4.5 Article

Novel scoring system as a useful model to predict the outcome of patients with acute liver failure: Application to indication criteria for liver transplantation

期刊

HEPATOLOGY RESEARCH
卷 42, 期 1, 页码 68-75

出版社

WILEY-BLACKWELL
DOI: 10.1111/j.1872-034X.2011.00902.x

关键词

acute liver failure; fulminant hepatitis; guideline; indication criteria; liver transplantation; outcome prediction

资金

  1. Ministry of Health, Labor, and Welfare of Japan

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Aim: In Japan, the indication for liver transplantation in patients with acute liver failure (ALF) is currently determined according to the guideline published in 1996. However, its predictive accuracy has fallen in recent patients. Thus, we attempted to establish a new guideline. Methods: The subjects were 1096 ALF patients enrolled in a nationwide survey. All patients showed a prothrombin time < 40% of the standardized value and grade II or more severe hepatic encephalopathy. A multiple logistic regression analysis and receiver operating characteristic analysis were performed in 698 patients seen between 1998 and 2003 to identify significant parameters determining the outcome of patients. The extracted parameters were graded as numerical scores. An established scoring system was validated in patients seen between 2004 and 2008. Results: Six parameters were identified and graded as 0, 1 and/or 2; the interval between disease onset and development of hepatic encephalopathy, prothrombin time, serum total bilirubin concentration, the ratio of direct to total bilirubin concentration, peripheral platelet count and the presence of liver atrophy. When the prognosis of the patients with total score of 5 or more was judged as death, the predictive accuracy was 0.80 with sensitivity, specificity, positive predictive value and negative predictive value greater than 0.70. The values were similarly high in patients for validation. Conclusion: Novel scoring system for predicting the outcome of ALF patients may be useful to determine the indication of liver transplantation, since the system showed high predictive accuracy even after validation.

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