Development of a Predictive Model for Hepatitis B Virus (HBV) Status Using Gender and Serum Biomarkers

Early screening for the Hepatitis B Virus (HBV) is crucial for effective management and treatment, especially in resource-limited areas like Enugu State, Nigeria. This study aims to develop and validate a predictive model for HBV status. By integrating several factors, this research seeks to improve diagnostic accuracy and contribute to more effective HBV management, particularly in resource-limited settings. The study is focused on the following gender and serum biomarkers: alpha glutathione S-transferase (α-GST), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP) among patients at Enugu State University Teaching Hospital. Binary logistic regression was employed to analyze the predictive power of these variables. The results indicate that AST is a statistically significant predictor of HBV status. In contrast, gender, alpha GST, ALP, and ALT were not statistically significant predictors in the model.