Kidney exchange in dynamic sparse heterogenous pools

Efficient Kidney Exchange with Dichotomous Preferences

Sir: Matas and colleagues [3] recently presented an interesting option for dealing with the scarcity of transplantable organs. The Minnesota transplantation program has implemented a policy of nondirected donation that allows altruistic strangers to donate a kidney. Acceptance would likely be high on the recipients’ side. In the extensive interviews which we have conducted as part of our follow-up study on recipients of kidneys from living donors, many deplore the fact that the German Transplantation Law of 1997 precludes anonymous or nondirected donation (the law requires an “obviously close personal relationship with the prospective recipient” (§ 8.1)). The well-known case of a German transplant surgeon who donated a kidney to an unknown patient in 1996 [l] is often mentioned in this context. Whereas the patients in our study categorically reject any form of organ trading, most of them don’t see why an altruistic donation should not be possible. The challenge will be to see if one can be had without the other. A possible advantage of nondirected living donation is that it excludes pressure on the donor resulting from the relationship to the prospective recipient or family members. In genetically or emotionally related living organ donation, decisions are always made within the context of a family system, even when no overt pressure is exerted. In our study we noted a significant gender imbalance, with mothers, wives, and sisters donating much more frequently than their male counterparts. The same tendency can be found in the national figures for living donation in Germany (63 % female vs 37 % male living kidney donors in 1998) and in the United States (UNOS reports 58 % female vs 42 % male living kidney donors in 1998). The decisions of these women may well be motivated by their role within the family as the caring, supporting ones. It shall be interesting to see whether the same gender pattern will appear in the Minnesota program or whether it will elicit a gender-neutral or even predominantly male response. As has been pointed out rightly [2], there are procedural aspects, like financial compensation or the selection process of potential donors, that need to be doublechecked for ethical pitfalls. Certainly protection from exploitation has to come first; pressuring people into living organ donation by whatever means or arguments is unacceptable. But given a satisfactory regulatory framework could be worked out, the national laws and professional guidelines prohibiting nondirected organ donation might have to be reconsidered.

Kidney exchange programs (KEPs) allow the exchange of kidneys between incompatible donor-recipient pairs. Optimization approaches can help KEPs in defining which transplants should be made among all incompatible pairs according to some objective. The most common objective is to maximize the number of transplants. In this paper, we propose an integer programming model which addresses the objective of maximizing the expected number of transplants, given that there are equal probabilities of failure associated with vertices and arcs. The model is compact, i.e. has a polynomial number of decision variables and constraints, and therefore can be solved directly by a general purpose integer programming solver (e.g. Cplex).

To this day, there are still some countries where the exchange of kidneys between multiple incompatible patient-donor pairs is restricted by law. Typically, legal regulations in this context are put in place to prohibit coercion and manipulation in order to prevent a market for organ trade. Yet, in countries where kidney exchange is practiced, existing platforms to facilitate such exchanges generally lack sufficient privacy mechanisms. In this paper, we propose a privacy-preserving protocol for kidney exchange that not only addresses the privacy problem of existing platforms but also is geared to lead the way in overcoming legal issues in those countries where kidney exchange is still not practiced. In our approach, we use the concept of secret sharing to distribute the medical data of patients and donors among a set of computing peers in a privacy-preserving fashion. These computing peers then execute our new Secure Multi-Party Computation (SMPC) protocol among each other to determine an optimal set of kidney exchanges. As part of our new protocol, we devise a privacy-preserving solution to the maximum matching problem on general graphs. We have implemented the protocol in the SMPC benchmarking framework MP-SPDZ and provide a comprehensive performance evaluation. Furthermore, we analyze the practicality of our protocol when used in a dynamic setting (where patients and donors arrive and depart over time) based on a data set from the United Network for Organ Sharing.

Kidney paired donation (KPD) is increasing the number of living donor transplants. Two major obstacles prevent moving KPD forward in the United States: (1) achieving a critical mass of pairs to efficiently find matches and (2) efficiently coordinating KPD transplants between multiple transplant centers. Two large regional programs, The New England Program for Kidney Exchange (NEPKE) and the Mid-Atlantic Paired Exchange Program (MAPEP) have developed a system of protocols to effectively increase the number of KPD transplants. Incompatible pairs and nondirected donors (NDD) are referred to the system through transplant centers. Donor and recipient ABO, human leukocyte antigen, and recipient human leukocyte antigen antibody screening are used to determine potential matches. Utilization of a computer optimization algorithm matches pairs in two- and three-way exchanges, NDD chains, and list exchange chains. Team conference calls regarding transfer of information, crossmatches, surgery date, coordination of simultaneous donor nephrectomies, and other issues are coordinated as needed. Ten matches moved forward to donation and transplantation, and one is pending. Eight of these matches involved NDD chains, two 2-way exchanges, and 1 a list exchange chain. These matches resulted in 27 transplants. Eighteen transplants occurred in NEPKE-only transplant centers, four in MAPEP-only centers, and an additional five were crossregional. The collaboration of NEPKE and MAPEP has demonstrated that crossregional coordination is feasible and expands the number of transplants performed beyond the capability of either program alone, especially when combined with computer optimization and multiple-type matches of three-way, NDD chains, and list exchange chains.

In kidney exchange programs, multiple patient-donor pairs each of whom are otherwise incompatible, exchange their donors to receive compatible kidneys. The Kidney Exchange problem is typically modelled as a directed graph where every vertex is either an altruistic donor or a pair of patient and donor; directed edges are added from a donor to its compatible patients. The computational task is to find if there exists a collection of disjoint cycles and paths starting from altruistic donor vertices of length at most $\ell_c$ and $\ell_p$ respectively that covers at least some specific number t of non-altruistic vertices (patients). We study parameterized algorithms for the kidney exchange problem in this paper. Specifically, we design FPT algorithms parameterized by each of the following parameters: (1) the number of patients who receive kidney, (2) treewidth of the input graph + $\max\\ell_p,\ell_c\ $, and (3) the number of vertex types in the input graph when $\ell_płeq \ell_c$. We also present interesting algorithmic and hardness results on the kernelization complexity of the problem. Finally, we present an approximation algorithm for an important special case of Kidney Exchange.

In kidney exchange programs, multiple patient-donor pairs each of whom are otherwise incompatible, exchange their donors to receive compatible kidneys. The Kidney Exchange problem is typically modelled as a directed graph where every vertex is either an altruistic donor or a pair of patient and donor; directed edges are added from a donor to its compatible patients. The computational task is to find if there exists a collection of disjoint cycles and paths starting from altruistic donor vertices of length at most l_c and l_p respectively that covers at least some specific number t of non-altruistic vertices (patients). We study parameterized algorithms for the kidney exchange problem in this paper. Specifically, we design FPT algorithms parameterized by each of the following parameters: (1) the number of patients who receive kidney, (2) treewidth of the input graph + max{l_p, l_c}, and (3) the number of vertex types in the input graph when l_p <= l_c. We also present interesting algorithmic and hardness results on the kernelization complexity of the problem. Finally, we present an approximation algorithm for an important special case of Kidney Exchange.

P170Aims:Kidney exchange (KE) among incompatible pairs is an organizational solution to increase the living donation rate. It saves lives and it is extensively used in countries with low cadaver donation rate. Legislation could obstruct. Any ethical concern about KE has a potential solution. The aim

Kidney exchange, where needy patients swap incompatible donors with each other, offers a lifesaving alternative to waiting for an organ from the deceased-donor waiting list. Recently, chains---sequences of transplants initiated by an altruistic kidney donor---have shown marked success practice, yet remain poorly understood. We provide a theoretical analysis of the efficacy of chains the most widely used kidney exchange model, proving that long chains do not help beyond chains of length of 3 the This completely contradicts our real-world results gathered from the budding nationwide kidney exchange the United States; there, solution quality improves by increasing the chain length cap to 13 or beyond. We analyze reasons for this gulf between theory and practice, motivated by our experiences running the only nationwide kidney exchange. We augment the standard kidney exchange model to include a variety of real-world features. Experiments the static setting support the theory and help determine how large is really in the large. Experiments the dynamic setting cannot be conducted the large due to computational limitations, but with up to 460 candidates, a chain cap of 4 was best (in fact, better than 5).

ObjectivesLiving donor kidney transplantation (LTx) is the preferred treatment for patients with end-stage renal disease. Kidney exchange programs (KEPs) promote LTx by facilitating exchange of donors among patients who are not compatible with their donors. We analyze and maximize the efficacy and effectiveness of KEPS from a health value perspective and the health value of altruistic donation in KEPs. MethodsWe developed a Markov model for the health outcomes of patients, which was embedded in a discrete event simulation model to assess the effectiveness of allocation policies in KEPs. A new allocation policy to maximize health value was developed on the basis of integer programing techniques. The evidence-based transition probabilities in the Markov model were based on data from the Dutch KEP using a variety of econometric models. Scenarios analysis was presented to improve robustness. ResultsThe efficacy of the Dutch KEP without altruistic donation is reflected by the increase in expected discounted quality-adjusted life-years (QALYs) by 3.23 from 6.42 to 9.65. The present Dutch policy and the policy to maximize the number of transplants achieve 63% of the potential efficacy gain (2.11 discounted QALYs). The new policy achieves 69% of this gain (2.33 discounted QALYs). When systematically enrolling altruistic donors in the KEP, the new policy increased expected discounted QALYs by 4.05 to 10.27 and reduced inequities for patients with blood type O. ConclusionsThe Dutch KEP can increase health value for patients by more than half. An allocation policy that maximizes health outcomes and maximally allows altruistic donation can yield significant further improvements.

The preferred treatment for kidney failure is a transplant; however, demand for donor kidneys far outstrips supply. Kidney exchange, an innovation where willing but incompatible patient-donor pairs can exchange organs- — via barter cycles and altruist-initiated chains —provides a life-saving alternative.Typically, fielded exchanges act myopically, considering only the current pool of pairs when planning the cycles and chains. Yet kidney exchange is inherently dynamic, with participants arriving and departing. Also, many planned exchange transplants do not go to surgery due to various failures. So, it is important to consider the future when matching. Motivated by our experience running the computational side of a large nationwide kidney exchange, we present FutureMatch, a framework for learning to match in a general dynamic model. FutureMatch takes as input a high-level objective (e.g., "maximize graft survival of transplants over time'') decided on by experts, then automatically (i) learns based on data how to make this objective concrete and (ii) learns the ``means'' to accomplish this goal — a task, in our experience, that humans handle poorly. It uses data from all live kidney transplants in the US since 1987 to learn the quality of each possible match; it then learns the potentials of elements of the current input graph offline (e.g., potentials of pairs based on features such as donor and patient blood types), translates these to weights, and performs a computationally feasible batch matching that incorporates dynamic, failure-aware considerations through the weights. We validate FutureMatch on real fielded exchange data. It results in higher values of the objective. Furthermore, even under economically inefficient objectives that enforce equity, it yields better solutions for the efficient objective (which does not incorporate equity) than traditional myopic matching that uses the efficiency objective.

This article studies multicenter coordination of unspecified living kidney donation and transplantation across the blood-type barrier in kidney exchange. Important questions are whether such coordination should use domino paired donation or non simultaneous extended altruistic donor chains, what the length of the segments in such chains should be, when they should be terminated, and how much time should be allowed between matching rounds. Furthermore, it is controversial whether the different modalities should be coordinated centrally or locally and independently. Kidney exchange policies are simulated using actual data from the Dutch national kidney exchange program. Sensitivity analysis is performed on the composition of the population, the time unspecified and bridge donors wait before donating to the wait list, the time between matching rounds, and donor renege rates. Central coordination of unspecified donation and transplantation across the blood-type barrier can increase transplants by 10% (PG0.001). Especially highly sensitized and blood type O patients benefit. Sufficient time between matching rounds is essential: three-monthly exchanges result in 31% more transplants than weekly exchanges. Benefits of non simultaneous extended altruistic donor chains are limited in case of low numbers of highly sensitized patients and sufficient unspecified donors. Chains are best terminated when no further segment is part of an optimal exchange within 3 months. There is clear synergy in the central coordination of both unspecified donation and transplantation across the blood-type barrier in kidney exchange. The best configuration of a national program depends on the composition of the patient Y donor population.

Living donor kidney transplantation is the preferred treatment for patients suffering from end-stage renal disease. To alleviate the shortage of kidney donors, many advances have been made to improve the utilization of living donors deemed incompatible with their intended recipient. The most prominent of these advances is kidney paired donation (KPD), which matches incompatible patient-donor pairs to facilitate a kidney exchange. This review discusses the various approaches to matching and allocation in KPD. In particular, it focuses on the underlying principles of matching and allocation approaches, the combination of KPD with other strategies such as ABO incompatible transplantation, the organization of KPD, and important future challenges. As the transplant community strives to balance quantity and equity of transplants to achieve the best possible outcomes, determining the right long-term allocation strategy becomes increasingly important. In this light, challenges include making full use of the various modalities that are now available through integrated and optimized matching software, encouragement of transplant centers to fully participate, improving transplant rates by focusing on the expected long-run number of transplants, and selecting uniform allocation criteria to facilitate international pools.

Domino paired kidney donation: a strategy to make best use of live non-directed donation

<p>The problem of evacuating two robots from the disk in the face-to-face model was first introduced by Czyzowicz et al. [DISC’2014], and has been extensively studied (along with many variations) ever since with respect to worst-case analysis. We initiate the study of the same problem with respect to average-case analysis, which is also equivalent to designing randomized algorithms for the problem. In particular, we introduce constrained optimization problem <sub>2</sub>Evac<sub>𝐹2𝐹</sub>, in which one is trying to minimize the average-case cost of the evacuation algorithm given that the worst-case cost does not exceed <em>w</em>. The problem is of special interest with respect to practical applications, since a common objective in search-and-rescue operations is to minimize the average completion time, given that a certain worst-case threshold is not exceeded, e.g., for safety or limited energy reasons. Our main contribution is the design and analysis of families of new evacuation parameterized algorithms which can solve <sub>2</sub>Evac<sub>𝐹2𝐹</sub>, for every <em>w</em> for which the problem is feasible. Notably, the worst-case analysis of the problem, since its introduction, has been relying on technical numerical, computer-assisted calculations, following tedious robot trajectory analysis. Part of our contribution is a novel systematic procedure, which given <em>any evacuation algorithm</em>, can derive its worst- and average-case performance in a clean and unified way. </p>