Determining optimal ALT cut-off values for predicting significant hepatic histological changes in patients with normal ALT in the grey zone of chronic hepatitis B virus infection.

BACKGROUND AND AIMS: We aimed to define gender-specific, optimal alanine aminotransferase (ALT) cut-off values for the prediction of significant liver histological changes (SLHC) in Chinese patients with grey zone (GZ) chronic hepatitis B (CHB) and normal ALT. METHODS: In a retrospective study, we included 1101 consecutive patients with GZ CHB and normal ALT assigned to training or internal validation cohorts. We included an independent cohort of 842 patients for external validation. We performed receiver operating characteristic (ROC) curve, smoothed curve fitting, and threshold effect analyses to determine optimal ALT cut-off values. Area under the curve (AUC) values were calculated to assess their predictive performance. RESULTS: A proportion of 79.3% of patients with GZ CHB and normal ALT (≤40 U/L) had SLHC. ROC curve analysis initially identified optimal ALT cut-off values of 29 U/L (male) and 22 U/L (female). After smoothed curve fitting and threshold effect analyses, new optimal cut-off values were 27 U/L for males and 24 U/L for females. AUCs for these values were 0.836 (male) and 0.833 (female) in the internal validation cohort, and 0.849 (male) and 0.844 (female) in the external validation cohort. The accuracy and discriminative ability of the newly defined ALT cut-off values were greater than those of the current recommendations. CONCLUSION: This study established novel optimal ALT cut-off values for more precise prediction of SLHC among Chinese patients with GZ CHB and normal ALT levels. This may help identify individuals who will benefit from timely antiviral therapy.

Liver Stiffness Measurement can Predict Liver Inflammation in Chronic Hepatitis B Patients with Normal Alanine Transaminase.

Background and Aims: To determine whether liver stiffness measurement (LSM) indicates liver inflammation in chronic hepatitis B (CHB) with different upper limits of normal (ULNs) for alanine aminotransferase (ALT). Methods: We grouped 439 CHB patients using different ULNs for ALT: cohort I, ≤40 U/L (439 subjects); cohort II, ≤35/25 U/L (males/females; 330 subjects); and cohort III, ≤30/19 U/L (males/females; 231 subjects). Furthermore, 84 and 96 CHB patients with normal ALT (≤40 U/L) formed the external and prospective validation groups, respectively. We evaluated the correlation between LSM and biopsy-confirmed liver inflammation, and determined diagnostic accuracy using area under the curve (AUC). A noninvasive LSM-based model was developed using multivariate logistic regression. Results: Fibrosis-adjusted LSM values significantly increased with increasing inflammation. The AUCs of LSM in cohorts I, II, and III were 0.799, 0.796, and 0.814, respectively, for significant inflammation (A≥2) and 0.779, 0.767, and 0.770, respectively, for severe inflammation (A=3). Cutoff LSM values in all cohorts for A≥2 and A=3 were 6.3 and 7.5 kPa, respectively. Internal, external, and prospective validations showed high diagnostic accuracy of LSM for A≥2 and A=3, and no significant differences in AUCs among the four groups. LSM and globulin independently predicted A≥2. The AUC of an LSM-globulin model for A≥2 exceeded those of globulin, ALT, and AST, but was similar to that of LSM. Conclusions: LSM predicted liver inflammation and guided the indication of antiviral therapy for CHB in patients with normal ALT.

Development a novel nomogram model for predicting significant hepatic histological changes in chronic hepatitis B virus carriers.

A proportion of chronic hepatitis B virus (HBV) carriers with normal alanine transaminase (ALT) present with significant liver histological changes (SLHC). To construct a noninvasive nomogram model to identify SLHC in chronic HBV carriers with different upper limits of normal (ULNs) for ALT. The training cohort consisted of 732 chronic HBV carriers who were stratified into four sets according to different ULNs for ALT: chronic HBV carriers I, II, III, and IV. The external validation cohort comprised 277 chronic HBV carriers. Logistic regression and least absolute shrinkage and selection operator analyses were applied to develop a nomogram model to predict SLHC. A nomogram model-HBGP (based on hepatitis B surface antigen, gamma-glutamyl transpeptidase, and platelet count) demonstrated good performance in diagnosing SLHC with area under the curve (AUCs) of 0.866 (95% confidence interval [CI]: 0.839-0.892) and 0.885 (95% CI: 0.845-0.925) in the training and validation cohorts, respectively. Furthermore, HBGP displayed high diagnostic values for SLHC with AUCs of 0.866 (95% CI: 0.839-0.892), 0.868 (95% CI: 0.838-0.898), 0.865 (95% CI: 0.828-0.901), and 0.853 (95% CI: 0.798-0.908) in chronic HBV carriers I, II, III, and IV, respectively. Additionally, HBGP showed greater ability in predicting SLHC compared with the existing predictors. HBGP has shown high predictive performance for SLHC, and thus may lead to an informed decision on the initiation of antiviral treatment.

Advantages of a Novel Model for Predicting Hepatic Fibrosis in Chronic Hepatitis B Virus Carriers Compared with APRI and FIB-4 Scores.

Background and Aims: Aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) are widely used to assess liver fibrosis in chronic hepatitis B virus (HBV) infection. Currently, the definition of normal alanine aminotransferase (ALT) is controversial. We aimed to examine the diagnostic value of APRI and FIB-4 in chronic HBV carriers with different upper limits of normal (ULNs) for ALT. Methods: 581 chronic HBV carriers were divided into the following four groups based on different ULNs for ALT: chronic HBV carriers I, II, III, and IV. Furthermore, 106 chronic HBV carriers formed an external validation group. Predictive values of APRI and FIB-4 were elucidated using the area under the curve (AUC). A liver fibrosis-predictive model-GPSA (named for its measure of gamma glutamyl transpeptidase, platelet count, HBsAg and albumin) was developed using multivariate logistic regression analysis. Results: In chronic HBV carriers I, the AUCs of APRI and FIB-4 were 0.680 and 0.609 for significant fibrosis and 0.678 and 0.661 for cirrhosis, respectively. The AUCs of GPSA for significant fibrosis in the training group, internal group, and external validation group were 0.877, 0.837, and 0.871, respectively. The diagnostic value of GPSA differed among chronic HBV carriers I, II, III, and IV, with AUCs for significant fibrosis being 0.857, 0.853, 0.868, and 0.905 and AUCs for cirrhosis being 0.901, 0.905, 0.886, and 0.913, respectively. GPSA showed a higher diagnostic value than APRI and FIB-4 for predicting significant fibrosis in the four groups. Conclusions: The GPSA model allows for accurate diagnosis of liver fibrosis in chronic HBV carriers with different ULN for ALT.