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.

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.