ORGAN ALLOCATION AND THE COST OF LIVER TRANSPLANTATION

本提案的目的是确定决定成本的因素 肝移植的成功我中心参加了NIDDK肝脏 移植数据库（LTD），一个为期五年的项目， 收集了919例连续肝移植的详细临床数据 在三个主要的中心。通过合并资源利用率 数据库与有限责任公司，重要信息的决定因素 肝移植的费用将得到。具体目标 该项目的主要内容是：1）建立数学模型， 原位肝移植（奥尔特）的成本基于受体的 移植前临床和人口统计学特征2）确定 不同器官分配方案对肝脏成本的影响 移植 我们将采用三种策略来分析数据。一旦成本数据 已与临床数据合并，单变量和多变量 将进行分析，以确定临床特征， 这与奥尔特的成本增加有关。第二，构建模型 其可以基于特定的临床概况来预测奥尔特的成本。 最后，利用这些模型，我们将确定 不同的器官分配方案。 这些研究将提供重要的见解， 终末期肝病的有效治疗。通过了解 影响成本的因素，我们希望能够更好地评估新的 治疗，评估和修改我们当前的器官分配系统， 使肝移植更具成本效益。

The goal of this proposal is to identify factors which determine the cost of liver transplantation. Our center has participated in the NIDDK Liver Transplant Database (LTD), a five year project which has prospectively collected detailed clinical data on 919 consecutive liver transplants performed at three major centers. By merging a resource utilization database with the LTD, important information regarding the determinants of the cost of liver transplantation will be obtained. The specific aims of this project are: 1) To develop mathematical models which predict the cost of orthotopic liver transplantation (OLT) based on the recipient's pre-transplant clinical and demographic profile 2) To determine the impact of different organ allocation schemes on the cost of liver transplantation. Three strategies will be employed to analyze the data. Once the cost data has been merged with the clinical data, univariate and multi-variate analyses will be performed to identify clinical features which are associated with increased cost of OLT. Second, models will be constructed which can predict the cost of OLT based on a specific clinical profile. Finally, using these models, we will determine the economic impact of different schemes of organ allocation. These studies will provide important insights into this costly yet effective therapy for end-stage liver disease. By understanding the factors that influence costs we hope to be able to better evaluate new therapies, evaluate and modify our current system of organ allocation, and make liver transplantation more cost effective.