Construction of a non-invasive model to predict liver fibrosis in HBeAg negative hepatitis B with alanine aminotransferase less than 2 upper limit of normal
Construction of a non-invasive model to predict liver fibrosis in HBeAg negative hepatitis B with alanine aminotransferase less than 2 upper limit of normal
Objective:To evaluate influencing factors of liver fibrosis in HBeAg negative hepatitis B with alanine aminotransferase ( ALT) less than 2 upper limit of normal( ULN) and establish the non-invasive prediction model to assess the severity of liver fibrosis. Methods:The clinical data of 295 patients in HBeAg negative CHB patients with ALT≤2ULN were retrospectively analyzed. The degree of liver fibrosis S≥2 was taken as the discriminant criterion for significant liver fibrosis according to the pathological results of liver puncture. There were 94 cases in the mild group of liver fibrosis ( S≤1 ) and 201 cases in the significant group ( S≥2 ) . The independent predictors of liver fibrosis were screened by multivariate logistic regression analysis and non-invasive model was constructed. Finally,the model was evaluated by area under the receiver operating characteristic curve to identify the severity of liver fibrosis. Results:Multivariate logistic regression analysis showed that aspartate aminotransferase and hepatitis B core antibody were the independent predictors of liver fibrosis (P <0. 01). The AUC of this model was 0. 721(95％CI:0. 660 -0. 782,P <0. 01). The sensitivity and specificity for the diagnosis of significant liver fibrosis were 60. 0％ and 74. 5％. Conclusions:The non-invasive prediction model based on the two indicators of aspartate aminotransferase and hepatitis B core antibody has high diagnostic value for evaluating the severity of liver fibrosis in CHB.
关键词
慢性乙型肝炎/肝纤维化/血清学指标/无创预测模型
Key words
chronic hepatitis B/liver fibrosis/serological indicators/non-invasive prediction model
维普
万方数据