Predicting the presence of hepatitis B virus surface antigen in Chinese patients by pathology data mining.

Hepatitis B virus (HBV) is a pathogen of worldwide health significance, associated with liver disease. A vaccine is available, yet HBV prevalence remains a concern, particularly in developing countries. Pathology laboratories have a primary role in the diagnosis and monitoring of HBV infection, through hepatitis B surface antigen (HBsAg) immunoassay and associated tests. Analysis of HBsAg immunoassay and associated pathology data from 821 Chinese patients applied 10-fold cross-validation to establish classification decision trees (CDTs), with CDT results used subsequently to develop a logistic regression model. The robustness of logistic regression model was confirmed by the Hosmer-Lemeshow test, Pseudo-R(2) and an area under receiver operating characteristic curve (AUROC) result that showed the logistic regression model was capable of accurately discriminating the HBsAg positive from HBsAg negative patients at 95% accuracy. Overall CDT sensitivity and specificity was 94.7% (± 5.0%) and 89.5% (± 5.7%), respectively, close to the sensitivity and specificity of the immunoassay, providing an alternative to predict HBsAg status. Both the CDT and logistic regression modeling demonstrated the importance of the routine pathology variables alanine aminotransferase (ALT), serum albumin (ALB), and alkaline phosphatase (ALP) to accurately predict HBsAg status in a Chinese patient cohort. The study demonstrates that CDTs and a linked logistic regression model applied to routine pathology data were an effective supplement to HBsAg immunoassay, and a possible replacement method where immunoassays are not requested or not easily available for the laboratory diagnosis of HBV infection.