BACKGROUND & AIMS: Biomarkers are being developed as alternatives to liver biopsy for predicting liver fibrosis in patients with chronic hepatitis C. Hepascore uses noninvasive serum markers and has been validated in Australian and European populations for predicting different degrees of fibrosis. This study validated this test in a U.S. population. METHODS: Patients with chronic hepatitis C virus infection were assigned to training (n = 203) or validation (n = 188) sets. Liver fibrosis was staged according to the METAVIR scoring system. The Hepascore algorithm uses data on age, sex, as well as total bilirubin, gamma-glutamyl transferase, alpha2-macroglobulin, and hyaluronic acid levels. RESULTS: The ability of Hepascore to predict significant fibrosis (F2-4) as determined by the area under the receiver operating curve was similar in training (0.83) and validation sets (0.81) and was comparable to results seen in previous studies. A cutoff score of > or =0.55 was best for predicting significant fibrosis, with a sensitivity and specificity of 82% and 65% and positive and negative predictive values of 70% and 78%. When compared with 2 simple indices, FIB-4 (age, platelets, AST, and ALT) and APRI (AST/platelet ratio index), Hepascore performed better at excluding advanced fibrosis by using a low cutoff score but worse at predicting fibrosis by using a high cutoff score. An algorithm with Hepascore followed by FIB-4 or APRI spared 103 of 391 individuals a liver biopsy and missed advanced fibrosis in only 1 patient. CONCLUSIONS: Hepascore accurately predicted likelihood of developing fibrosis and could alleviate the need for liver biopsy in a subset of patients.
BACKGROUND & AIMS: Biomarkers are being developed as alternatives to liver biopsy for predicting liver fibrosis in patients with chronic hepatitis C. Hepascore uses noninvasive serum markers and has been validated in Australian and European populations for predicting different degrees of fibrosis. This study validated this test in a U.S. population. METHODS:Patients with chronic hepatitis C virus infection were assigned to training (n = 203) or validation (n = 188) sets. Liver fibrosis was staged according to the METAVIR scoring system. The Hepascore algorithm uses data on age, sex, as well as total bilirubin, gamma-glutamyl transferase, alpha2-macroglobulin, and hyaluronic acid levels. RESULTS: The ability of Hepascore to predict significant fibrosis (F2-4) as determined by the area under the receiver operating curve was similar in training (0.83) and validation sets (0.81) and was comparable to results seen in previous studies. A cutoff score of > or =0.55 was best for predicting significant fibrosis, with a sensitivity and specificity of 82% and 65% and positive and negative predictive values of 70% and 78%. When compared with 2 simple indices, FIB-4 (age, platelets, AST, and ALT) and APRI (AST/platelet ratio index), Hepascore performed better at excluding advanced fibrosis by using a low cutoff score but worse at predicting fibrosis by using a high cutoff score. An algorithm with Hepascore followed by FIB-4 or APRI spared 103 of 391 individuals a liver biopsy and missed advanced fibrosis in only 1 patient. CONCLUSIONS: Hepascore accurately predicted likelihood of developing fibrosis and could alleviate the need for liver biopsy in a subset of patients.
Authors: Saurabh Sethi; Douglas A Simonetto; Soha S Abdelmoneim; Michael B Campion; Irakli Kaloiani; Amy C Clayton; Walter K Kremers; Kevin C Halling; Patrick S Kamath; Jayant Talwalkar; Vijay H Shah Journal: J Clin Exp Hepatol Date: 2012-04-12
Authors: Paul M Trembling; Sudeep Tanwar; William M Rosenberg; Geoffrey M Dusheiko Journal: Nat Rev Gastroenterol Hepatol Date: 2013-09-10 Impact factor: 46.802
Authors: Aparna Srinivasa Babu; Michael L Wells; Oleg M Teytelboym; Justin E Mackey; Frank H Miller; Benjamin M Yeh; Richard L Ehman; Sudhakar K Venkatesh Journal: Radiographics Date: 2016-09-30 Impact factor: 5.333