Establishment and evaluation of noninvasive diagnostic models for liver fibrosis in patients with chronic hepatitis B
10.3760/cma.j.issn.1007-3418.2017.01.005
- VernacularTitle: 慢性乙型肝炎肝纤维化非创伤性诊断模型的建立与评价
- Author:
Qing YANG
1
;
Dongping LIU
2
;
Luping LI
1
;
Ye GU
1
;
Mingxiang ZHANG
1
;
Yue LIU
3
;
Kai YANG
1
Author Information
1. Department of Liver Diseases , the Sixth People’s Hospital of Shenyang City, Shenyang 110006, China
2. Department of Gastroenterology , the First Affiliated Hospital of China Medical University, Shenyang 110001, China
3. Department of Cadres Clinic, the Fourth People’s Hospital of Shenyang City, Shenyang 110031, China
- Publication Type:Journal Article
- Keywords:
Liver cirrhosis;
Hepatitis B, chronic;
Noninvasive;
Model
- From:
Chinese Journal of Hepatology
2017;25(1):15-20
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish the model of liver fibrosis based on noninvasive indices, and to investigate the diagnostic value of this model.
Methods:A total of 838 patients with chronic hepatitis B (CHB) who underwent liver biopsy in our hospital from March 2003 to October 2013 were selected, and the results of blood tests and B-ultrasound were collected. The correlation between these indices and liver fibrosis stage was analyzed. A logistic regression analysis was performed to establish a predictive model, and the value of this model was examined in validation group. The t-test, Mann-Whitney U non-parametric test, and chi-square test were used for data analysis. A Spearman rank correlation analysis was used for bivariate correlation analysis, and a dichotomous logistic stepwise regression analysis was used for multivariate analysis.
Results:In the model group, a model (FV) consisting of age, platelet count (PLT), γ-glutamyl transferase (GGT), albumin/globulin ratio (A/G), and splenic square area (SSA) was established. The areas under the receiver operating characteristic curve (AUROCs) of the model FV were 0.892, 0.910, and 0.915, respectively, in diagnosing significant liver fibrosis (S2-4), progressive liver fibrosis (S3-4), and early-stage liver cirrhosis (S4), with sensitivities of 77.6%, 83.7%, and 86.0%, respectively, specificities of 89.7%, 84.5%, and 83.7%, respectively, and accuracy of 82.1%, 84.2%, and 84.2%, respectively. There were no significant differences in AUROCs between the validation group and the model group (Z = 0.360, 0.885, and 0.046, all P > 0.05). In all patients, FV had significantly higher AUROCs in the diagnosis of liver fibrosis than FIB4 index and S index (Z = 4.569/3.423, 5.640/4.709, and 4.652/4.439, all P < 0.05). With < 0.374 and ≥ 0.577 as the cut-off values for the exclusion and diagnosis of significant liver fibrosis, 61.1% (512/838) of all patients could avoid liver biopsy, and the accuracy was 92.6% (474/512).
Conclusion:The noninvasive model based on age, PLT, GGT, A/G, and SSA can accurately predict liver fibrosis degree in patients with CHB with good reproducibility; therefore, it can be used for dynamic monitoring of liver fibrosis degree in clinical practice.
