Chen Jing, Jiang Jia-Ji, Zheng Qi, Zhu Yue-Yong
Liver Diseases Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China.
Zhonghua Yi Xue Za Zhi. 2009 Sep 8;89(33):2349-52.
To build a mathematical model for diagnosing liver fibrosis progression by using conventional laboratory indicators, and to evaluate its clinical value of predicting hepatic fibrosis and hepatocirrhosis in chronic hepatitis B.
Liver biopsy and routine laboratory tests were performed in 391 patients with chronic hepatitis B. Using Multiple logistic regression to analyse evidently relevant indicators,then the models predicting for different stages of liver fibrosis were built and analyzed by receiver operating characteristic (ROC) curve.
Age, platelet (PLT), international rate (INR), total bilirubin, albumin (ALB), aspartate aminotransferase, gamma-glutamyltranspeptidase (GGT), total bile acid and cholinesterase (CHE) were correlated with liver fibrosis stage. Multiple Logistic regression analysis showed that PLT, INR, ALB, GGT and CHE were independent predictors of three models ( S > or = 2, S > or = 3, S = 4). We finally built the predicting models and got Fibrosis scores (FS). ROC curve analysis revealed that the area under the curve was 0.784 in model-1 (S > or = 2), 0.768 in model-2 (S > or = 3) and 0.806 in model-3 (S = 4). A FS, cutoff point of 7.09 had 67.4% sensitivity, 79.3% specificity and 71.1% accuracy in Model-1. A FS2 cutoff point of 5.67 had 75.0% sensitivity, 67.7% specificity and 72.9% accuracy in Model-2. A FS3 cutoff point of 3.65 had 71.4% sensitivity, 78.5% specificity and 73.7% accuracy in Model-3.
The mathematical models using conventional laboratory indicators have fairly well value for predicting hepatic fibrosis progressing in chronic hepatitis B.
利用传统实验室指标建立诊断肝纤维化进展的数学模型，并评估其预测慢性乙型肝炎肝纤维化和肝硬化的临床价值。
对391例慢性乙型肝炎患者进行肝活检和常规实验室检查。采用多元逻辑回归分析明显相关指标，然后建立预测不同肝纤维化阶段的模型，并通过受试者工作特征（ROC）曲线进行分析。
年龄、血小板（PLT）、国际标准化比值（INR）、总胆红素、白蛋白（ALB）、天冬氨酸转氨酶、γ-谷氨酰转肽酶（GGT）、总胆汁酸和胆碱酯酶（CHE）与肝纤维化阶段相关。多元逻辑回归分析显示，PLT、INR、ALB、GGT和CHE是三个模型（S≥2、S≥3、S = 4）的独立预测因子。我们最终建立了预测模型并获得了纤维化评分（FS）。ROC曲线分析显示，模型1（S≥2）曲线下面积为0.784，模型2（S≥3）为0.768，模型3（S = 4）为0.806。在模型1中，FS截止点为7.09时，灵敏度为67.4%，特异度为79.3%，准确度为71.1%。在模型2中，FS2截止点为5.67时，灵敏度为75.0%，特异度为67.7%，准确度为72.9%。在模型3中，FS3截止点为3.65时，灵敏度为71.4%，特异度为78.5%，准确度为73.7%。
使用传统实验室指标的数学模型对预测慢性乙型肝炎肝纤维化进展具有较好的价值。
