Identify Hard-To-Place Kidneys for Early Engagement in Accelerated Placement with a Deep Learning Optimization Approach
Abstract
Recommended practices that follow match-run sequences for hard-to-place kidneys succumb to many declines, accruing cold ischemic time and exacerbating kidney quality that may lead to unnecessary kidney discard. Hard-to-place deceased donor kidneys accepted and transplanted later in the match-run sequence may threaten higher graft failure rates. Accelerated placement is a practice for organ procurement organizations (OPOs) to allocate high-risk kidneys out of sequence and reach patients at aggressive transplant centers. The current practice of assessing hard-to-place kidneys and engaging in accelerated kidney placements relies heavily on the kidney donor profile index (KDPI) and the number of declines. Although this practice is reasonable, it also accrues cold ischemic time and increases the risk for kidney discard. We use a deep learning optimization approach to quickly identify kidneys at risk for discard. This approach uses Organ Procurement and Transplantation Network data to model kidney disposition. We filter discards and develop a model to predict transplant and discard of recovered and not transplanted kidneys. Kidneys with a higher probability of discard are deemed hard-to-place kidneys, which require early engagement for accelerated placement. Our approach will aid in identifying hard-to-place kidneys before or after procurement and support OPOs to deviate from the match-run for accelerated placement. Compared with the KDPI-only prediction of the kidney disposition, our approach demonstrates a 10% increase in correctly predicting kidneys at risk for discard. Future work will include developing models to identify candidates with an increased benefit from using hard-to-place kidneys.
Recommended Citation
L. Ashiku and C. H. Dagli, "Identify Hard-To-Place Kidneys for Early Engagement in Accelerated Placement with a Deep Learning Optimization Approach," Transplantation Proceedings, Elsevier, Jan 2023.
The definitive version is available at https://doi.org/10.1016/j.transproceed.2022.12.005
Department(s)
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
1873-2623; 0041-1345
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Elsevier, All rights reserved.
Publication Date
01 Jan 2023
PubMed ID
36641350
