AUTHOR=Li Xiaoke , Xing Yufeng , Zhou Daqiao , Xiao Huanming , Zhou Zhenhua , Han Zhiyi , Sun Xuehua , Li Shuo , Zhang Ludan , Li Zhiguo , Zhang Peng , Zhang Jiaxin , Zhang Ningyi , Cao Xu , Zao Xiaobin , Du Hongbo , Tong Guangdong , Chi Xiaoling , Gao Yueqiu , Ye Yong'an
TITLE=A Non-invasive Model for Predicting Liver Inflammation in Chronic Hepatitis B Patients With Normal Serum Alanine Aminotransferase Levels
JOURNAL=Frontiers in Medicine
VOLUME=8
YEAR=2021
URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.688091
DOI=10.3389/fmed.2021.688091
ISSN=2296-858X
ABSTRACT=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.