Department of Management, Information Systems and Analytics, State University of New York at Plattsburgh, Plattsburgh, NY 12901, USA.
Department of Engineering Management, Information, and Systems and Department of Computer Science, Southern Methodist University, Dallas, TX 75205, USA.
Int J Environ Res Public Health. 2021 Jan 20;18(3):873. doi: 10.3390/ijerph18030873.
More than 8000 patients on the waiting list for kidney transplantation die or become ineligible to receive transplants due to health deterioration. At the same time, more than 4000 recovered kidneys from deceased donors are discarded each year in the United States. This paper develops a simulation-based optimization model that considers several crucial factors for a kidney transplantation to improve kidney utilization. Unlike most proposed models, the presented optimization model incorporates details of the offering process, the deterioration of patient health and kidney quality over time, the correlation between patients' health and acceptance decisions, and the probability of kidney acceptance. We estimate model parameters using data obtained from the United Network of Organ Sharing (UNOS) and the Scientific Registry of Transplant Recipients (SRTR). Using these parameters, we illustrate the power of the simulation-based optimization model using two related applications. The former explores the effects of encouraging patients to pursue multiple-region waitlisting on post-transplant outcomes. Here, a simulation-based optimization model lets the patient select the best regions to be waitlisted in, given their demand-to-supply ratios. The second application focuses on a system-level aspect of transplantation, namely the contribution of information sharing on improving kidney discard rates and social welfare. We investigate the effects of using modern information technology to accelerate finding a matching patient to an available donor organ on waitlist mortality, kidney discard, and transplant rates. We show that modern information technology support currently developed by the United Network for Organ Sharing (UNOS) is essential and can significantly improve kidney utilization.
超过 8000 名等待肾移植的患者因健康恶化而死亡或失去接受移植的资格。与此同时，美国每年有超过 4000 个来自已故捐赠者的恢复肾脏被丢弃。本文开发了一种基于模拟的优化模型，考虑了几个关键因素，以提高肾脏的利用率。与大多数提出的模型不同，所提出的优化模型包含了提供过程的细节、患者健康和肾脏质量随时间的恶化、患者健康和接受决策之间的相关性以及肾脏接受的概率。我们使用从美国器官共享网络 (UNOS) 和移植受者科学登记处 (SRTR) 获得的数据来估计模型参数。使用这些参数，我们通过两个相关应用来说明基于模拟的优化模型的强大功能。前者探讨了鼓励患者进行多区域候补等待对移植后结果的影响。在这里，基于模拟的优化模型允许患者根据其供需比选择最佳的候补区域。第二个应用重点关注移植的系统层面，即信息共享对提高肾脏丢弃率和社会福利的贡献。我们研究了利用现代信息技术加速寻找与候补名单上可用供体器官匹配的患者对等待名单死亡率、肾脏丢弃和移植率的影响。我们表明，目前由美国器官共享网络 (UNOS) 开发的现代信息技术支持是必要的，并且可以显著提高肾脏的利用率。
