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.
翻译:心脏移植是晚期心力衰竭患者的一种可行治疗途径,但由于供体短缺,这一挽救生命的方案受到严重限制。尽管现行分配政策已于2018年修订,但其主要问题在于未能充分考虑移植前与移植后的死亡率。本文针对这些缺陷迈出了重要一步。首先,我们利用器官共享联合网络(UNOS)的历史数据开发了新型模拟器,以评估和比较不同分配策略的性能。通过该模拟器,我们证明现行策略远逊于短视策略——后者将新进供体匹配给能使移植获得生存年数最大化的患者。进一步地,我们通过引入势能函数(一种衡量患者在后续分配中效用的指标)构建了改进策略,以应对分配过程的动态特性。研究还表明,对特定类型供体实施小批量集中分配可进一步提升系统性能。此外,该模拟器能够评估地理邻近性、移植中心拒受倾向等关键但常被忽视的分配影响因素。