Simple clinical variables predict liver histology in hepatitis C: prospective validation of a clinical prediction model

Abstract

Objective: A recent single-center multivariate analysis of hepatitis C (HCV) patients showed that having any two criteria: 1) ferritin > or =200 microg/l and 2) spider nevi and/or albumin < or = 35 g/l predicted grade 2 or greater histological inflammation; the presence of any two of the following criteria: spider nevi, platelets < or =150 x 109/l, palpable splenomegaly and/or albumin < or =35 g/l predicted stage 2 or greater histological fibrosis. Absence of predictors also predicted a lack of inflammation and fibrosis. Our aim was prospectively to validate this clinical prediction model using an independent multicenter sample.

Material and methods: Eighty-one patients with previously untreated active chronic HCV underwent physical examination, laboratory investigation, and liver biopsy. Biopsies were read, in blinded fashion, by a single pathologist, using a modified Hytiroglou (1995) scale. The clinical scoring system was correlated with histology; likelihood ratios (LRs), Fisher's exact p-values, and receiver operating characteristics (ROCs) were calculated.

Results: Data recording was complete in 77 and 38 patients regarding fibrotic stage and inflammatory grade, respectively. For fibrosis, 3/3 patients with any three criteria (LR 17, positive predictive value (PPV) 100%), 4/5 patients with any two criteria (LR 5.1), and 15/47 with no criteria (LR 0.6, negative predictive value (NPV) 68%) had stage 2 or greater fibrosis on biopsy (p=0.01). For inflammation, 5/5 patients with both criteria (LR 15, PPV 100%), and 8/19 patients with no criteria (LR 0.5, NPV 58%) had moderate-severe inflammation on liver biopsy (p=0.036). When missing variables were assumed to be normal, recalculated LRs were almost identical. An alanine aminotransferase (ALAT) level <60 U/l may increase the NPVs.

Conclusions: This independent multicenter data set has validated our published model which uses simple clinical variables accurately and significantly to predict hepatic fibrosis and inflammation in HCV patients.