Promoting Utilization of Kidneys by Improving Patient Level Decision Making

 描述(由申请人提供)：通过改善器官接受决策促进肾脏的利用2014年，在近109,000名等待肾移植(KT)的患者中，只有15,652人接受了肾脏移植。6000多人在等待KT的过程中死亡，2000多人病情严重，无法移植。患者存活率、生活质量和发病率对于那些仍在接受透析的患者来说明显更差。大多数肾脏是 移植的器官是从已故的捐赠者那里回收的。2014年，虽然为KT回收了12,664个肾脏，但有2,272个被丢弃--最常见的原因是肾脏质量不佳或质量较低。许多被丢弃的肾脏被认为比其他获得的肾脏带来的存活收益较小。先前的研究表明，与继续透析相比，边缘肾脏提供了生存益处，但忽略了对单个患者的这些益处的量化。这些研究忽略了两个主要问题：(I)移植后一年和三年患者和移植物存活模型的低C-统计量；(Ii)器官供应的动态和当前等待名单患者的基于积分的优先系统。鉴于这些事实，从一项决定 从制造者的角度来看，接受或拒绝低质量的器官并不一定是一个明智的选择。这项研究综合考虑了透析时间、受者特征、移植中心特征、 和捐献的肾脏特征。它建议应用生物统计学和机器学习的技术来提高模型的精度。这项研究进一步旨在量化不同质量器官的肾脏利用率的动态变化，并将未来的肾脏供应建模为随机决策树。建议的计算引擎用于评估接受或拒绝提议的生存益处，将在临床决策中产生革命性的影响。此外，这项拟议研究的成功完成将为支持肾脏接受/排斥决定提供高度可靠的方法。这项研究将由一个具有移植分配系统、决策方法和临床决策方面的专业知识的跨学科团队进行。研究小组将纳入一个外部顾问委员会的意见，该委员会由肾脏和胰腺移植项目的领导人和关键利益相关者组成。

 DESCRIPTION (provided by applicant): Promoting Utilization of Kidneys by Improving Organ Acceptance Decision Making Only 15,652 of nearly 109,000 waitlisted patients for kidney transplant (KT) received one in 2014. More than six thousand died while waiting for a KT and more than two thousand became too sick to be transplanted. Patient survival, quality of life, and morbidity is significantly worse for those who remain on dialysis. The majority of kidneys that are transplanted are recovered from deceased donors. In 2014, while 12,664 kidneys were recovered for KT, 2,272 were discarded - most frequently due to being of marginal or low quality. Many of the discarded kidneys are believed to confer smaller survival gains than other procured kidneys. Previous studies show that the marginal kidneys offer survival benefits with respect to remaining on dialysis, but neglect quantification of these benefits to an individual patient. These studies ignore two major issues: (i) the low c-statistics of one and three year post-transplant patient and graft survival models; and (ii) dynamics of the organ offers and the current point based priority system for the waitlisted patients. Given these facts, from a decision maker's perspective, accepting or rejecting a low quality organ is not necessarily a well informed choice. The proposed research takes a comprehensive approach in developing a survival model incorporating time on dialysis, recipient characteristics, transplant center characteristics, and donated kidney characteristics. It proposes to apply techniques from biostatistics and machine learning to improve model accuracy. The research further aims to quantify the dynamics of kidney availability of different quality organs, and model future kidney offers as a stochastic decision tree. The proposed computational engine for estimating the survival benefits of accepting or rejecting an offer will be transformative in clinical decision making. Moreover, th successful completion of the proposed research will provide a highly reliable approach to support kidney acceptance/rejection decisions. The research will be carried out by a trans- disciplinary team with expert knowledge in the transplant allocation system, decision methodologies, and clinical decision making. The research team will incorporate input from an external advisory board consisting of leaders and key stakeholders in kidney and pancreas transplant programs.