Department of Gastroenterology and Hepatology, Faculty of Medicine, Saitama Medical University, 38 Morohongo, Moroyama-Machi, Iruma-gun, Saitama, 350-0495, Japan.
J Gastroenterol. 2012 Jun;47(6):664-77. doi: 10.1007/s00535-012-0529-8. Epub 2012 Mar 9.
We established algorithms to predict the prognosis of acute liver failure (ALF) patients through a data-mining analysis, in order to improve the indication criteria for liver transplantation.
The subjects were 1,022 ALF patients seen between 1998 and 2007 and enrolled in a nationwide survey. Patients older than 65 years, and those who had undergone liver transplantation and received blood products before the onset of hepatic encephalopathy were excluded. Two data sets were used: patients seen between 1998 and 2003 (n=698), whose data were used for the formation of the algorithm, and those seen between 2004 and 2007 (n=324), whose data were used for the validation of the algorithm. Data on a total of 73 items, at the onset of encephalopathy and 5 days later, were collected from 371 of the 698 patients seen between 1998 and 2003, and their outcome was analyzed to establish decision trees. The obtained algorithm was validated using the data of 160 of the 324 patients seen between 2004 and 2007.
The outcome of the patients at the onset of encephalopathy was predicted through 5 items, and the patients were classified into 6 categories with mortality rates between 23% and89%. When the prognosis of the patients in the categories with mortality rates greater than 50% was predicted as "death", the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the algorithm were 79, 78, 81, 83, and 75%, respectively. Similar high values were obtained when the algorithm was employed in the patients for validation. The outcome of the patients 5 days after the onset of encephalopathy was predicted through 7 items, and a similar high accuracy was found for both sets of patients.
Novel algorithms for predicting the outcome of ALF patients may be useful to determine the indication for liver transplantation.
通过数据挖掘分析，我们建立了预测急性肝衰竭（ALF）患者预后的算法，以改善肝移植的适应证标准。
本研究纳入了 1998 年至 2007 年间接受全国性调查的 1022 例 ALF 患者，排除年龄>65 岁、发生肝性脑病前已接受肝移植和血制品治疗的患者。使用了 2 组数据：1998 年至 2003 年（n=698）就诊的患者数据用于建立算法，2004 年至 2007 年（n=324）就诊的患者数据用于验证算法。从 1998 年至 2003 年就诊的 698 例患者中，共有 371 例患者在发生肝性脑病时和 5 天后收集了共 73 项总计数据，对其结局进行分析，建立决策树。使用 2004 年至 2007 年就诊的 160 例患者的数据对获得的算法进行验证。
通过 5 项指标预测了患者发生肝性脑病时的结局，并将患者分为 6 类，病死率为 23%~89%。当对病死率>50%的患者分类为“死亡”时，该算法的准确性、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）分别为 79%、78%、81%、83%和 75%，在验证组患者中也获得了类似的高值。通过 7 项指标预测了患者发生肝性脑病 5 天后的结局，在两组患者中均获得了较高的准确性。
预测 ALF 患者结局的新算法可能有助于确定肝移植的适应证。
