在补偿的晚期慢性肝病中,第一个失补偿的非侵入性预测模型:元分析

Angus W Jeffrey1, James Chen2, Andrew Chin3

  • 1Department of Medicine, University of Western Australia, Perth, Australia; Department of Hepatology, Sir Charles Gairdner Hospital, Perth, Australia; Liver Transplant Unit, Austin Hospital, Melbourne, Australia.

概括

非侵入性预测模型 (NIT) 准确地识别了处于脱补偿风险的补偿晚期慢性肝病 (cACLD) 患者. cACLD特定的模型,如SAVE和ABC分数,显示出最好的预测性能.