Developing High-Quality Tools to Characterize Allograft Quality, Predict Transplant Outcomes and Expand Access to Kidney and Liver Transplantation

项目概要/摘要 在美国，近95，000名患者等待肾移植，但在2018年，只有14，700人接受了肾移植。 死亡捐赠者肾脏移植，而近8，500人死亡或病情严重。器官短缺同样 肝脏移植的激烈; 2018年，在14，000多名等待名单的患者中，只有7，700人接受了 2，500名死者捐赠者肝脏移植，而2，500人死亡或病情严重。不幸的是，超过5,000个肾脏 2018年，有2,000名已故捐赠者的肝脏被提供用于移植，但从未被移植。虽然 这些器官的子集不适合移植，数据清楚地表明，无法准确地 评估移植物质量直接导致了许多丢弃和/或破坏了我们引导器官进行适当移植的能力。 患者在他们的器官被提供用于移植之前，已故的捐赠者通常会住院几天， 具有多个纵向数据点（例如，实验室值），以评估器官功能。然而， 现有的移植物质量模型具有以下主要缺陷：1）依赖于横截面临床和实验室 直接在采购前的数据; 2）忽略来自供体终端的生物相关纵向数据 住院治疗，如连续血流动力学（肾脏和肝脏）和尿量（肾脏）;以及3）未能 整合捐赠者和接受者特征之间的相互作用。因此，现有的肾脏和肝脏捐赠者 风险模型的预测准确性不足（C统计量仅为0.6 - 0.65）。我们小组建议 通过开发利用广泛的纵向供体数据的最先进模型来推进该领域 器官损伤的实验室生物标志物和器官损伤的测量 功能和灌注。其次，我们将使用关节建模开发高度稳健的同种异体移植风险模型 这种方法可以解释纵向供体暴露数据和事件发生时间结局，如移植物 故障，而不是标准技术（例如，考克斯回归）。第三，我们将突出现实世界的影响， 在人口健康方面的结果。我们的具体目标是：1）建立移植肾衰竭模型 使用联合建模以相对于当前更高的区分度和校准来预测移植失败 肾供体风险指数; 2）使用联合建模开发肝移植失败风险模型以预测移植失败 具有高分辨力和校准; 3a）模拟同种异体移植物寿命年从更好配对的变化 基于投影器官和患者存活率的对准将器官移植到受体;以及3b）模拟变化 在移植数量和移植人口的同种异体移植生命年， 器官分配中的器官质量指标，以减少丢弃。模型将使用 全面的美国移植数据，并与来自加拿大两个省的数据进行外部验证。授出 还将通过与医疗保险数据的链接，包括对移植并发症的重要探索性分析。 我们最终将开发一种基于网络的工具，以实现对器官结果的实时预测， 临床医生和其他研究人员的手。

Project Summary/Abstract In the US, nearly 95,000 patients are waitlisted for a kidney transplant, yet in 2018, only 14,700 received a deceased donor kidney transplant, while nearly 8,500 died or became too sick. The organ shortage is equally intense for liver transplant; in 2018, among more than 14,000 waitlisted patients, only 7,700 received a deceased donor liver transplant while 2,500 died or became too sick. Unfortunately, more than 5,000 kidneys and 2,000 livers from deceased donors were offered for transplant in 2018, but never transplanted. Although a subset of these organs was unsuitable for transplant, data clearly demonstrate that the inability to accurately assess graft quality directly led to many discards and/or undermined our ability to guide organs to appropriate patients. Prior to their organs being offered for transplant, deceased donors are hospitalized for days, often with numerous longitudinal data points (e.g., laboratory values) available to assess organ function. Yet, existing models of graft quality have these major flaws: 1) a reliance on cross-sectional clinical and laboratory data directly prior to procurement; 2) neglect of biologically-relevant, longitudinal data from the donor terminal hospitalization such serial hemodynamics (kidney and liver) and urine output (kidney); and 3) failure to integrate interactions between donor and recipient characteristics. As a result, existing kidney and liver donor risk models have inadequate prediction accuracy (C-statistics of only 0.6-0.65). Our group proposes to advance the field by developing state-of-the art models that make use of extensive, longitudinal donor data during the donor's terminal hospitalization—laboratory biomarkers of organ injury, and measures of organ function and perfusion. Second, we will develop highly robust allograft risk models using the joint modeling approach, which can account for longitudinal donor exposure data and time-to-event outcomes such as graft failure, instead of standard techniques (e.g., Cox regression). Third, we will highlight the real-world impact of the results in terms of population health. We have these specific aims: 1) Develop kidney graft failure models using joint modeling to predict graft failure with higher discrimination and calibration relative to the current kidney donor risk index; 2) Develop liver graft failure risk models using joint modeling to predict graft failure with high discrimination and calibration; 3a) Simulate the change in allograft life years from better pairing organs to recipients based on alignment of projected organ and patient survival; and 3b) Simulate the change in the number of transplants and allograft life years for the transplant population by implementing improved organ quality metrics in organ allocation to decrease discards. The models will be constructed using comprehensive US transplant data and externally validated with data from two Canadian provinces. The grant will also include important exploratory analyses of transplant complications by linking to data from Medicare. We will finally develop a web-based tool to enable real-time predictions of organ outcomes to put the results in the hands of clinicians and other investigators.