Optimization and Simulation of Kidney Paired Donation Programs

描述(由申请人提供)：一种名为肾脏配对捐赠(KPD)的不断发展的策略采用了一种技术解决方案，以克服许多肾衰竭患者面临的障碍，这些患者提供有意愿但免疫或血型不相容的活体捐赠者。肾配对捐赠使用计算机算法将一个不相容的供者/受者对与另一对具有互补不相容的供者/受者配对，这样第一对的供者给第二对的受者，反之亦然。我们还考虑了涉及三对或更多对器官的更复杂的交换。此外，我们允许无私捐赠者自愿捐赠肾脏，从而有可能创建肾脏移植链。管理KPD项目的一个基本问题是从可能产生的许多数学上可能的替代组合中选择“最佳”移植集合。最佳分配的选择在一定程度上取决于特定移植的分配效用。我们表明，这种选择也应该依赖于随机元素，并定义新的更好的优化方法。我们将开发一个基于数据的KPD项目的微观模拟模型，并利用该模型来评估不同的分配策略，比较这些策略对绩效结果的影响，以及评估不同效用分配的效果。此外，我们将开发这些模型的用户友好版本，为负责管理KPD项目的个人提供决策支持。为了完成这些任务，我们提出了以下具体目标：目标1：开发考虑效用和不确定性的KPD计划中选择交易所和链的优化方法，并将这些新方法与目前仅基于效用的方法进行比较。这包括通过在现有的KPD计划中实施这些方法来测试和改进这些方法。目标2：利用基于多个数据源的模拟方法，开发一种全面的方法来对KPD计划的所有方面进行建模，并使用该微观模拟模型来评估政策决策和方法对KPD关键绩效衡量的影响。目的3：开发一个用户友好的界面，使微观模拟模型能够在主动的KPD项目中用于决策支持。 公共卫生相关性：USRDS报告称，2009年，联邦医疗保险花费了近240亿美元用于终末期肾病(ESRD)患者的护理，占所有联邦医疗保险支出的近5.8%。如果将非医疗保险支出考虑在内，ESRD总成本达到353亿美元，占美国2007年2.2万亿美元医疗支出的1.6%。无论是从患者预后还是从医疗费用来看，移植，尤其是活体供体移植都是终末期肾病的首选治疗方法。该项目将寻求优化肾脏配对捐赠计划的潜力，以增加活体捐赠者移植的数量和质量，从而在大幅降低医疗成本的同时改善患者的预后。

DESCRIPTION (provided by applicant): An evolving strategy known as kidney paired donation (KPD) employs a technological solution to overcome the barriers faced by many patients with kidney failure who present with willing, but immunologically or blood type incompatible living donors. Kidney paired donation uses a computerized algorithm to match one incompatible donor/recipient pair to another pair with a complementary incompatibility, such that the donor of the first pair gives to the recipient of the second, and vice versa. We also consider more complex exchanges of organs involving three or more pairs. In addition, we allow for altruistic donors who donate a kidney voluntarily and thereby have the potential to create a chain of kidney transplants. A fundamental problem in managing a KPD program is selecting the "optimal" set of transplants from among the many mathematically possible alternative combinations that could be generated. The choice of an optimal allocation depends in part on the assigned utility of a specific transplant. We show that this choice should also depend on stochastic elements and define new and better methods for optimization. We will develop a data-based micro-simulation model of KPD program, and utilize that model to evaluate different allocation strategies, to compare the impact of these strategies on performance outcomes, and to assess effects of different utility assignments. In addition, we will develop a user-friendly version of these models to provide decision support to individuals charged with managing KPD programs. To accomplish these tasks, we propose the following specific aims: Aim 1: Develop optimization methods for selecting exchanges and chains in a KPD program that take account of utility and uncertainty, and to compare these new methods with those currently in use that are based solely on utility. This includes testing and refining these methods through their implementation in existing KPD programs. Aim 2: To develop a holistic approach to the modeling of all aspects of a KPD program using simulation methods based on multiple data sources, and the use of this micro simulation model to assess the effects of policy decisions and approaches on key performance measures of a KPD. Aim 3: To develop a user friendly interface to enable the use of the micro simulation model for decision support in active KPD programs. PUBLIC HEALTH RELEVANCE: The USRDS reports that for the 2009 year, Medicare spent nearly $24 billion to care for patients with End Stage Renal Disease (ESRD), or nearly 5.8 percent of all Medicare spending. When non-Medicare spending is factored in, total ESRD costs reached $35.3 billion or 1.6% of the $2.2 trillion the US spent on healthcare in 2007. Both in terms of patient outcomes and in terms of medical costs, transplantation, and especially living-donor transplantation is the preferred treatment of ESRD. This project would seek to optimize the potential of Kidney Paired Donation Programs to increase the number and quality of living donor transplants and so improve patient outcomes while also reducing health care costs substantially.