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Comparative Study
. 2018 Oct 18;8(1):15449.
doi: 10.1038/s41598-018-33412-z.

A novel diagnostic algorithm to predict significant liver inflammation in chronic hepatitis B virus infection patients with detectable HBV DNA and persistently normal alanine transaminase

Affiliations
Comparative Study

A novel diagnostic algorithm to predict significant liver inflammation in chronic hepatitis B virus infection patients with detectable HBV DNA and persistently normal alanine transaminase

Qiang Li et al. Sci Rep. .

Abstract

Significant liver inflammation might be found in 20-34% of chronic hepatitis B virus (HBV) infection patients with detectable HBV DNA and persistently normal alanine transaminase (ALT) (PNALT). We aimed to develop a diagnostic algorithm to predict significant liver inflammation in these specific patients. Using liver biopsy as the gold standard, we developed a novel, simple diagnostic algorithm to predict significant liver inflammation in a training set of 365 chronic HBV infection patients with detectable HBV DNA and PNALT, and validated the diagnostic accuracy in a validation set of 164 similar patients. The novel algorithm (AAGP) attributed to age, ALT, gamma-glutamyl transpeptidase (GGT), and platelet count was developed. In the training set, the area under the receiver operating characteristic curve (AUROC) of AAGP was higher than that of ALT and aspartate transaminase (AST), to diagnose significant liver inflammation (0.77, 0.67, and 0.59, respectively, p < 0.001). In the validation set, the AUROC of AAGP was also higher than ALT and AST (0.75, 0.61, and 0.54, respectively, p < 0.001). Using AAGP ≥2, the sensitivity and negative predictive value (NPV) was 91% and 93%, respectively, to diagnose significant liver inflammation. Using AAGP ≥8, the specificity and NPV was 91% and 86%, respectively, for significant liver inflammation. In conclusion, the AAGP algorithm is a novel, simple, user-friendly algorithm for the diagnosis of significant liver inflammation in chronic HBV infection patients with detectable HBV DNA and PNALT.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow diagram of the training set population. HBV, hepatitis B virus; NAFLD, non-alcoholic fatty liver disease; HCV, hepatitis C virus; HDV, hepatitis D virus; HIV, human immunodeficiency virus; ALT, alanine transaminase.
Figure 2
Figure 2
Flow diagram of the validation set population. HBV, hepatitis B virus; NAFLD, non-alcoholic fatty liver disease; HCV, hepatitis C virus; HDV, hepatitis D virus; HIV, human immunodeficiency virus; ALT, alanine transaminase.
Figure 3
Figure 3
ROC curves of noninvasive tests in the training (A) and validation set (B). The AAGP algorithm is the sum of the scores obtained form age, ALT, GGT, and platelet count.

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