BACKGROUND: Staging hepatic fibrosis by liver biopsy guides prognosis and treatment of hepatitis C, but is invasive and expensive. We sought to create an algorithm of serum markers that accurately and reliably predict liver fibrosis stage among hepatitis C patients. METHODS: Ten biochemical markers were measured at time of liver biopsy in 117 untreated hepatitis C patients (training set). Multivariate logistic regression and ROC curve analyses were used to create a predictive model for significant fibrosis (METAVIR F2, F3, and F4), advanced fibrosis (F3 and F4), and cirrhosis (F4). The model was validated in 104 patients from other institutions. RESULTS: A model (Hepascore) of bilirubin, gamma-glutamyltransferase, hyaluronic acid, alpha(2)-macroglobulin, age, and sex produced areas under the ROC curves (AUCs) of 0.85, 0.96, and 0.94 for significant fibrosis, advanced fibrosis, and cirrhosis, respectively. In the training set, a score > or = 0.5 (range, 0.0-1.0) was 92% specific and 67% sensitive for significant fibrosis, a score <0.5 was 81% specific and 95% sensitive for advanced fibrosis, and a score <0.84 was 84% specific and 71% sensitive for cirrhosis. Among the validation set, the AUC for significant fibrosis, advanced fibrosis, and cirrhosis were 0.82, 0.90, and 0.89, respectively. A score > or = 0.5 provided a specificity and sensitivity of 89% and 63% for significant fibrosis, whereas scores <0.5 had 74% specificity and 88% sensitivity for advanced fibrosis. CONCLUSIONS: A model of 4 serum markers plus age and sex provides clinically useful information regarding different fibrosis stages among hepatitis C patients.
BACKGROUND: Staging hepatic fibrosis by liver biopsy guides prognosis and treatment of hepatitis C, but is invasive and expensive. We sought to create an algorithm of serum markers that accurately and reliably predict liver fibrosis stage among hepatitis C patients. METHODS: Ten biochemical markers were measured at time of liver biopsy in 117 untreated hepatitis C patients (training set). Multivariate logistic regression and ROC curve analyses were used to create a predictive model for significant fibrosis (METAVIR F2, F3, and F4), advanced fibrosis (F3 and F4), and cirrhosis (F4). The model was validated in 104 patients from other institutions. RESULTS: A model (Hepascore) of bilirubin, gamma-glutamyltransferase, hyaluronic acid, alpha(2)-macroglobulin, age, and sex produced areas under the ROC curves (AUCs) of 0.85, 0.96, and 0.94 for significant fibrosis, advanced fibrosis, and cirrhosis, respectively. In the training set, a score > or = 0.5 (range, 0.0-1.0) was 92% specific and 67% sensitive for significant fibrosis, a score <0.5 was 81% specific and 95% sensitive for advanced fibrosis, and a score <0.84 was 84% specific and 71% sensitive for cirrhosis. Among the validation set, the AUC for significant fibrosis, advanced fibrosis, and cirrhosis were 0.82, 0.90, and 0.89, respectively. A score > or = 0.5 provided a specificity and sensitivity of 89% and 63% for significant fibrosis, whereas scores <0.5 had 74% specificity and 88% sensitivity for advanced fibrosis. CONCLUSIONS: A model of 4 serum markers plus age and sex provides clinically useful information regarding different fibrosis stages among hepatitis C patients.