Predictive Models for Kidney Offer Acceptance: Challenges and Strategies

Leveraging advanced machine learning models and incorporating nonclinical datapoints (like transportation distances) can improve transplant organ offer acceptance prediction models. However, we observed steep tradeoffs between precision and recall as captured in the low average precision scores despite deceptively high AUROCs (baseline AUROC 0.832). Our findings suggest that even the best-performing models would not provide clear, equitable benefits over existing allocation policies. More research is needed before these models are practical for clinical implementation.