Machine Learning Predictions for Assessing Hard-to-Place Deceased Donor Kidneys.

Rationale & Objective: Nearly 20% of deceased donor kidneys in the United States are placed "out-of-sequence" (ie, outside of standard allocation rules). The rationale for out-of-sequence placements is to expedite placement of kidneys at risk of nonuse. We aimed to (1) develop machine learning (ML) models to predict the risk of kidney nonuse over time during the allocation process and (2) use the ML predictions to assess current out-of-sequence placements. Study Design: Retrospective cohort study using Organ Procurement and Transplantation Network data. Setting & Participants: Deceased donors with at least one kidney recovered for transplant between January 1, 2022, and December 31, 2023 (25,785 donors; 51,320 kidneys). Predictor: Clinical information available at distinct time points throughout the allocation process (donor medical history, biopsy, and center refusal patterns). Outcome: Probability of kidney nonuse. Analytical Approach: We trained ML models, evaluating area under the receiver operating characteristic curve, accuracy, and other metrics. Feature importance was assessed using Gini impurity. We compared predicted nonuse probabilities across kidneys by outcome (in-sequence, out-of-sequence, not used), conditioned on the Kidney Donor Profile Index (KDPI). Results: Adding refusal information up to clamp time performs better than a model that uses biopsy but no refusal information (area under the receiver operating characteristic curve 0.90 vs 0.88). Center refusal information by time of prediction was among the most important predictors. Donors with out-of-sequence placements had intermediate predicted nonuse probabilities between donors with in-sequence placements and donors with unused kidneys. ML models were able to discriminate hard-to-place kidneys within each KDPI strata. Limitations: Incomplete data on out-of-sequence placements. Conclusions: ML can identify kidneys at high risk of nonuse before biopsy data become available and better than the KDPI. Overall, ML can provide real-time, data-driven tools to identify hard-to-place kidneys, offer a standardized and transparent way to guide accelerated placement and evaluate current practices, and ultimately reduce organ wastage.