A Non-invasive Model for Predicting Liver Inflammation in Chronic Hepatitis B Patients With Normal Serum Alanine Aminotransferase Levels.
A Non-invasive Model for Predicting Liver Inflammation in Chronic Hepatitis B Patients With Normal Serum Alanine Aminotransferase Levels.
复制标题
预测血清丙氨酸转氨酶水平正常的慢性乙型肝炎患者肝脏炎症的非侵入性模型
DOI：
10.3389/fmed.2021.688091
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发表时间：
2021
影响因子：
3.9
通讯作者：
Ye Y
中科院分区：
文献类型：
--
作者：
Li X;Xing Y;Zhou D;Xiao H;Zhou Z;Han Z;Sun X;Li S;Zhang L;Li Z;Zhang P;Zhang J;Zhang N;Cao X;Zao X;Du H;Tong G;Chi X;Gao Y;Ye Y
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.
影响因子：
3.7
作者：
Hong MZ;Zhang RM;Chen GL;Huang WQ;Min F;Chen T;Xu JC;Pan JS
通讯作者：
Pan JS