Predicting Offer Burden to Optimize Batch Sizes in Simultaneously Expiring Kidney Offers

Sean Berry¹Berk Görgülü^2*Sait Tunç³Mucahit Cevik¹

• ¹Department of Mechanical, Industrial and Mechatronics Engineering, Toronto Metropolitan University, Toronto, Canada
• ²DeGroote School of Business, McMaster University, Hamilton, Canada
• ³Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, United States

Background: Timely and efficient allocation of deceased donor kidneys is a persistent challenge in transplantation. Traditional sequential offer systems often lead to extended delays and high nonuse rates, as many kidneys undergo multiple refusals before being accepted. Simultaneously expiring offers, where a kidney is offered to a batch of centers with synchronized response deadlines, offer a more efficient alternative. However, fixed batch sizes fail to account for variability in offer requirements, potentially introducing new inefficiencies or overwhelming transplant professionals with excessive notifications. Methods: We investigated the use of machine learning–based survival models to dynamically predict the number of offers a kidney will require before acceptance. Utilizing data on over 16,000 deceased donor kidneys from the national organ offer dataset, we engineered predictive features from both donor profiles and recipient pool characteristics. We trained and evaluated multiple survival models using time-dependent concordance indices along with other survival and regression performance metrics. Results: The Random Survival Forest model achieved the best performance, with a time-dependent C-index of 0.882, effectively estimating the required offer volume for kidney placement. Feature importance analysis revealed that waitlist characteristics, such as mean Estimated Post-Transplant Survival (EPTS), mean Calculated Panel Reactive Antibody (CPRA), time on dialysis, and waitlist duration, were among the most influential predictors. When integrated into a dynamic simultaneous offer system, these predictions have the potential to reduce average placement delays from 17.37 hours to 1.59 hours while maintaining a manageable level of extraneous offers. Discussion: Our results demonstrate that survival-based predictive modeling can meaningfully improve the efficiency of simultaneously expiring offers in kidney allocation. By personalizing batch sizes based on expected offer burden, such models can reduce delays without overwhelming transplant professionals. These findings underscore the value of integrating real-time, data-driven tools into organ allocation systems to improve operational efficiency and facilitate practical implementation.