Diagnostic accuracy, sensitivity, and specificity of serum AST, α-GST, ALT, ALP, and gender in predicting hepatitis B infection

Abstract

Aim Hepatitis B virus (HBV) infection remains a major public health concern, particularly in resource-limited settings. Accurate early detection is essential for effective intervention and disease control. This study evaluates the diagnostic performance of our previously developed predictive model based on gender and serum biomarkers. It assesses the diagnostic accuracy, sensitivity, and specificity of the predictive model for HBV status using gender, alpha-glutathione S-transferase (α-GST), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP). Methods A total of 304 participants (53.9% male, 46.1% female) were evenly stratified into HBV-positive and HBV-negative groups based on standard serological assays. Our previously published model was applied to this new dataset. Performance metrics, including sensitivity, specificity, and overall accuracy, were calculated. Results The model achieved a sensitivity of 78.1%, specificity of 77%, and overall accuracy of 77.6%, indicating strong diagnostic capability. These findings reinforce the earlier published results identifying AST as a significant predictor of HBV status, while α-GST, ALT, and ALP demonstrated lower individual predictive value. Conclusion The validated model offers a practical and efficient tool for HBV screening, especially in settings with limited diagnostic resources. Broader application across diverse populations is recommended to confirm generalizability and enhance public health outcomes.