AIM: To assess the accuracy of a model in diagnosing severe fibrosis/cirrhosis in chronic hepatitis C virus (HCV) infection. METHODS: The model, based on the sequential combination of the Bonacini score (BS: ALT/AST ratio, platelet count and INR) and ultrasonography liver surface characteristics, was applied to 176 patients with chronic HCV infection. Assuming a pre-test probability of 35%, the model defined four levels of post-test probability of severe fibrosis/cirrhosis: <10% (low), 10-74% (not diagnostic), 75-90% (high) and >90% (almost absolute). The predicted probabilities were compared with the observed patientso distribution according to the histology (METAVIR). RESULTS: Severe fibrosis/cirrhosis was found in 67 patients (38%). The model discriminated patients in three comparable groups: 34% with a very high (>90%) or low (<10%) probability of severe fibrosis, 33% with a probability ranging from 75% to 90%, and 33% with an uncertain diagnosis (i.e., a probability ranging from 10% to 74%). The observed frequency of severe fibrosis/cirrhosis was within the predefined ranges. CONCLUSION: The model can correctly identify 67% of patients with a high (>75%) or low (<10%) probability of cirrhosis, leaving only 33% of the patients still requiring liver biopsy.
AIM: To assess the accuracy of a model in diagnosing severe fibrosis/cirrhosis in chronic hepatitis C virus (HCV) infection. METHODS: The model, based on the sequential combination of the Bonacini score (BS: ALT/AST ratio, platelet count and INR) and ultrasonography liver surface characteristics, was applied to 176 patients with chronic HCV infection. Assuming a pre-test probability of 35%, the model defined four levels of post-test probability of severe fibrosis/cirrhosis: <10% (low), 10-74% (not diagnostic), 75-90% (high) and >90% (almost absolute). The predicted probabilities were compared with the observed patientso distribution according to the histology (METAVIR). RESULTS: Severe fibrosis/cirrhosis was found in 67 patients (38%). The model discriminated patients in three comparable groups: 34% with a very high (>90%) or low (<10%) probability of severe fibrosis, 33% with a probability ranging from 75% to 90%, and 33% with an uncertain diagnosis (i.e., a probability ranging from 10% to 74%). The observed frequency of severe fibrosis/cirrhosis was within the predefined ranges. CONCLUSION: The model can correctly identify 67% of patients with a high (>75%) or low (<10%) probability of cirrhosis, leaving only 33% of the patients still requiring liver biopsy.
Authors: J G McHutchison; L M Blatt; M de Medina; J R Craig; A Conrad; E R Schiff; M J Tong Journal: J Gastroenterol Hepatol Date: 2000-08 Impact factor: 4.029
Authors: Xavier Forns; Sergi Ampurdanès; Josep M Llovet; John Aponte; Llorenç Quintó; Eva Martínez-Bauer; Miquel Bruguera; Jose Maria Sánchez-Tapias; Juan Rodés Journal: Hepatology Date: 2002-10 Impact factor: 17.425
Authors: F Oberti; E Valsesia; C Pilette; M C Rousselet; P Bedossa; C Aubé; Y Gallois; H Rifflet; M Y Maïga; D Penneau-Fontbonne; P Calès Journal: Gastroenterology Date: 1997-11 Impact factor: 22.682
Authors: C Niederau; S Lange; T Heintges; A Erhardt; M Buschkamp; D Hürter; M Nawrocki; L Kruska; F Hensel; W Petry; D Häussinger Journal: Hepatology Date: 1998-12 Impact factor: 17.425
Authors: Jeanne M Horowitz; Sudhakar K Venkatesh; Richard L Ehman; Kartik Jhaveri; Patrick Kamath; Michael A Ohliger; Anthony E Samir; Alvin C Silva; Bachir Taouli; Michael S Torbenson; Michael L Wells; Benjamin Yeh; Frank H Miller Journal: Abdom Radiol (NY) Date: 2017-08
Authors: Roberta D'Ambrosio; Elisabetta Degasperi; Alessio Aghemo; Mirella Fraquelli; Pietro Lampertico; Maria Grazia Rumi; Floriana Facchetti; Eleonora Grassi; Giovanni Casazza; William Rosenberg; Pierre Bedossa; Massimo Colombo Journal: PLoS One Date: 2016-06-15 Impact factor: 3.240
Authors: Boran Zhou; Juntao Shao; Kyle J Schaefbauer; Ashley M Egan; Eva M Carmona; Andrew H Limper; Xiaoming Zhang Journal: J Ultrasound Med Date: 2020-08-31 Impact factor: 2.153