Anti-HCV antibody titer highly predicts HCV viremia in patients with hepatitis B virus dual-infection

Background/Aims Hepatitis C Virus (HCV) infection is diagnosed by the presence of antibody to HCV and/or HCV RNA. This study aimed to evaluate the accuracy of anti-HCV titer (S/CO ratio) in predicting HCV viremia in patients with or without hepatitis B virus (HBV) dual infection. Methods Anti-HCV seropositive patients who were treatment-naïve consecutively enrolled. Anti-HCV antibodies were detected using a commercially chemiluminescent microparticle immunoassay. HCV RNA was detected by real-time PCR method. Results A total of 1321 including1196 mono-infected and 125 HBV dually infected patients were analyzed. The best cut-off value of anti-HCV titer in predicting HCV viremia was 9.95 (AUROC 0.99, P<0.0001). Of the entire cohort, the anti-HCV cut-off value of 10 provided the best accuracy, 96.8%, with the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 96.3%, 98.9%, 99.7% and 87.3% respectively. The best cut-off value of anti-HCV titer in predicting HCV viremia was 9.95 (AUROC 0.99, P<0.0001) and 9.36 (AUROC 1.00, P<0.0001) in patients with HCV mono-infection and HBV dual-infection respectively. Among the HBV dually infected patients, the accuracy of anti-HCV titer in predicting HCV viremia reached up to 100% with the cut-off value of 9. All the patients were HCV-viremic if their anti-HCV titer was greater than 9 (PPV 100%). On the other hand, all the patients were HCV non-viremic if their anti-HCV titer was less than 9 (NPV 100%). Conclusions Anti-HCV titer strongly predicted HCV viremia. This excellent performance could be generalized to either HCV mono-infected or HBV dually infected patients.

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PREVENTION OF PERINATAL TRANSMISSION OF HEPATITIS B VIRUS: THE SENSITIVITY, SPECIFICITY, AND PREDICTIVE VALUE OF THE RECOMMENDED SCREENING QUESTIONS TO DETECT HIGH-RISK WOMEN IN AN OBSTETRIC POPULATION

American Journal of Epidemiology ◽

10.1093/oxfordjournals.aje.a114680 ◽

1987 ◽

Vol 126 (3) ◽

pp. 484-491 ◽

Cited By ~ 9

Author(s):

GERALDINE M. McQUILLAN ◽

TIMOTHY R. TOWNSEND ◽

CATHERINE B. JOHANNES ◽

TODD DILLARD ◽

RICHARD A. MOLTENI ◽

...

Keyword(s):

Hepatitis B Virus ◽

High Risk ◽

Hepatitis B ◽

Predictive Value ◽

Perinatal Transmission ◽

High Risk Women ◽

B Virus ◽

Screening Questions ◽

Obstetric Population ◽

Sensitivity Specificity

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Significant Hepatic Fibrosis Among Treatment-Naive Chronic Hepatitis B Virus With Increased Hepatitis B Virus DNA and Normal Alanine Aminotransferase

Clinical Gastroenterology and Hepatology ◽

10.1016/j.cgh.2017.07.012 ◽

2018 ◽

Vol 16 (1) ◽

pp. 146-148

Author(s):

Robert J. Wong ◽

Angelica Le ◽

My T. Nguyen ◽

Huy N. Trinh ◽

Andrew Huynh ◽

...

Keyword(s):

Chronic Hepatitis ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Chronic Hepatitis B ◽

Hepatic Fibrosis ◽

Alanine Aminotransferase ◽

Chronic Hepatitis B Virus ◽

B Virus ◽

Treatment Naïve ◽

Normal Alanine Aminotransferase

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Novel Genetic Variants of Hepatitis B Virus in Fulminant Hepatitis

Journal of Pathogens ◽

10.1155/2017/1231204 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Jack Bee Chook ◽

Yun Fong Ngeow ◽

Kok Keng Tee ◽

Suat Cheng Peh ◽

Rosmawati Mohamed

Keyword(s):

Hepatitis B Virus ◽

Hepatitis B ◽

Predictive Value ◽

Fulminant Hepatitis ◽

Predictive Accuracy ◽

Stop Codon ◽

Acute Infection ◽

Support Vector ◽

Svm Model ◽

B Virus

Fulminant hepatitis (FH) is a life-threatening liver disease characterised by intense immune attack and massive liver cell death. The common precore stop codon mutation of hepatitis B virus (HBV), A1896, is frequently associated with FH, but lacks specificity. This study attempts to uncover all possible viral nucleotides that are specifically associated with FH through a compiled sequence analysis of FH and non-FH cases from acute infection. We retrieved 67 FH and 280 acute non-FH cases of hepatitis B from GenBank and applied support vector machine (SVM) model to seek candidate nucleotides highly predictive of FH. Six best candidates with top predictive accuracy, 92.5%, were used to build a SVM model; they are C2129 (85.3%), T720 (83.0%), Y2131 (82.4%), T2013 (82.1%), K2048 (82.1%), and A2512 (82.1%). This model gave a high specificity (99.3%), positive predictive value (95.6%), and negative predictive value (92.1%), but only moderate sensitivity (64.2%). We successfully built a SVM model comprising six variants that are highly predictive and specific for FH: four in the core region and one each in the polymerase and the surface regions. These variants indicate that intracellular virion/core retention could play an important role in the progression to FH.

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SERO-PREVALENCE AND PREDICTIVE STATISTICAL MODEL FOR THE HEPATITIS B VIRUS EXPOSURE AMONG PEOPLE IN COMMUNITY, YASOTHON, THAILAND

Malaysian Journal of Public Health Medicine ◽

10.37268/mjphm/vol.20/no.3/art.583 ◽

2020 ◽

Vol 20 (3) ◽

pp. 35-41

Author(s):

Thanom Namwong ◽

Choosak Nithikathkul ◽

Vorapoj Promsatayaprot ◽

Sumattana Glangkarn

Keyword(s):

Hepatitis B Virus ◽

Hepatitis B ◽

Statistical Model ◽

Predictive Model ◽

Risk Group ◽

Cross Sectional Study ◽

Operating Characteristics ◽

Cross Sectional ◽

B Virus ◽

Sensitivity Specificity

This study investigated the prevalence of Hepatitis B virus (HBV) and identified a predictive statistical model for the HBV exposure among people in the community, Yasothon, Thailand. A cross-sectional study was performed on participants over 26 years old and living in Muang district, Yasothon province, Thailand. The research was conducted from July to August 2019. All 1,258 participants were verbally screened. Four hundred and fifty nine people were the risk group and tested for HBsAg, and 18 cases were positive for HBsAg (3.9% [95%CI 3.5-4.4]). For the predictive model, the HBV exposure connected with sex, marital status, alcohol, smoking, and knowledge. The area under the receiver operating characteristics (ROC) curve was 61.8 % (95%CI, 58.6 to 65.0). At cut-off-point -0.66, the sensitivity, specificity and accuracy were 72.6%, 42.4 % and 53.4%, respectively. HBV infection was a serious health problem, it can cause cirrhosis and liver cancer in the future. The predictive model of five variables can predict risk exposure of HBV which may had other relevant factors. Verbal screening by questionnaire to classify HBsAg risk group can lower the implement cost.

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