title: Development and validation of two models for early prediction of response to therapy in genotype 1 chronic hepatitis C.
abstract: Early prediction of response to therapy in genotype 1 chronic hepatitis C is difficult. Two predictive models, a pretreatment scoring model (PreT-SM) and a fourth week of therapy scoring model (4w-SM) were constructed in a cohort of 104 patients from a single center (estimation cohort) and validated in a cohort of 141 patients from four independent centers (validation cohort). Individual scores were calculated using variables independently associated with sustained virological response (SVR). Baseline viral load, aspartate aminotransferase/alanine aminotransferase ratio, serum cholesterol, and a numerical score for noninvasive estimation of liver fibrosis were included in the PreT-SM; HCV RNA clearance and PreT-SM scores were included in the 4w-SM. Receiver operating characteristic analysis revealed the area under the curve in the estimation cohort and in the validation cohort to be, respectively, 0.856 and 0.847 for the PreT-SM and 0.908 and 0.907 for the 4w-SM. Low scores were associated with SVR, high scores with non-SVR. The best cutoff scores from the PreT-SM (7 and 9.70) identified, respectively, 36% of patients with SVR and 41% of those with non-SVR from the validation cohort, with high accuracy (> or =90% positive predictive value [PPV] and specificity). Similarly, cutoff scores of 3.20 and 5.60 from the 4w-SM identified, respectively, 71% of patients with SVR and 53% of those with non-SVR from the same cohort with high accuracy (PPV and specificity >92%). In conclusion, these models predicted response to therapy before or after 4 weeks of treatment in approximately 60% of genotype 1 patients and may be valuable for the management of this condition.
authors.affiliation: Liver Unit, Hospital Clínic, IDIBAPS, Barcelona, Spain.
authors.forename: Eva
authors.initials: E
authors.lastname: Martínez-Bauer
authors.forename: Javier
authors.initials: J
authors.lastname: Crespo
authors.forename: Manuel
authors.initials: M
authors.lastname: Romero-Gómez
authors.forename: Ricardo
authors.initials: R
authors.lastname: Moreno-Otero
authors.forename: Ricard
authors.initials: R
authors.lastname: Solá
authors.forename: Nancy
authors.initials: N
authors.lastname: Tesei
authors.forename: Fernando
authors.initials: F
authors.lastname: Pons
authors.forename: Xavier
authors.initials: X
authors.lastname: Forns
authors.forename: José M
authors.initials: JM
authors.lastname: Sánchez-Tapias
mesh_terms: D000328:Adult; D000410:Alanine Transaminase / Q000097:blood; D001219:Aspartate Aminotransferases / Q000097:blood; D015331:Cohort Studies; D005260:Female; D005838:Genotype; D016174:Hepacivirus / Q000145:classification* / Q000235:genetics; D019698:Hepatitis C, Chronic / Q000188:drug therapy* / Q000821:virology*; D006801:Humans; D000077190:Interferon alpha-2; D016898:Interferon-alpha / Q000627:therapeutic use; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011092:Polyethylene Glycols / Q000627:therapeutic use; D012367:RNA, Viral / Q000097:blood; D011994:Recombinant Proteins; D012254:Ribavirin / Q000627:therapeutic use
chemical_list: D000077190:Interferon alpha-2; D016898:Interferon-alpha; D012367:RNA, Viral; D011994:Recombinant Proteins; D011092:Polyethylene Glycols; D012254:Ribavirin; D001219:Aspartate Aminotransferases; D000410:Alanine Transaminase; C100416:peginterferon alfa-2a