A Data-Driven Approach towards Improving Deceased-Donor Kidney Utilization

Aditya Shankar Garg, Itai Feigenbaum, Miko E. Yu, Sumit Mohan, Jay Sethuraman
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:521-549, 2026.

Abstract

Deceased donor kidneys in the United States remain substantially underutilized: Approximately 30% of recovered kidneys are discarded, and many other kidneys are transplanted in suboptimal condition due to delays. A major contributing factor is the allocation procedure, which offers kidneys sequentially to patients in priority order (“match run"). Offers are often declined, and repeated declines delay transplant, prolong cold ischemia time, and increase the risk of discarding the kidney. We propose a machine-learning framework to identify kidneys at risk of underutilization. Such a framework can be used to expedite the offering process and shorten the time lost due to strings of declines. We use only medical and historical information available upon arrival of the donor to the system. On the dataset of U.S. kidney offers, we predict discards well (AUROC 0.993, AUPRC 0.977). Furthermore, we are able to accurately identify 86% of the kidneys at the risk of underutilization. Our framework consists of three models, which are each an ensemble of decision trees. The first model, OfferPred, directly evaluates the chances of an offer acceptance for a particular kidney-patient pair; however, applying this model directly to evaluate an entire match run leads to error accumulation. Therefore, we train models DiscardPred and LocationPred, which indirectly use OfferPred to predict the chance of discard and identify some of the initial string of declines. Remarkably, DiscardPred and LocationPred do not directly use granular information about patients and transplant centers in the offer sequence, but rather replace these with aggregate data from OfferPred’s input. Our models shows that underutilization risk is not solely a function of donor characteristics but is strongly correlated with the structure of the match run and recent historical center behavior.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-garg26a, title = {A Data-Driven Approach towards Improving Deceased-Donor Kidney Utilization}, author = {Garg, Aditya Shankar and Feigenbaum, Itai and Yu, Miko E. and Mohan, Sumit and Sethuraman, Jay}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {521--549}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/garg26a/garg26a.pdf}, url = {https://proceedings.mlr.press/v340/garg26a.html}, abstract = {Deceased donor kidneys in the United States remain substantially underutilized: Approximately 30% of recovered kidneys are discarded, and many other kidneys are transplanted in suboptimal condition due to delays. A major contributing factor is the allocation procedure, which offers kidneys sequentially to patients in priority order (“match run"). Offers are often declined, and repeated declines delay transplant, prolong cold ischemia time, and increase the risk of discarding the kidney. We propose a machine-learning framework to identify kidneys at risk of underutilization. Such a framework can be used to expedite the offering process and shorten the time lost due to strings of declines. We use only medical and historical information available upon arrival of the donor to the system. On the dataset of U.S. kidney offers, we predict discards well (AUROC 0.993, AUPRC 0.977). Furthermore, we are able to accurately identify 86% of the kidneys at the risk of underutilization. Our framework consists of three models, which are each an ensemble of decision trees. The first model, OfferPred, directly evaluates the chances of an offer acceptance for a particular kidney-patient pair; however, applying this model directly to evaluate an entire match run leads to error accumulation. Therefore, we train models DiscardPred and LocationPred, which indirectly use OfferPred to predict the chance of discard and identify some of the initial string of declines. Remarkably, DiscardPred and LocationPred do not directly use granular information about patients and transplant centers in the offer sequence, but rather replace these with aggregate data from OfferPred’s input. Our models shows that underutilization risk is not solely a function of donor characteristics but is strongly correlated with the structure of the match run and recent historical center behavior.} }
Endnote
%0 Conference Paper %T A Data-Driven Approach towards Improving Deceased-Donor Kidney Utilization %A Aditya Shankar Garg %A Itai Feigenbaum %A Miko E. Yu %A Sumit Mohan %A Jay Sethuraman %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-garg26a %I PMLR %P 521--549 %U https://proceedings.mlr.press/v340/garg26a.html %V 340 %X Deceased donor kidneys in the United States remain substantially underutilized: Approximately 30% of recovered kidneys are discarded, and many other kidneys are transplanted in suboptimal condition due to delays. A major contributing factor is the allocation procedure, which offers kidneys sequentially to patients in priority order (“match run"). Offers are often declined, and repeated declines delay transplant, prolong cold ischemia time, and increase the risk of discarding the kidney. We propose a machine-learning framework to identify kidneys at risk of underutilization. Such a framework can be used to expedite the offering process and shorten the time lost due to strings of declines. We use only medical and historical information available upon arrival of the donor to the system. On the dataset of U.S. kidney offers, we predict discards well (AUROC 0.993, AUPRC 0.977). Furthermore, we are able to accurately identify 86% of the kidneys at the risk of underutilization. Our framework consists of three models, which are each an ensemble of decision trees. The first model, OfferPred, directly evaluates the chances of an offer acceptance for a particular kidney-patient pair; however, applying this model directly to evaluate an entire match run leads to error accumulation. Therefore, we train models DiscardPred and LocationPred, which indirectly use OfferPred to predict the chance of discard and identify some of the initial string of declines. Remarkably, DiscardPred and LocationPred do not directly use granular information about patients and transplant centers in the offer sequence, but rather replace these with aggregate data from OfferPred’s input. Our models shows that underutilization risk is not solely a function of donor characteristics but is strongly correlated with the structure of the match run and recent historical center behavior.
APA
Garg, A.S., Feigenbaum, I., Yu, M.E., Mohan, S. & Sethuraman, J.. (2026). A Data-Driven Approach towards Improving Deceased-Donor Kidney Utilization. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:521-549 Available from https://proceedings.mlr.press/v340/garg26a.html.

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