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

开发高质量工具来表征同种异体移植质量、预测移植结果并扩大肾移植和肝移植的机会

• 批准号: 10201592
• 负责人: David Seth Goldberg
• 金额: $ 47.9万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10201592
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Allografting; Biliary; Biological; Biological Markers; Blood Circulation; Blood Pressure; Calibration; Cessation of life; Characteristics; Clinical; Clinical Data; Communities; Complication; Creatinine; Data; Decision Making; Discrimination; Elderly; Event; Failure; Grant; Hepatitis; Hospitalization; Impairment; Individual; Ischemia; Joints; Kidney; Kidney Transplantation; Knowledge; Laboratories; Life; Link; Liver; Measures; Medicare; Methodology; Methods; Modeling; Names; Organ; Organ Donor; Organ Survival; Organ Transplantation; Outcome; Output; Overdose; Patients; Perfusion; Pharmaceutical Preparations; Policy Developments; Population; Probability; Province; Public Health; Registries; Research Personnel; Risk; Risk Assessment; Savings; Serum; Statistical Methods; Techniques; Time; Transplantation; Urine; Waiting Lists; Work; base; comorbidity; demographics; graft failure; hemodynamics; improved; indexing; liver transplantation; neglect; novel; organ allocation; organ injury; population health; pressure; secondary analysis; statistics; tool; web-based tool; Acute Renal Failure with Renal Papillary Necrosis; Allografting; Biliary; Biological; Biological Markers; Blood Circulation; Blood Pressure; Calibration; Cessation of life; Characteristics; Clinical; Clinical Data; Communities; Complication; Creatinine; Data; Decision Making; Discrimination; Elderly; Event; Failure; Grant; Hepatitis; Hospitalization; Impairment; Individual; Ischemia; Joints; Kidney; Kidney Transplantation; Knowledge; Laboratories; Life; Link; Liver; Measures; Medicare; Methodology; Methods; Modeling; Names; Organ; Organ Donor; Organ Survival; Organ Transplantation; Outcome; Output; Overdose; Patients; Perfusion; Pharmaceutical Preparations; Policy Developments; Population; Probability; Province; Public Health; Registries; Research Personnel; Risk; Risk Assessment; Savings; Serum; Statistical Methods; Techniques; Time; Transplantation; Urine; Waiting Lists; Work; base; comorbidity; demographics; graft failure; hemodynamics; improved; indexing; liver transplantation; neglect; novel; organ allocation; organ injury; population health; pressure; secondary analysis; statistics; tool; web-based tool

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.

项目摘要/摘要 在美国，将近95,000名患者被列入肾脏移植，但在2018年，只有14,700名接受了肾脏移植。 已故的供体肾脏移植，而近8500人死亡或病得很重。器官短缺同样 肝移植激烈； 2018年，在14,000多名等待名单患者中，只有7,700名收到 死者的供体肝移植，而2500人死亡或病得很重。不幸的是，超过5,000个孩子 2018年，有2,000人来自已故捐助者的移植者，但从未移植。虽然 这些器官的子集不适合移植，数据清楚地表明，无法准确地表明 评估移植质量直接导致许多丢弃和/或破坏了我们指导器官适当的能力 患者。在提供移植的器官之前，已故捐赠者住院了几天，经常 有许多可用于评估器官功能的纵向数据点（例如，实验室值）。然而， 现有的移植质量具有以下主要缺陷：1）提醒横截面临床和实验室 在采购之前直接数据； 2）从供体终端忽略了与生物学相关的纵向数据 住院治疗此类系列血液动力学（肾脏和肝脏）和尿量（肾脏）； 3）未能 捐助者和受体特征之间的综合相互作用。结果，现有的肾脏和肝脏供体 风险模型的预测准确性不足（C统计量仅为0.6-0.65）。我们的小组提议 通过开发使用大量纵向供体数据的最先进的模型来推进该领域 在捐赠者的末期住院期间 - 器官损伤的实验室生物标志物和器官的措施 功能和灌注。其次，我们将使用关节建模开发高度强大的同种异体风险模型 方法，可以说明纵向供体暴露数据和事件的时间结果，例如移植 故障，而不是标准技术（例如Cox回归）。第三，我们将重点介绍现实世界的影响 在人口健康方面的结果。我们有这些特定的目标：1）发展肾脏移植失败模型 使用关节建模来预测相对于电流的更高歧视和校准的移植失败 肾脏供体风险指数； 2）使用关节模型开发肝移植失败风险模型，以预测移植失败 具有高歧视和校准； 3A）模拟同种异体移植寿命的变化与更好的配对 基于预计器官和患者生存的对齐的受体器官；和3b）模拟更改 通过实施改善，在移植和同种异体移植寿命的数量中 器官分配中的器官质量指标可减少丢失。这些模型将使用 全面的美国移植数据，并通过两个加拿大计划的数据在外部验证。赠款 还将通过与Medicare的数据链接到移植并发症的重要探索性分析。 我们最终将开发一种基于Web的工具，以实现器官结果的实时预测，以将结果列入 临床医生和其他研究人员的手。