目前临床上大多使用Child-Pugh分级来评估肝癌患者的肝功能及治疗相关的预后预测,但由于Child-Pugh分级评价指标中的腹水及白蛋白水平存在关联,且腹水及肝性脑病分度存在较大主观性,降低了评分的客观性和精确性。ALBI分级是Philip J. Jo...目前临床上大多使用Child-Pugh分级来评估肝癌患者的肝功能及治疗相关的预后预测,但由于Child-Pugh分级评价指标中的腹水及白蛋白水平存在关联,且腹水及肝性脑病分度存在较大主观性,降低了评分的客观性和精确性。ALBI分级是Philip J. Johnson等人于2014年提出的评估肝功能的新方法,是一个仅包含白蛋白和胆红素两项客观指标的统计模型。本文就近年来ALBI分级在肝癌患者疗效和预后的评估、ALBI的改良及与其他预后评分的结合予以总结并进行综述。展开更多
Background & Objectives: Hepatocellular carcinoma (HCC) leads to high morbidity and mortality. Various models have been proposed for predicting the outcome of patients with HCC. We aim to compare the prognostic ab...Background & Objectives: Hepatocellular carcinoma (HCC) leads to high morbidity and mortality. Various models have been proposed for predicting the outcome of patients with HCC. We aim to compare the prognostic abilities of Child-Pugh, MELD, MELD-Na, and ALBI scores for predicting in-hospital mortality of HCC. Methods: We enrolled patients diagnosed with liver cirrhosis and HCC from May 2017 through May 2018. We further divided eligible patients into hepatitis B virus (HBV), patients without ascites, and patients with ascites subgroups. Areas under the characteristic curves (AUCs) were analyzed. Results: A total of 495 patients were included in the study. We collected data on patients at admission. A majority of patients were infected with HBV (91.5%). None of them were complicated with hepatic encephalopathy. Only 14.9% of patients presented with ascites. In the whole population, AUCs with 95% confidence interval (CI) of Child-Pugh, ALBI, MELD, and MELD-Na scores in predicting in-hospital mortality were 0.889 (95% CI: 0.858 - 0.915), 0.849 (95% CI: 0.814 - 0.879), 0.669 (95% CI: 0.626 - 0.711), and 0.721 (95% CI: 0.679 - 0.760), respectively. In the patients without ascites subgroup, Child-Pugh showed better discriminatory ability than ALBI score in predicting in-hospital mortality (P = 0.0002), while there were no significant differences among other comparisons. Conclusions: Child-Pugh and ALBI may be useful predictors for predicting in-hospital mortality in whole patients, in patients with HBV infection, and in patients without ascites. In HCC patients with ascites, MELD-Na may be effective for predicting in-hospital mortality.展开更多
文摘目前临床上大多使用Child-Pugh分级来评估肝癌患者的肝功能及治疗相关的预后预测,但由于Child-Pugh分级评价指标中的腹水及白蛋白水平存在关联,且腹水及肝性脑病分度存在较大主观性,降低了评分的客观性和精确性。ALBI分级是Philip J. Johnson等人于2014年提出的评估肝功能的新方法,是一个仅包含白蛋白和胆红素两项客观指标的统计模型。本文就近年来ALBI分级在肝癌患者疗效和预后的评估、ALBI的改良及与其他预后评分的结合予以总结并进行综述。
文摘Background & Objectives: Hepatocellular carcinoma (HCC) leads to high morbidity and mortality. Various models have been proposed for predicting the outcome of patients with HCC. We aim to compare the prognostic abilities of Child-Pugh, MELD, MELD-Na, and ALBI scores for predicting in-hospital mortality of HCC. Methods: We enrolled patients diagnosed with liver cirrhosis and HCC from May 2017 through May 2018. We further divided eligible patients into hepatitis B virus (HBV), patients without ascites, and patients with ascites subgroups. Areas under the characteristic curves (AUCs) were analyzed. Results: A total of 495 patients were included in the study. We collected data on patients at admission. A majority of patients were infected with HBV (91.5%). None of them were complicated with hepatic encephalopathy. Only 14.9% of patients presented with ascites. In the whole population, AUCs with 95% confidence interval (CI) of Child-Pugh, ALBI, MELD, and MELD-Na scores in predicting in-hospital mortality were 0.889 (95% CI: 0.858 - 0.915), 0.849 (95% CI: 0.814 - 0.879), 0.669 (95% CI: 0.626 - 0.711), and 0.721 (95% CI: 0.679 - 0.760), respectively. In the patients without ascites subgroup, Child-Pugh showed better discriminatory ability than ALBI score in predicting in-hospital mortality (P = 0.0002), while there were no significant differences among other comparisons. Conclusions: Child-Pugh and ALBI may be useful predictors for predicting in-hospital mortality in whole patients, in patients with HBV infection, and in patients without ascites. In HCC patients with ascites, MELD-Na may be effective for predicting in-hospital mortality.
