Liver fibrosis staging through a stepwise analysis of non-invasive markers (FibroSteps) in patients with chronic hepatitis C infection

摘要

Background: Non-invasive fibrosis markers can distinguish between liver fibrosis stages in lieu of liver biopsy or imaging elastography. Aims: To develop a sensitive, non-invasive, freely-available algorithm that differentiates between individual liver fibrosis stages in chronic hepatitis C virus (HCV) patients. Methods: Chronic HCV patients (n = 355) at Cairo University Hospital, Egypt, with liver biopsy to determine fibrosis stage (METAVIR), were tested for preselected fibrosis markers. A novel multistage stepwise fibrosis classification algorithm (FibroSteps) was developed using random forest analysis for biomarker selection, and logistic regression for modelling. FibroSteps predicted fibrosis stage using four steps: Step 1 distinguished no(F0)/mild fibrosis(F1) vs. moderate(F2)/severe fibrosis(F3)/cirrhosis(F4); Step 2a distinguished F0 vs. F1; Step 2b distinguished F2 vs. F3/F4; and Step 3 distinguished F3 vs. F4. FibroSteps was developed using a randomly-selected training set (n = 234) and evaluated using the remaining patients (n = 118) as a validation set. Results: Hyaluronic Acid, TGF-β1, α2-macroglobulin, MMP-2, Apolipoprotein-A1, Urea, MMP-1, alpha-fetoprotein, haptoglobin, RBCs, haemoglobin and TIMP-1 were selected into the models, which had areas under the receiver operating curve (AUC) of 0.973, 0.923 (Step 1); 0.943, 0.872 (Step 2a); 0.916, 0.883 (Step 2b) and 0.944, 0.946 (Step 3), in the training and validation sets respectively. The final classification had accuracies of 94.9% (95% CI: 91.3-97.4%) and 89.8% (95% CI: 82.9-94.6%) for the training and validation sets respectively. Conclusions: FibroSteps, a freely available, non-invasive liver fibrosis classification, is accurate and can assist clinicians in making prognostic and therapeutic decisions. The statistical code for FibroSteps using R software is provided in the supplementary materials.