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

Area of Science:

• Hepatology
• Virology
• Biostatistics

Background:

• Chronic hepatitis B virus (HBV) infection affects millions globally.
• Some HBV carriers with normal alanine transaminase (ALT) levels exhibit significant liver histological changes (SLHC).
• Accurate, noninvasive identification of SLHC is crucial for timely treatment decisions.

Purpose of the Study:

• To develop and validate a noninvasive nomogram model for identifying SLHC in chronic HBV carriers with normal ALT.
• To assess the model's performance across different upper limits of normal (ULNs) for ALT.

Main Methods:

• A training cohort of 732 chronic HBV carriers and an external validation cohort of 277 chronic HBV carriers were analyzed.
• Logistic regression and least absolute shrinkage and selection operator (LASSO) analyses were used.
• A nomogram model, HBGP, was constructed using hepatitis B surface antigen, gamma-glutamyl transpeptidase, and platelet count.

Main Results:

• The HBGP nomogram demonstrated strong predictive performance for SLHC, with an AUC of 0.866 in the training cohort and 0.885 in the validation cohort.
• High diagnostic values (AUCs ranging from 0.853 to 0.868) were observed across different ALT ULN strata.
• HBGP outperformed existing predictors in identifying SLHC.

Conclusions:

• The HBGP nomogram is a reliable, noninvasive tool for predicting SLHC in chronic HBV carriers with normal ALT.
• This model can assist clinicians in making informed decisions regarding the initiation of antiviral therapy.
• Further validation in diverse populations may enhance its clinical utility.