Pretransplant model to predict posttransplant survival in liver transplant patients
Pretransplant model to predict posttransplant survival in liver transplant patients
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DOI：
10.1097/00000658-200209000-00008
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发表时间：
2002-09-01
影响因子：
9
通讯作者：
Busuttil, RW
中科院分区：
文献类型：
--
作者：
Ghobrial, RM;Gornbein, J;Busuttil, RW
目的建立一个预测模型，确定患者的生存结局后，原位肝移植（奥尔特）使用现成的pretransplant variables.Summary背景资料目前的肝脏器官分配系统强烈支持器官分配给危重患者谁表现出生存不良的结果后奥尔特。严重有限的器官资源，不断增加的等待名单死亡，以及越来越多的危重患者要求器官分配系统平衡疾病严重程度与生存结果。这些目标可以实现，只有通过发展预测奥尔特以下奥尔特.Methods的预后模型，可能会影响患者生存率以下OLT. Methods的变量进行了分析，在丙型肝炎（HCV）收件人在作者的中心，因为HCV是最常见的适应症OLT。在器官共享联合网络（UNOS）数据库中的HCV和非HCV患者中检查和改进了由此产生的患者生存模型。Kaplan-Meier方法，单变量比较，多变量考克斯比例风险回归分析，结果变量确定为独立的预测因素，患者生存后，在过去10年中，在作者的中心主要移植成人HCV受体进入预后生存模型，以预测患者的生存。因此，死亡率预测值为0.0293（受体年龄）+1.085（log（10）受体肌酐）+0.289（供体女性性别）+0.675紧急UNOS - 1.612（log（10）受体肌酐乘以紧急UNOS）。上述变量，除了供体年龄，总胆红素，凝血酶原时间（PT），再次移植，热和冷缺血时间，应用于UNOS数据库。在过去10年中接受移植的46，942例患者中，有25，772例患者有完整的数据集。一个八因素模型，准确地预测生存的推导。因此，移植后死亡指数= 0.0084供体年龄+0.019受体年龄+0.816 log肌酐+0.0044热缺血（分钟）+0.659（如果是第二次移植）+0.10 log胆红素+0.0087 PT +0.01冷缺血（小时）。因此，该模型适用于第一次或第二次肝移植。基于模型预测的死亡风险评分的患者生存率和观察到的移植后生存率相似。此外，该模型准确地预测了HCV和非HCV patients.Conclusions移植后患者的生存率可以准确预测的基础上，八个简单的因素。平衡应用肝移植生存率估计模型，以及疾病严重程度，如终末期肝病模型所估计的，将显著改善生存结局，并使奥尔特后患者获益最大化。
Objective To develop a prognostic model that determines patient survival outcomes after orthotopic liver transplantation (OLT) using readily available pretransplant variables.Summary Background Data The current liver organ allocation system strongly favors organ distribution to critically ill recipients who exhibit poor survival outcomes following OLT. A severely limited organ resource, increasing waiting list deaths, and rising numbers of critically ill recipients mandate an organ allocation system that balances disease severity with survival outcomes. Such goals can be realized only through the development of prognostic models that predict survival following OLT.Methods Variables that may affect patient survival following OLT were analyzed in hepatitis C (HCV) recipients at the authors' center since HCV is the most common indication for OLT. The resulting patient survival model was examined and refined in HCV and non-HCV patients in the United Network for Organ Sharing (UNOS) database. Kaplan-Meier methods, univariate comparisons, and multivariate Cox proportional hazard regression were employed for analyses.Results Variables identified by multivariate analysis as independent predictors for patient survival following primary transplantation of adult HCV recipients in the last 10 years at the authors' center were entered into a prognostic survival model to predict patient survival. Accordingly, mortality was predicted by 0.0293 (recipient age) + 1.085 (log(10) recipient creatinine) + 0.289 (donor female gender) + 0.675 urgent UNOS - 1.612 (log(10) recipient creatinine times urgent UNOS). The above variables, in addition to donor age, total bilirubin, prothrombin time (PT), retransplantation, and warm and cold ischemia times, were applied to the UNOS database. Of the 46,942 patients transplanted over the last 10 years, 25,772 patients had complete data sets. An eight-factor model that accurately predicted survival was derived. Accordingly, the mortality index posttransplantation = 0.0084 donor age + 0.019 recipient age + 0.816 log creatinine + 0.0044 warm ischemia (in minutes) + 0.659 (if second transplant) + 0.10 log bilirubin + 0.0087 PT + 0.01 cold ischemia (in hours). Thus, this model is applicable to first or second liver transplants. Patient survival rates based on model-predicted risk scores for death and observed posttransplant survival rates were similar. Additionally, the model accurately predicted survival outcomes for HCV and non-HCV patients.Conclusions Posttransplant patient survival can be accurately predicted based on eight straightforward factors. The balanced application of a model for liver transplant survival estimate, in addition to disease severity, as estimated by the model for end-stage liver disease, would markedly improve survival outcomes and maximize patients' benefits following OLT.