Improving hard-to-place kidney allocation: A machine learning approach to center ranking.

Kidney transplantation is the preferred treatment for end-stage renal disease, yet donor scarcity and inefficiencies in allocation systems create major bottlenecks, resulting in prolonged wait times and alarming mortality rates. Despite the severe shortage of donor kidneys, timely and effective interventions to prevent non-utilization of life-saving organs remain limited. Expedited out-of-sequence placement of hard-to-place kidneys to centers with a high likelihood of acceptance has been recommended in the literature as a strategy to improve placement success. However, in practice, this process remains nonstandardized and relies heavily on the subjective judgment of decision-makers. We propose a data-driven, machine learning-based ranking policy for out-of-sequence allocation of hard-to-place kidneys that prioritizes transplant centers using predicted center-level acceptance probabilities. Using national deceased-donor and kidney-offer data, we construct a unique offer-level dataset with donor- and center-specific features. We also employ machine learning interpretability tools to provide insight into the factors influencing kidney allocation decisions. Our analysis demonstrates that the proposed policy can reduce the average number of centers considered before placement by fourfold for all kidneys and tenfold for the subset of hard-to-place kidneys. These results highlight the potential of the proposed framework to improve the efficiency of expedited placement and support more timely utilization of hard-to-place kidneys.

Duke Scholars

Author Matthew Jay Ellis Medicine, Nephrology