Accept or Decline? An Analytics-Based Decision Tool for Kidney Offer Evaluation.

Background: When a deceased-donor kidney is offered to a waitlisted candidate, the decision to accept or decline the organ relies primarily upon a practitioner's experience and intuition. Such decisions must achieve a delicate balance between estimating the immediate benefit of transplantation and the potential for future higher-quality offers. However, the current experience-based paradigm lacks scientific rigor and is subject to the inaccuracies that plague anecdotal decision-making. Methods: A data-driven analytics-based model was developed to predict whether a patient will receive an offer for a deceased-donor kidney at KDPI thresholds of 0.2, 0.4, and 0.6, and at time frames of 3, 6, and 12 months. The model accounted for OPO, blood group, wait time, DR antigens, and prior offer history to provide accurate and personalized predictions. Performance was evaluated on datasets spanning various lengths of time to understand the adaptability of the method. Results: Using UNOS match-run data from 03/2007 to 06/2013, out-of-sample AUC was approximately 0.87 for all KDPI thresholds and time frames considered for the 10 most populous OPOs. As more data becomes available, AUC values increase and subsequently level off. Conclusions: The development of a data-driven analytics-based model may assist transplant practitioners and candidates during the complex decision of whether to accept or forgo a current kidney offer in anticipation of a future high-quality offer. The latter holds promise to facilitate timely transplantation and optimize the efficiency of allocation. Copyright (C) 2017 Wolters Kluwer Health, Inc. All rights reserved.

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