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

Abstract:

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 Kidney Donor Profile Index thresholds of 0.2, 0.4, and 0.6, and at timeframes of 3, 6, and 12 months. The model accounted for Organ Procurement Organization, blood group, wait time, DR antigens, and prior offer history to provide accurate and personalized predictions. Performance was evaluated on data sets spanning various lengths of time to understand the adaptability of the method. RESULTS:Using United Network for Organ Sharing match-run data from March 2007 to June 2013, out-of-sample area under the receiver operating characteristic curve was approximately 0.87 for all Kidney Donor Profile Index thresholds and timeframes considered for the 10 most populous Organ Procurement Organizations. As more data becomes available, area under the receiver operating characteristic curve 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.

journal_name

Transplantation

journal_title

Transplantation

authors

Bertsimas D,Kung J,Trichakis N,Wojciechowski D,Vagefi PA

doi

10.1097/TP.0000000000001824

subject

Has Abstract

pub_date

2017-12-01 00:00:00

pages

2898-2904

issue

12

eissn

0041-1337

issn

1534-6080

journal_volume

101

pub_type

杂志文章
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    doi:

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    pub_type: 杂志文章

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    doi:10.1097/00007890-200102270-00006

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    pub_type: 杂志文章

    doi:

    authors: Yuzawa K,Kondo I,Fukao K,Iwasaki Y,Hamaguchi H

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