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An efficient algorithm to enumerate sets with fallbacks in a kidney paired donation program.

Wen Wang1, Mathieu Bray1, Peter X K Song1,2

  • 1Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, USA.

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|March 12, 2019
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Summary
This summary is machine-generated.

Kidney paired donation (KPD) programs can improve transplant success by identifying optimal patient-donor pairs. This study introduces locally relevant (LR) subgraphs and an efficient algorithm to find them, increasing realized kidney transplants.

Keywords:
Breadth-first searchFallback optionsKidney paired donationLocally relevant subgraphNon-directed donor

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Area of Science:

  • Nephrology
  • Transplantation Surgery
  • Operations Research

Background:

  • Kidney paired donation (KPD) facilitates transplants between biologically incompatible donor-recipient pairs.
  • KPD programs involve altruistic non-directed donors (NDDs) and incompatible pairs.
  • Transplant success is limited by biological incompatibility and potential transplant failures.

Purpose of the Study:

  • To define and efficiently enumerate locally relevant (LR) subgraphs within KPD programs.
  • To improve the selection of potential transplants by considering fallback options.
  • To enhance the number of realized kidney transplants.

Main Methods:

  • Modeling KPD programs as directed graphs with vertices representing NDDs and pairs, and edges representing potential transplants.
  • Associating failure probabilities with vertices and edges.
  • Defining and developing an efficient algorithm for enumerating locally relevant (LR) subgraphs.

Main Results:

  • The proposed algorithm for enumerating LR subgraphs is computationally efficient.
  • LR subgraphs provide a framework for selecting disjoint subgraphs with fallback options.
  • This approach is expected to generate more realized transplants compared to previous methods.

Conclusions:

  • Locally relevant (LR) subgraphs offer a more effective strategy for KPD program optimization.
  • The developed algorithm significantly improves computational efficiency for subgraph enumeration.
  • This research contributes to increasing kidney transplant rates through improved KPD strategies.