Equity or Equality? Which Approach Brings More Satisfaction in a Kidney-Exchange Chain?

In United States (U.S.), government-funded organizations, such as NLDAC, reimburse travel and subsistence expenses incurred during living-organ donation process. However, in Iran, there is a non-governmental organization called Iranian Kidney Foundation (IKF) that funds the direct and indirect costs of donors through charitable donations and contributions from participants in the exchange program. In this article, for countries outside the U.S. that currently use an equality approach, we propose a potential new compensation-apportionment approach (equitable approach) for kidney-exchange chains and compare it with the currently available system (equality approach) in terms of the apportionment of compensation in a kidney-exchange chain to cover the expenses incurred by the initiating living donor of the chain in the act of donation. To this end, we propose a mechanism to apportion compensation among all participating pairs based on the equity approach by utilizing a prediction model to calculate the probability of graft survival in each transplant operation. These probabilities are then used to define the utility of any transplantation, considering the quality of each pair’s donated and received kidney in the chain. Afterward, the corresponding cost is apportioned by a mechanism based on the normalized differences between the utility of donated and received kidneys for each incompatible pair of the chain. In summary, we demonstrate that by utilizing the equitable approach, there is more fairness and equity in the allocation of resources in organ-procurement systems, which results in more satisfaction among incompatible pairs. Additional future prospective studies are needed to assess this proposed equitable approach for kidney-exchange chains in countries outside the U.S., such as Iran, that currently use an equality approach.

Recourse in Kidney Exchange Programs

We introduce the problem of selecting patient-donor pairs in a kidney exchange program to undergo a crossmatch test, and we model this selection problem as a two-stage stochastic integer programming problem. The optimal solutions of this new formulation yield a larger expected number of realized transplants than previous approaches based on internal recourse or subset recourse. We settle the computational complexity of the selection problem by showing that it remains NP-hard even for maximum cycle length equal to two. Furthermore, we investigate to what extent different algorithmic approaches, including one based on Benders decomposition, are able to solve instances of the model. We empirically investigate the computational efficiency of this approach by solving randomly generated instances and study the corresponding running times as a function of maximum cycle length, and of the presence of nondirected donors. Summary of Contribution: This paper deals with an important and very complex issue linked to the optimization of transplant matchings in kidney exchange programs, namely, the inherent uncertainty in the assessment of compatibility between donors and recipients of transplants. Although this issue has previously received some attention in the optimization literature, most attempts to date have focused on applying recourse to solutions selected within restricted spaces. The present paper explicitly formulates the maximization of the expected number of transplants as a two-stage stochastic integer programming problem. The formulation turns out to be computationally difficulty, both from a theoretical and from a numerical perspective. Different algorithmic approaches are proposed and tested experimentally for its solution. The quality of the kidney exchanges produced by these algorithms compares favorably with that of earlier models.

Global kidney chains

Kidney failure is a worldwide scourge, made more lethal by the shortage of transplants. We propose a way to organize kidney exchange chains internationally between middle-income countries with financial barriers to transplantation and high-income countries with many hard to match patients and patient–donor pairs facing lengthy dialysis. The proposal involves chains of exchange that begin in the middle-income country and end in the high-income country. We also propose a way of financing such chains using savings to US health care payers.

Paired Exchanges, Chain Donations, and Organ Markets

This chapter introduces the use of an innovation called “paired exchange,” a way to encourage donations even when there is no match. The chapter shows a graphic presentation to simply describe the idea of paired donation exchange. It explains the risks of paired exchange for the donors' and recipients' perspective, arguing that the risks were the same from the donors' perspective, while the outcomes from the recipients' perspective would be much better as a result of receiving a histocompatible organ than they would be if they received their own designated recipient's organ. The chapter also offers some legal questions after lawyers wondered whether a paired kidney exchange was a sort of barter and thus the beginning of a gray market in organs. Ultimately, the chapter looks at another suggestion of creating a serial chain of donor–recipient pairs, with the world's first kidney–liver swap took place in 2017.

Binary Particle Swarm Optimization Algorithm for Kidney Exchanges Acceleration using Parallel MATLAB

In this paper, we implement a new method binary Particle Swarm Optimization (PSO) for solving the kidney exchange problem, which will improve the future decisions of kidney exchange programs. Because using a kidney exchange, we can help incompatible patient-donor couples to swap donors to receive a compatible kidney. Kidney paired donation programs provide an innovative approach for increasing the number of available kidneys. Further, we implementing binary particle swarm optimization in parallel with MATLAB with one, two, three and four threads and from the computations point of view, the authors compare the performance to reduce the running time for kidney exchange to match patients as fast as possible to help clinicians. Moreover, implementing binary particle swarm optimization in solving the kidney exchange problem is an effective method. The obtained results indicate that binary PSO outperforms other stochastic-based methods such as genetic algorithm, ant lion optimization, and efficient the number of resulting exchanges.