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MELD评分对肝移植手术患者预后的预测价值
引用本文:赖添顺,郭振辉,苏磊,霍枫,汪邵平,浦淼水,蔡敏捷.MELD评分对肝移植手术患者预后的预测价值[J].肝胆胰外科杂志,2008,20(6):407-410.
作者姓名:赖添顺  郭振辉  苏磊  霍枫  汪邵平  浦淼水  蔡敏捷
作者单位:广州军区广州总医院,MICU科,广东,广州,510010;广州军区广州总医院,ICU科,广东,广州,510010;广州军区广州总医院,肝胆外科,广东,广州,510010
摘    要:目的探讨术前终末期肝病模型(model for end-stage liver disease,MELD)评分在肝移植治疗终末期肝病早期预后中的预测价值。方法回顾106例终末期肝病患者行肝移植治疗的临床资料,计算术前MELD评分,根据并发症、死亡检验ROC曲线中最佳曲线Youden指数最高时的MELD截断值进行分组,并对各组早期并发症发生率和生存率结果进行分析。结果本组106例肝移植患者中各种严重并发症发生率为29.25%,住院28d和术后3个月生存率分别为90.57%和89.62%;非并发症组、并发症组以及生存组、死亡组的MELD评分均值分别为12.00、21.19和13.28、28.27,其MELD分值差异有统计学意义(P〈0.01):评价并发症的ROC曲线下面积为0.24±0.05(P〈0.01),死亡检验ROC曲线下面积为0.87±0.06(P〈0.01),死亡检验ROC曲线Youden指数最高时的MELD截断值分别为18.42和27.15;与MELD≤18.42组相比,18.42—27.15组和≥27.15组两组的并发症发生率、死亡率均显著增加(P〈0.01)。结论终末期肝病患者术前MELD评分分值越高,肝移植后早期严重并发症发生率和死亡率越高:MELD分值对行肝移植术的患者发生严重并发症的预测效果较差,但对死亡的预测效果较好:高分值MELD(≥27.15)是预测肝移植患者术后高并发症发生与死亡的较好指标。

关 键 词:肝移植  终末期肝病模型  并发症  死亡率  预测价值

Predictive value of MELD score for the evaluation of the outcome of patients undergone liver transplantation
LAI Tianshun,GUO Zhenhui,SU Lei,et al..Predictive value of MELD score for the evaluation of the outcome of patients undergone liver transplantation[J].Journal of Hepatopancreatobiliary Surgery,2008,20(6):407-410.
Authors:LAI Tianshun  GUO Zhenhui  SU Lei  
Institution:LAI Tianshun,GUO Zhenhui,SU Lei,et al.Department of Medical Intensive Care Unit(MICU),Guangzhou General Hospital of Guangzhou Military Comm,,Guangzhou 510010
Abstract:Objective To investigate the predictive value of model for end-stage liver disease(MELD) for the evaluation of the short-time outcome of patients underwent liver transplantation.Methods The MELD scores of 106 patients with end-stage liver disease undergone liver transplantation were calculated.Groups were categorized with the cut-off MELD scores with the highest Youden index,which were adopted from the best ROC curve for complications and death.And the early-stage complications and mortality in different gr...
Keywords:liver transplantation  model for end-stage liver disease  complication  mortality  predictive value  
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