title: A predictive scoring system for the seroclearance of HBsAg in HBeAg-seronegative chronic hepatitis B patients with genotype B or C infection.
abstract: OBJECTIVE
  Seroclearance of hepatitis B surface antigen (HBsAg) is the most ideal end point in the treatment of chronic hepatitis B. This study develops a predictive scoring system to assess whether the addition of serum levels HBsAg may improve the predictability of HBsAg loss.
  
  
  METHODS
  This study included 2491 untreated participants with genotype B or C HBV infection, who were HBsAg-seropositive, HBeAg-seronegative, anti-HCV-seronegative, and cirrhosis free at study entry. Regression coefficients of predictors in Cox Regression models were converted into integer scores for predicting HBsAg seroclearance. Predictive accuracy was assessed with area under the receiver operating characteristic curves (AUROC), and predictive accuracies of models with and without serum HBsAg levels were compared.
  
  
  RESULTS
  Low serum levels of both HBsAg and HBV DNA were the strongest predictors of spontaneous HBsAg seroclearance. Compared to baseline serum HBsAg levels ≥1000 IU/ml, the multivariate adjusted rate ratio of spontaneous HBsAg seroclearance was 10.96 (7.92-15.16) for those with baseline serum HBsAg levels <100 IU/ml. The predictive ability of HBsAg levels was modified by HBV viral load, showing a weaker effect in those with higher viral loads, and the strongest effect among those with undetectable viral loads. The inclusion of serum HBsAg levels greatly improved the AUROC for predicting HBsAg seroclearance at the fifth (from 0.79 [0.787-0.792] to 0.89 [0.889-0.891]) and tenth year (from 0.73 [0.728-0.732] to 0.84 [0.839-0.841]) after study entry.
  
  
  CONCLUSIONS
  Incorporated into an easy-to-use scoring system, HBV viral load and quantitative serum HBsAg levels can accurately predict HBsAg seroclearance.
authors.affiliation: Graduate Institute of Epidemiology and Preventative Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Jessica
authors.initials: J
authors.lastname: Liu
authors.forename: Mei-Hsuan
authors.initials: MH
authors.lastname: Lee
authors.forename: Richard
authors.initials: R
authors.lastname: Batrla-Utermann
authors.forename: Chin-Lan
authors.initials: CL
authors.lastname: Jen
authors.forename: Uchenna H
authors.initials: UH
authors.lastname: Iloeje
authors.forename: Sheng-Nan
authors.initials: SN
authors.lastname: Lu
authors.forename: Li-Yu
authors.initials: LY
authors.lastname: Wang
authors.forename: San-Lin
authors.initials: SL
authors.lastname: You
authors.forename: Chuhsing K
authors.initials: CK
authors.lastname: Hsiao
authors.forename: Hwai-I
authors.initials: HI
authors.lastname: Yang
authors.forename: Chien-Jen
authors.initials: CJ
authors.lastname: Chen
mesh_terms: D000328:Adult; D000368:Aged; D000998:Antiviral Agents / Q000627:therapeutic use; D015415:Biomarkers / Q000097:blood; D004279:DNA, Viral / Q000097:blood; D005260:Female; D005838:Genotype; D006514:Hepatitis B Surface Antigens / Q000097:blood*; D006513:Hepatitis B e Antigens / Q000097:blood*; D006515:Hepatitis B virus / Q000235:genetics*; D019694:Hepatitis B, Chronic / Q000188:drug therapy* / Q000276:immunology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012699:Serology / Q000379:methods*; D016896:Treatment Outcome; D019562:Viral Load
chemical_list: D000998:Antiviral Agents; D015415:Biomarkers; D004279:DNA, Viral; D006514:Hepatitis B Surface Antigens; D006513:Hepatitis B e Antigens