number of viability adjusted transplants, and maximizing geographic equity, as measured by the minimum rate of organ flow into individual OPOs from outside of their own local area. Our results show that efficiency improvements of up to 6% or equity gains of about 70% can be achieved when compared to the current performance of the system by redesigning the regional configuration for the national liver allocation hierarchy.We also introduce a stochastic programming framework to capture the uncertainty of the system by considering scenarios that correspond to different snapshots of the national waiting list and maximize the expected benefit from liver transplants under this stochastic view of the system. We explore many algorithmic and computational strategies including sampling methods, column generation strategies, branching and integer-solution generation procedures, to aid the solution process of the resulting large-scale integer programs. We also explore an OPO-based extension to our two-stage stochastic programming framework that lends itself to more extensive computational testing. The regional configurations obtained using these models are estimated to increase expected life-time gained per transplant operation by up to 7% when compared to the current system.This dissertation also focuses on the general question of designing efficient algorithms that combine column and cut generation to solve large-scale two-stage stochastic linear programs. We introduce a flexible method to combine column generation and the L-shaped method for two-stage stochastic linear programming. We explore the performance of various algorithm designs that employ stabilization subroutines for strengthening both column and cut generation to effectively avoid degeneracy. We study two-stage stochastic versions of the cutting stock and multi-commodity network flow problems to analyze the performances of algorithms in this context

    MARKOV DECISION PROCESS MODELS FOR IMPROVING EQUITY IN LIVER ALLOCATION

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    In the United States, end-stage liver disease (ESLD) patients are prioritized primarily by their Model for End-stage Liver Disease score (MELD) to receive organ offers. Therefore, patients are required to update their MELD score at predefined frequencies that depend on the patient's last reported score. One aim of this dissertation is to mitigate inequities that stem from patients' flexibility regarding MELD score updates. We develop a Markov decision process (MDP) model to examine the degree to which an individual patient can benefit from the updating flexibility, and provide a menu of updating requirements that balance inequity and data processing more efficiently than the current updating requirements. We also derive sufficient conditions under which a structured optimal updating policy exists. As the coordinator of the harvesting Organ Procurement Organization (OPO) extends offers according to MELD score prioritization, the organ becomes less desirable. To avoid not placing the organ, the OPO coordinator can initiate an expedited placement, i.e., offer the organ to a transplant center, which can then allocate it to any of its patients. A second aim of this dissertation is to mitigate inequities induced by the OPO coordinator's premature departure from the prioritized list of patients via an expedited placement. As a preliminary step to studying the inequity induced by expedited liver placement, we conduct an extensive analysis of the current expedited liver placement practice based on recent data. We investigate different aspects of extending offers, e.g., the number of offers extended concurrently, and patients' response characteristics. Several of the results from this analysis serve as inputs for a second MDP model that examines how many concurrent offers the OPO coordinator should extend and when the coordinator should initiate an expedited placement. Numerical experimentation reveals a structured optimal policy, and we test the sensitivity of the model outcomes with respect to changes in model inputs. Lastly, we examine how our model outputs compare to the analogous measures observed in current practice and how they can be used to improve current practice

    Increasing and Assessing the Impact of Patient Choice in Liver Transplantation

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    In 2009, almost 1,500 Americans died of end-stage liver disease (ESLD), which is the twelfth leading cause of death in the U.S. As liver transplantation is the only possible therapy for ESLD and there is a considerable difference between the number of donated organs and patients, it is important to manage donor-patient match and investigate alternative treatments to transplantation.Every patient lists in at least one waiting list (OPO) in order to be eligible for a donated organ. However, patients may list in additional OPOs. This practice is called multiple listing. Currently, multiple listing is one of the most debated topics in organ allocation.Although transplantation is a successful procedure, it may not be available on time due to the massive shortage of donated organs. Therefore, an alternative therapy to transplantation is needed. Liver Assist Devices (LADs) are an emerging therapy for ESLD that aim to stabilize a patient until transplantation or her own organ recovers.In this dissertation, we discuss three models that are related to ESLD. In the first model, we optimize the three-stage decision process faced by a single patient. The patient decides her geographic location, in which OPOs to multiple list, and which organ offers to accept. This problem is formulated as a continuous-time Markov Decision Process (MDP). We derive structural properties of this model and solve it using clinical data.The second model analyzes multiple listing from the societal perspective. Utilizing an existing simulation of the U.S. liver allocation system, we give every patient the flexibility to multiple list. Therefore, we evaluate the effects of multiple listing on every wait-listed patient, rather than on a single patient. We also study the same problem where multiple listing is a more widespread practice in the U.S.The third model considers a hypothetical system in which an internal LAD is available.So, in addition to the liver accept/reject decision, patients can decide to accept an LAD. This model aims to help manufacturers by estimating potential demand for an LAD. We model this problem as a discrete-time MDP and give sufficient conditions under which an LAD will be worthwhile

    Heart and lung organ offer acceptance practices of transplant programs are associated with waitlist mortality and organ yield

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    Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145354/1/ajt14885.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145354/2/ajt14885_am.pd

    An investigation into the functional and psychosocial impact of living organ donation

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    General Abstract Objective: In April 2006, the Scottish Liver Transplant Unit (SLTU) became the first NHS transplant unit in the UK to offer the option of Living Donor Liver Transplantation (LDLT). This represented a unique opportunity to evaluate the functional and psychosocial impact of LDLT upon healthy donors and their recipients. Subsequent aims were to investigate the challenge of introducing LDLT in Scotland and to establish the perceived deterrents and attractions of the procedure. An additional aim was to evaluate the impact of Living Donor Kidney Transplantation (LDKT) upon donors and recipients. Design: A series of cross sectional and longitudinal studies were designed for the purpose of this thesis (3 quantitative, 2 qualitative, and 1 mixed methods). Method: Self report questionnaires were used in each of the quantitative studies, with the addition of neuropsychological computerized tests in two studies. Semi-structured interviews were employed in the qualitative studies. Main Findings: •Prior to its introduction general support for the option of LDLT was found, although it was highlighted that the risk involved was not well understood by the general public. •Since becoming available LDLT has not been a readily acceptable treatment option from the perspective of patients due to the perceived risk for the donor, but it may be considered as a “last option”. Family members were motivated to save their loved one’s life but the personal implications of donating resulted in reconsideration of LDLT. • Staff at the SLTU perceived a lack of family commitment in relation to LDLT, which is explained as a cultural factor contributing to the slow uptake of LDLT. In Scotland, a donation from a younger to an older generation is not easily accepted. This, in addition to patients’ optimism that a deceased donation will arrive, and the poor health of potential donors, is thought to have affected the uptake of LDLT. As has the unit’s conservative approach to the promotion of LDLT. This approach is the result of a perceived reduction in the need for LDLT and a preference to avoid the risk to a healthy donor and conduct transplants with deceased donations. • In over 3 years, only one couple completed LDLT. The recipient showed functional and psychosocial improvement from pre to post procedure, whilst the donor showed slight deterioration in aspects of quality of life 6 weeks post donation, which did not always completely return to a baseline level by 6 months. The donor made sacrifices to provide her husband with a fresh start to life and unmet expectations were found to effect quality of life. •Willingness to become a liver donor is not thought to be influenced by the frame of the information provided. •Like the LDLT donor, LDKT donors experience some functional and psychosocial deterioration at 6 weeks post donation, but donors largely recover by 6 months post donation. However, the anticipated benefit to recipients was not evident and may not be quantifiable until after 6 months post operation. Conclusion: This thesis has added to current knowledge on living organ donation and specifically represents the first psychological evaluation of a UK LDLT programme. The slow uptake of LDLT was unexpected and has resulted in informative, novel research
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