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Liver fibrosis staging through a stepwise analysis of non‐invasive markers (FibroSteps) in patients with chronic hepatitis C infection
Author(s) -
ElKamary Samer S.,
Mohamed Mona M.,
ElRaziky Maissa,
Shardell Michelle D.,
Shaker Olfat G.,
ElAkel Wafaa A.,
Esmat Gamal
Publication year - 2013
Publication title -
liver international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.873
H-Index - 110
eISSN - 1478-3231
pISSN - 1478-3223
DOI - 10.1111/liv.12139
Subject(s) - medicine , cirrhosis , fibrosis , stepwise regression , receiver operating characteristic , liver biopsy , gastroenterology , stage (stratigraphy) , biomarker , biopsy , pathology , biology , paleontology , biochemistry
Abstract 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, RBC s, 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.