Using MDPs to Optimize Living Donor Liver Transplants

使用 MDP 优化活体肝移植

• 批准号: 6822126
• 负责人: MARK Stenius ROBERTS
• 金额: $ 19.77万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6822126
• 关键词: chronic disease /disorder; clinical research; computer assisted medical decision making; computer data analysis; human data; liver disorder; liver transplantation; mathematical model; model design /development; postmortem; prognosis; statistics /biometry; time resolved data; tissue donors; transplantation resource /registry /referral center

DESCRIPTION (provided by applicant): Virtually unheard of prior to 1990, the use of living donors for liver transplantation has been increasing and now represents over 500 transplants per year. Although living donor transplantation has several potential advantages, the procedure places the living donor at risk of death and morbidity and, therefore, should be undertaken as appropriately and efficiently as possible. In this exploratory application, we propose to use Markov decision processes, (MDPs, which are mathematical tools specifically designed to analyze sequential decisions under conditions of uncertainty) to evaluate the optimal timing of living donor liver transplantation. The overall goal of this developmental R21 application is to provide insights into the factors that affect this timing decision so that transplant decisions can be made as carefully and successfully as possible. Specifically, this work seeks to fulfill two specific aims: Aim 1: Evaluate the optimal time to transplant a living donor liver given the characteristics of the recipient and donor. Acceptance of a living donor organ occurs outside of the UNOS waiting list and is not subject to external allocation and selection rules. Therefore, the only decision to be made is the appropriate time (in clinical terms of declining health status) to conduct the transplant. We propose to build an MDP that will evaluate this question. Aim 2: Evaluate the optimal waiting time for a cadaveric transplant prior to accepting a living donor transplant. Because the existence of a willing potential living donor does not preclude listing on the cadaveric wait list, and because a prospective recipient may not want to place that donor at mortal risk, the more realistic question is: "How long should I wait on the cadaveric list before accepting a living donor?" The MDP developed in AIM 1 will be enhanced to incorporate the sequential possibility of being offered cadaveric organs of varying quality. The work is innovative and developmental as MDPs have not typically been used to evaluate medical decisions, even though they were specifically developed to evaluate sequential decisions under uncertainty (which aptly describes most decisions clinicians make). The investigative team has substantial and unique experience in modeling the progression of liver disease and the transplant process, and the application of mathematical models from management science to problems in health and medicine. We seek to extend our history of successful collaboration between Medicine and Industrial Engineering, to investigate the usefulness of these sophisticated mathematical models when applied to a real and complex medical decision.

描述（由申请人提供）： 使用活体捐献者进行肝移植的情况在 1990 年之前几乎闻所未闻，但这种情况一直在增加，现在每年的移植数量超过 500 例。尽管活体捐献者移植具有多种潜在优势，但该程序使活体捐献者面临死亡和发病的风险，因此应尽可能适当和有效地进行。在这个探索性应用中，我们建议使用马尔可夫决策过程（MDP，是专门设计用于分析不确定条件下的顺序决策的数学工具）来评估活体肝移植的最佳时机。这一开发 R21 应用程序的总体目标是深入了解影响这一时间决策的因素，以便尽可能谨慎、成功地做出移植决策。具体来说，这项工作旨在实现两个具体目标： 目标 1：根据受者和供者的特征评估活体供者肝脏移植的最佳时间。活体捐赠器官的接受发生在 UNOS 等候名单之外，并且不受外部分配和选择规则的约束。因此，唯一需要做出的决定是进行移植的适当时间（在临床上健康状况下降的情况下）。我们建议建立一个 MDP 来评估这个问题。 目标 2：评估接受活体移植之前尸体移植的最佳等待时间。因为愿意的潜在活体捐赠者的存在并不排除列入尸体等待名单，并且因为潜在的接受者可能不想将该捐赠者置于致命的风险中，所以更现实的问题是：“我应该在尸体上等待多长时间在接受活体捐赠者之前列出清单？” AIM 1 中开发的 MDP 将得到增强，以纳入提供不同质量尸体器官的连续可能性。 这项工作具有创新性和发展性，因为 MDP 通常不用于评估医疗决策，尽管它们是专门为评估不确定性下的顺序决策而开发的（这恰当地描述了临床医生做出的大多数决策）。研究团队在模拟肝病进展和移植过程以及将管理科学的数学模型应用于健康和医学问题方面拥有丰富而独特的经验。我们寻求扩展医学和工业工程之间成功合作的历史，研究这些复杂的数学模型在应用于真实而复杂的医疗决策时的有用性。