Policy Optimization for Dynamic Heart Transplant Allocation

TL;DR

This paper develops a new simulation-based approach to optimize heart transplant allocation policies by considering dynamic factors, leading to improved outcomes over existing policies.

Contribution

It introduces a novel simulator and potential-based policies that better account for pre- and post-transplant mortality, improving allocation efficiency.

Findings

The current policy is significantly less effective than the myopic policy.

Potential-based policies outperform traditional approaches.

Batching donors enhances transplant success rates.

Abstract

Heart transplantation is a viable path for patients suffering from advanced heart failure, but this lifesaving option is severely limited due to donor shortage. Although the current allocation policy was recently revised in 2018, a major concern is that it does not adequately take into account pretransplant and post-transplant mortality. In this paper, we take an important step toward addressing these deficiencies. To begin with, we use historical data from UNOS to develop a new simulator that enables us to evaluate and compare the performance of different policies. We then leverage our simulator to demonstrate that the status quo policy is considerably inferior to the myopic policy that matches incoming donors to the patient who maximizes the number of years gained by the transplant. Moreover, we develop improved policies that account for the dynamic nature of the allocation process through the use of potentials -- a measure of a patient's utility in future allocations that we introduce. We also show that batching together even a handful of donors -- which is a viable option for a certain type of donors -- further enhances performance. Our simulator also allows us to evaluate the effect of critical, and often unexplored, factors in the allocation, such as geographic proximity and the tendency to reject offers by the transplant centers.