title: Toward the establishment of a prediction system for the personalized treatment of chronic hepatitis C.
abstract: BACKGROUND
  Although several direct-acting antivirals (DAAs) are now available, the therapy regimen for chronic hepatitis C will continue to include pegylated interferon and ribavirin for the foreseeable future. Despite their improved rate of sustained virological response (SVR), DAAs pose increased risks of side effects and selection for antiviral resistance. Not all patients require DAA to achieve SVR, whereas others are unlikely to respond even to triple therapy. Therefore, a personalized approach to candidate selection is necessary.
  
  
  METHODS
  In this retrospective study, data from 640 Japanese patients who were treated for chronic hepatitis C genotype 1, 2, or 3 with pegylated interferon plus ribavirin combination therapy was compiled to identify robust pretreatment predictive factors for SVR.
  
  
  RESULTS
  A logistic regression model for personalized therapy was developed based on age, viral genotype, initial viral load, aspartate aminotransferase/alanine aminotransferase ratio, α-fetoprotein levels, and IL28B single-nucleotide polymorphism genotype. The area under the receiver-operating characteristic curve (AUC) was 0.85. The mean AUC following 10 rounds of 10-fold cross validation was 0.82, with a true positive rate of 78.2%.
  
  
  CONCLUSIONS
  A personalized approach to therapy may better inform treatment decisions and reduce incidence of side effects and antiviral resistance.
authors: Laboratory for Digestive Diseases, Center for Genomic Medicine, RIKEN, Hiroshima.
authors: Hidenori
authors: H
authors: Ochi
authors: C Nelson
authors: CN
authors: Hayes
authors: Hiromi
authors: H
authors: Abe
authors: Yasufumi
authors: Y
authors: Hayashida
authors: Tomotaka
authors: T
authors: Uchiyama
authors: Naoyuki
authors: N
authors: Kamatani
authors: Yusuke
authors: Y
authors: Nakamura
authors: Kazuaki
authors: K
authors: Chayama
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000410:Alanine Transaminase / Q000097:blood; D000998:Antiviral Agents / Q000009:adverse effects / Q000627:therapeutic use*; D019540:Area Under Curve; D001219:Aspartate Aminotransferases / Q000097:blood; D002648:Child; D004359:Drug Therapy, Combination; D005260:Female; D005838:Genotype; D016174:Hepacivirus / Q000235:genetics*; D019698:Hepatitis C, Chronic / Q000188:drug therapy* / Q000235:genetics*; D006801:Humans; D000077190:Interferon alpha-2; D016898:Interferon-alpha / Q000627:therapeutic use*; D007372:Interferons; D007378:Interleukins / Q000235:genetics; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011092:Polyethylene Glycols / Q000627:therapeutic use*; D020641:Polymorphism, Single Nucleotide; D057285:Precision Medicine; D011237:Predictive Value of Tests; D012372:ROC Curve; D011994:Recombinant Proteins / Q000627:therapeutic use; D012189:Retrospective Studies; D012254:Ribavirin / Q000009:adverse effects / Q000627:therapeutic use*; D019562:Viral Load; D055815:Young Adult; D000095022:Interferon Lambda
chemical_list: D000410:Alanine Transaminase; D000998:Antiviral Agents; D001219:Aspartate Aminotransferases; D000077190:Interferon alpha-2; D016898:Interferon-alpha; D007372:Interferons; D007378:Interleukins; D011092:Polyethylene Glycols; D011994:Recombinant Proteins; D012254:Ribavirin; C000624353:interferon-lambda, human; D000095022:Interferon Lambda; C417083:peginterferon alfa-2b