The prediction and validation of liver fibrosis by a noninvasive model and validation in patients with chronic hepatitis B
- VernacularTitle:慢性乙型肝炎肝纤维化无创检测模型建立及应用价值验证
- Author:
Weiping LIU
;
Dejun XU
;
Lianrong ZHAO
;
Zhonghua LU
;
Yuhua WANG
;
Zhenwei LANG
;
Guiqiang WANG
- Publication Type:Journal Article
- Keywords:
Hepatitis B,chronic;
Liver fibrosis;
Diagnosis;
Predictive model
- From:
Chinese Journal of Internal Medicine
2008;47(4):308-312
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a simple model for the noninvasive diagnosis of liver fibrosis in patients with chronic hepatitis B and to testify its diagnostic value. Methods One hundred and ninety patients with chronic hepatitis B who had undergone liver biopsy were divided into 2 groups:one for developing the model(n=110) and one for validation(n=80). Histological staging of liver fibrosis,assessed blindly and independently by 2 pathologists,was determined according to Scheuer fibrosis score.Twenty markers involved in the study were analyzed initially in the estimation group to derive a predictive model to discriminate the stages of fibrosis.The model created was then assessed with receiver operating characteristic curve(ROC)analysis. It was also applied to the validation group to test its accuracy. Results Haptoglobin(HPT),γ-glutamyl transpeptidase(GGT)and platelet were identified by logistic regression analysis as independent factors of fibrosis. A model developed from the above three markers was established to predict the stage of fibrosis(S). In ROC analysis,the area under curve(AUC) for identifying S≥1,S≥2,S≥3 and S=4 was 0.832,0.835,0.820 and 0.843 respectively. The model had a similar AUC in the vatidation group without statistically significant difierence. Using a cut-off of ＜0.18, significant fibrosis (S≥2)could be excluded in 27 patients of the total patient population(negative predictive value 90%).Similarly,applying a cut-off ≥0.70,significant fibrosis could be identified correctly in 67 patients of the total patient population(positive predictive value 82.7%).The model had a high level of diagnostic value in patients with HBeAg-positive chronic hepatitis B as well as in patients with HBeAg-negative chronic hepatitis B(AUC for identifying S≥2,0.857 vs 0.802). Restricting biopsy to patients with intermediate scores(≥0.70 and ＜0.18) may prevent liver biopsies in 58.4% of the patients while maintaining 84.7% accuracy. Conclusions A model including HPT,GGT and platelet is a simple and reliable index for predicting significant fibrosis in patients with HBeAg-positive chronic hepatitis B as well as in patients with HBeAg-negative chronic hepatitis B.
