KPDGUI: An interactive application for optimization and management of a virtual kidney paired donation program. (May 2019) - Bray, Mathieu; Wang, Wen; Rees, Michael A.; Song, Peter X-K.; Leichtman, Alan B.; Ashby, Valarie B.; Kalbfleisch, John D.
Abstract: Background and objectives: The aim in kidney paired donation (KPD) is typically to maximize the number of transplants achieved through the exchange of donors in a pool comprising incompatible donor-candidate pairs and non-directed (or altruistic) donors. With many possible options in a KPD pool at any given time, the most appropriate set of exchanges cannot be determined by simple inspection. In practice, computer algorithms are used to determine the optimal set of exchanges to pursue. Here, we present our software application, KPDGUI (Kidney Paired Donation Graphical User Interface), for management and optimization of KPD programs. Methods: While proprietary software platforms for managing KPD programs exist to provide solutions to the standard KPD problem, our application implements newly investigated optimization criteria that account for uncertainty regarding the viability of selected transplants and arrange for fallback options in cases where potential exchanges cannot proceed, with intuitive resources for visualizing alternative optimization solutions. Results: We illustrate the advantage of accounting for uncertainty and arranging for fallback options in KPD using our application through a case study involving real data from a paired donation program, comparing solutions produced under different optimization criteria and algorithmic priorities. Conclusions: KPDGUI is a flexible and powerful tool for offering decision support to clinicians and researchers on possible KPD transplant options to pursue under different user-specified optimization schemes. Highlights: Software application for optimization and management of paired donation programs. Implements newly developed paired donation optimization strategies. Strategies account for uncertainty and recourse to fallback options.