Optimal Kidney Exchange with Immunosuppressants

TL;DR

This paper introduces a computational framework and efficient algorithms for optimizing kidney exchanges that incorporate immunosuppressant use, enabling compatibility across incompatible donor-recipient pairs and improving transplant success rates.

Contribution

It presents a novel, scalable approach to optimal kidney exchange considering immunosuppressants, expanding beyond standard compatibility constraints.

Findings

Algorithms achieve flexible objectives in kidney exchange optimization.

Validated approach on realistic large-scale exchange data.

Improved compatibility and transplant success potential.

Abstract

Algorithms for exchange of kidneys is one of the key successful applications in market design, artificial intelligence, and operations research. Potent immunosuppressant drugs suppress the body's ability to reject a transplanted organ up to the point that a transplant across blood- or tissue-type incompatibility becomes possible. In contrast to the standard kidney exchange problem, we consider a setting that also involves the decision about which recipients receive from the limited supply of immunosuppressants that make them compatible with originally incompatible kidneys. We firstly present a general computational framework to model this problem. Our main contribution is a range of efficient algorithms that provide flexibility in terms of meeting meaningful objectives. Motivated by the current reality of kidney exchanges using sophisticated mathematical-programming-based clearing algorithms, we then present a general but scalable approach to optimal clearing with immunosuppression; we validate our approach on realistic data from a large fielded exchange.