Quantitative hepatitis B core antibody and quantitative hepatitis B surface antigen:Novel viral biomarkers for chronic hepatitis B management

The management of hepatitis B virus(HBV)infection now involves regular and appropriate monitoring of viral activity,disease progression,and treatment response.Traditional HBV infection biomarkers are limited in their ability to predict clinical outcomes or therapeutic effectiveness.Quantitation of HBV core antibodies(qAnti-HBc)is a novel non-invasive biomarker that may help with a variety of diagnostic issues.It was shown to correlate strongly with infection stages,hepatic inflammation and fibrosis,chronic infection exacerbations,and the presence of occult infection.Furthermore,qAnti-HBc levels were shown to be predictive of spontaneous or treatment-induced HBeAg and HBsAg seroclearance,relapse after medication termination,re-infection following liver transplantation,and viral reactivation in the presence of immunosuppression.qAnti-HBc,on the other hand,cannot be relied on as a single diagnostic test to address all problems,and its diagnostic and prognostic potential may be greatly increased when paired with qHBsAg.Commercial qAnti-HBc diagnostic kits are currently not widely available.Because many methodologies are only semi-quantitative,comparing data from various studies and defining universal cut-off values remains difficult.This review focuses on the clinical utility of qAnti-HBc and qHBsAg in chronic hepatitis B management.

Prediction of hepatic inflammation in chronic hepatitis B patients with a random forest-backward feature elimination algorithm

BACKGROUND Persistent liver inflammatory damage is the main risk factor for developing liver fibrosis,cirrhosis,and even hepatocellular carcinoma in chronic hepatitis B(CHB)patients.Thus,accurate prediction of the degree of liver inflammation is a high priority and a growing medical need.AIM To build an effective and robust non-invasive model for predicting hepatitis Brelated hepatic inflammation.METHODS A total of 650 treatment-naïve CHB(402 HBeAg-positive and 248 HBeAgnegative)patients who underwent liver biopsy were enrolled in this study.Histological inflammation grading was assessed by the Ishak scoring system.Serum quantitative hepatitis B core antibody(qAnti-HBc)levels and 21 immunerelated inflammatory factors were measured quantitatively using a chemiluminescent microparticle immunoassay.A backward feature elimination(BFE)algorithm utilizing random forest(RF)was used to select optional features and construct a combined model.The diagnostic abilities of the model or variables were evaluated based on the estimated area under the receiver operating characteristics curve(AUROC)and compared using the DeLong test.RESULTS Four features were selected to predict moderate-to-severe inflammation in CHB patients using the RF-BFE method.These predictive features included qAnti-HBc,ALT,AST,and CXCL11.Spearman’s correlation analysis indicated that serum qAnti-HBc,ALT,AST,and CXCL11 levels were positively correlated with the histology activity index(HAI)score.These selected features were incorporated into the model to establish a novel model named I-3A index.The AUROC[0.822;95%confidence interval(CI):0.790-0.851]of the I-3A index was significantly increased compared with qAnti-HBc alone(0.760,95%CI:0.724-0.792,P<0.0001)in all CHB patients.The use of an I-3A index cutoff value of 0.41 produced a sensitivity of 69.17%,specificity of 81.44%,and accuracy of 73.8%.Additionally,the I-3A index showed significantly improved diagnostic performance for predicting moderate-to-severe inflammation in HBeAg-positive and HBeAgnegative CHB patients(0.829,95%CI:0.789-0.865 and 0.810,95%CI:0.755-0.857,respectively).CONCLUSION The selected features of the I-3A index constructed using the RF-BFE algorithm can effectively predict moderate-to-severe liver inflammation in CHB patients.