scholarly journals Role of quantitative hepatitis B core antibody levels in predicting significant liver inflammation in chronic hepatitis B patients with normal or near-normal alanine aminotransferase levels

2017 ◽  
Vol 48 (3) ◽  
pp. E133-E145 ◽  
Author(s):  
Jing Li ◽  
Tian-Ying Zhang ◽  
Liu-Wei Song ◽  
Xun Qi ◽  
Xue-Ping Yu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Alanine Aminotransferase ◽  
Liver Inflammation ◽  
Normal Alanine Aminotransferase ◽  
Antibody Levels

scholarly journals A Non-invasive Model for Predicting Liver Inflammation in Chronic Hepatitis B Patients With Normal Serum Alanine Aminotransferase Levels

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaoke Li ◽  
Yufeng Xing ◽  
Daqiao Zhou ◽  
Huanming Xiao ◽  
Zhenhua Zhou ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Liver Biopsy ◽  
Chronic Hepatitis B ◽  
Alanine Aminotransferase ◽  
Liver Inflammation ◽  
Timely Initiation ◽  
Invasive Approach ◽  
Non Invasive ◽  
Normal Alt

Background and Aims: Chronic hepatitis B (CHB) patients with normal alanine aminotransferase (ALT) levels are at risk of disease progression. Currently, liver biopsy is suggested to identify this population. We aimed to establish a non-invasive diagnostic model to identify patients with significant liver inflammation.Method: A total of 504 CHB patients who had undergone liver biopsy with normal ALT levels were randomized into a training set (n = 310) and a validation set (n = 194). Independent variables were analyzed by stepwise logistic regression analysis. After the predictive model for diagnosing significant inflammation (Scheuer's system, G ≥ 2) was established, a nomogram was generated. Discrimination and calibration aspects of the model were measured using the area under the receiver operating characteristic curve (AUC) and assessment of a calibration curve. Clinical significance was evaluated by decision curve analysis (DCA).Result: The model was composed of 4 variables: aspartate aminotransferase (AST) levels, γ-glutamyl transpeptidase (GGT) levels, hepatitis B surface antigen (HBsAg) levels, and platelet (PLT) counts. Good discrimination and calibration of the model were observed in the training and validation sets (AUC = 0.87 and 0.86, respectively). The best cutoff point for the model was 0.12, where the specificity was 83.43%, the sensitivity was 77.42%, and the positive likelihood and negative likelihood ratios were 4.67 and 0.27, respectively. The model's predictive capability was superior to that of each single indicator.Conclusion: This study provides a non-invasive approach for predicting significant liver inflammation in CHB patients with normal ALT. Nomograms may help to identify target patients to allow timely initiation of antiviral treatment.

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