Model for end-stage liver disease (MELD) and allocation of donor livers

终末期肝病模型 医学 肝病 内科学 慢性肝病 肝移植 胃肠病学 死亡率 队列 接收机工作特性 肌酐 肝硬化 外科 移植
作者
Russell H. Wiesner,Erick Edwards,Richard B. Freeman,Ann Harper,Ray Kim,Patrick S. Kamath,Walter K. Kremers,John R. Lake,Todd K. Howard,Robert M. Merion,Robert A. Wolfe,Ruud A. F. Krom
出处
期刊:Gastroenterology [Elsevier BV]
卷期号:124 (1): 91-96 被引量:2547
标识
DOI:10.1053/gast.2003.50016
摘要

A consensus has been reached that liver donor allocation should be based primarily on liver disease severity and that waiting time should not be a major determining factor. Our aim was to assess the capability of the Model for End-Stage Liver Disease (MELD) score to correctly rank potential liver recipients according to their severity of liver disease and mortality risk on the OPTN liver waiting list.The MELD model predicts liver disease severity based on serum creatinine, serum total bilirubin, and INR and has been shown to be useful in predicting mortality in patients with compensated and decompensated cirrhosis. In this study, we prospectively applied the MELD score to estimate 3-month mortality to 3437 adult liver transplant candidates with chronic liver disease who were added to the OPTN waiting list at 2A or 2B status between November, 1999, and December, 2001.In this study cohort with chronic liver disease, 412 (12%) died during the 3-month follow-up period. Waiting list mortality increased directly in proportion to the listing MELD score. Patients having a MELD score <9 experienced a 1.9% mortality, whereas patients having a MELD score > or =40 had a mortality rate of 71.3%. Using the c-statistic with 3-month mortality as the end point, the area under the receiver operating characteristic (ROC) curve for the MELD score was 0.83 compared with 0.76 for the Child-Turcotte-Pugh (CTP) score (P < 0.001).These data suggest that the MELD score is able to accurately predict 3-month mortality among patients with chronic liver disease on the liver waiting list and can be applied for allocation of donor livers.

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