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©2014 Baishideng Publishing Group Inc.
World J Gastroenterol. Aug 28, 2014; 20(32): 11033-11053
Published online Aug 28, 2014. doi: 10.3748/wjg.v20.i32.11033
Published online Aug 28, 2014. doi: 10.3748/wjg.v20.i32.11033
Table 1 Factors contributing to fibrosis progression in chronic hepatitis C
Non-modifiable | Modifiable |
Duration of HCV infection | High alcohol consumption (≥ 20-50 g/d) |
Older age at infection | Insulin resistance |
Male sex | Obesity |
Presence of baseline fibrosis | Metabolic syndrome |
HIV or HBV co-infection | Daily cannabis use |
Infection with HCV genotype 3 | |
Gene polymorphisms involved in iron overload/inflammatory pathways | |
Latin ethnicity |
Table 2 Comparison of the main characteristics of liver biopsy, serum biomarkers and transient elastography
Liver biopsy | Serum biomarkers | Transient elastography | |
Advantages | Direct assessment of liver fibrosis | Immediate result | Immediate result |
Stage by stage fibrosis classification | Fast (one time blood sample) | Duration of examination 5 min | |
Evaluation of coexisting disorders (inflammation, steatosis, iron overload) | Patient friendly | Operator and patient friendly | |
Limitations | Complications (pain, bleeding) | Cost (unitary cost per patient for patented tests) | Cost (one time per machine) |
Sampling error, intra-observer and inter-observer variability | High rates of unclassified patients (APRI, Fib-4, Forns’ index, Lok index) | Failure in 5% of cases (25% in obese patients) | |
Hospitalization (day hospital) often required | Unreliable results in 15% of cases (obesity, ascites, limited operator experience) | ||
Cost | Lower performance for diagnosis of significant fibrosis | Lower performance for diagnosis of significant fibrosis | |
Delayed result (2-4 wk) | Unable to discriminate between intermediate stages of fibrosis | Unable to discriminate between intermediate stages of fibrosis | |
Contraindications | Absolute: uncooperative patient, severe coagulopathy, extrahepatic biliary obstruction | None | Pacemaker, pregnancy |
Relative: ascites, morbid obesity, possible vascular lesions, amyloidosis | |||
Risk factors for error | Biopsy sample < 2 cm in length, containing < 10 complete portal tracts; inexperienced pathologist | Autoimmune thrombocytopenia (APRI); Gilbert’s sydrome, extrahepatic cholestasis, hemolytic anemia (Fibrotest) | Transaminases flares; acute viral hepatitis; non-fasting patient; vascular hepatic congestion; extrahepatic cholestasis; IQR ≥ 30% |
Table 3 Role of liver biopsy and non-invasive tools across the international guidelines
Ref. | Threshold for definitive indication to antiviral therapy | Recommended methods for liver fibrosis staging | Can non-invasive methods replace liver biopsy? |
APASL[109], 2007 | F1 | Liver biopsy | No |
AASLD[190], 2009 | F2 | Liver biopsy, serum biomarkers, transient elastography | No |
EASL[81], 2014 | F2 | Liver biopsy, serum biomarkers, transient elastography | Yes |
CASL[111], 2012 | None | Liver biopsy, serum biomarkers, transient elastography | Yes |
Table 4 Main validation features among the non-invasive methods for liver fibrosis diagnosis
Ref. | Parameters | Independent validation studies | Etiology-validation studies | Characterization of risk factors for error | Validation in special HCV populations |
AAR[138] | AST, ALT | + | + | + | + |
APRI[142] | AST, platelets | + | + | + | + |
ELF[131] | Age, TIMP-1, hyaluronan, procollagen type III | +/- | + | + | - |
Fib-4[145] | Age, ALT, AST, platelets | + | + | + | + |
Fibrometer®[122] | Platelets, prothrombin index, AST, α2-macroglobulin, hyaluronan, urea, age | +/- | + | + | + |
Fibroscan®[167] | Liver stiffness measurement | + | + | + | + |
Fibrospect®[132] | Hyaluronan, TIMP-1, α2-macroglobulin | +/- | - | - | - |
Fibrotest-Fibrosure®[132] | γGT, total bilirubin, haptoglobin, α2-macroglobulin, apolipo-protein A1, age, gender | + | + | + | + |
Forns’ index[144] | Age, γGT, cholesterol, platelets | + | + | + | + |
Hepascore[129] | Age, gender, bilirubin, γGT, hyaluronan, α2-macroglobulin | +/- | + | - | + |
Hyaluronan | Hyaluronic acid | + | + | + | + |
Lok index[191] | AST, ALT, platelets | - | - | + | - |
Table 5 Diagnostic performance of serum biomarkers in chronic hepatitis C
Index | ≥F2/F4 | ||||||
AUC | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) | LR+ | LR- | |
Hyaluronan[113-115,119,128] | 0.73-0.86/ 0.89-0.92 | 64.5-75/ 79.2-100 | 81.0-91.2/ 80.0-89.4 | 44.0-86.3/ 63.0-100 | 78.5-93/ 99.0-100 | 3.94-7.32/ 5.00-7.47 | 0.30-0.38/ 0.00-0.23 |
Fibrometer[122,124] | 0.85-0.89/ 0.91 | 80.5-89/ 94.1 | 84.1-89.9/ 87.6 | 82.0-86.3/ 68 | 77.6-82.5/ 94.7 | 5.56-7.97/ 7.46 | 0.13-0.21/ 0.06 |
FibroSpect[122,126-128] | 0.82-0.87/ NA | 71.8-93.0/ NA | 66.0-73.9/ NA | 60.9-82.6/ NA | 77.7-94/ NA | 2.73-2.75/ NA | 0.10-0.24/ NA |
Hepascore[124,129,130] | 0.79-0.85/ 0.85-0.94 | 53.08-82/ 71.0-76.5 | 65.0-92.0/ 84.0-89.8 | 70-88/ 64.9 | 63.5-78/ 89.6-98 | 2.34-6.62/ 4.78-6.96 | 0.27-0.51/ 0.27-0.32 |
ELF score[122,131] | 0.80/ NA | 90/ NA | 31/ NA | 27.5/ NA | 92/ NA | 1.30/ NA | 0.32/ NA |
AAR[137,192] | NA/ 0.51-0.83 | NA/ 46.7-78.0 | NA/ 95.9-100 | NA/ 73.7-100 | NA/ 80.7-89 | NA/ 19.02 | NA/ 0.22-0.43 |
APRI[122,124,133,137,142,192-194] | 0.69-0.88/ 0.61-0.94 | 41-91/ 57-89 | 47-95/ 75-93 | 61-88/ 38-57 | 64-86/ 93-98 | 1.71-8.20/ 3.56-8.14 | 0.19-0.62/ 0.10-0.46 |
Lok Index[137,191] | NA/ 0.78-0.81 | NA/ 37-92 | NA/ 30-94 | NA/ 32-75 | NA/ 84-91 | NA/ 1.31-6.16 | NA/ 0.26-0.67 |
Forns’ Index[122,124,133,144,192,193] | 0.60-0.86/ NA | 79.8-94/ NA | 61.2-95.0/ NA | 66-94.7/ NA | 63.8-96/ NA | 2.42-15.96/ NA | 0.09-0.21/ NA |
Fib-4[145] | 0.82-0.89/ 0.79-0.91 | 37.6-74.3/ NA | 80.1-98.2/ NA | 82.1/ NA | 94.7/ NA | 3.73-20.77/ NA | 0.32-0.63/ NA |
Fibrotest[122,124,132,133,135] | 0.74-0.87/ 0.71-0.87 | 65-77/ 50-87 | 72-91/ 70-92.9 | 76-80/ 57.9-93 | 66.7-81/ 44-90.5 | 2.75-7.22/ 2.9-7.04 | 0.31-0.38/ 0.17-0.53 |
Table 6 Cut-off values, performance and number of patients per study of Fibroscan®
Ref. | Cut-off for≥F2 (kPa) | Cut-off for F4 (kPa) | AUC for≥F2 | AUC for F4 | Number of patients included |
Sandrin et al[147], 2003 | 7.6 | 14.4 | 0.88 | 0.99 | 106 |
Castéra et al[167], 2005 | 7.1 | 12.5 | 0.83 | 0.95 | 183 |
Ziol et al[195], 2005 | 8.7 | 14.5 | 0.79 | 0.97 | 327 |
Kettaneh et al[196], 2007 | 6.8 | 17.6 | 0.79 | 0.91 | 935 |
Arena et al[197], 2008 | 7.8 | 14.8 | 0.91 | 0.98 | 150 |
Cross et al[198], 2010 | 8.9 | 10.1 | 0.89 | 0.97 | 187 |
Degos et al[199], 2010 | 5.2 | 12.9 | 0.75 | 0.90 | 913 |
Table 7 Combination algorithms of non-invasive methods for liver fibrosis proposed in chronic hepatitis C
Algorithm’s name | Type | Non-invasive methods adopted | AUC for≥F2 | AUC for F4 | Saved liver biopsies for > F2 (%) | Saved liver biopsies for F4 (%) | Number of studies (patients) |
SAFE biopsy[133,165] | Stepwise | APRI, Fibrotest® | 0.89-0.94 | 0.87-0.92 | 43.8-54.0 | 74.8-93.4 | 6 (4118) |
Bordeaux algorithm[167,168] | Synchronous | Fibrotest, Fibroscan® | 0.88-0.91 | 0.93-0.95 | 71.9-77.0 | 78.8-79.0 | 3 (875) |
Leroy algorithm[124] | Synchronous | APRI, Fibrotest® | 0.94 | NA | 19.0-29.2 | NA | 3 (1381) |
Fibropaca algorithm[134] | Synchronous | APRI, Fibrotest, Forns’ index | 0.88 | 0.85 | 51.7 | 76.2-81.3 | 2 (1248) |
Angers algorithms[171] | Synchronous | Fibrotest, Fibrometer® | 0.892 | 0.917 | 79.8 | 89.7 | 1 (390) |
Bourliere’s algorithm[166] | Stepwise | APRI, Hepascore | 91%-96% (accuracy) | 33-45 | 1 (467) | ||
Fibrometer® + Fibroscan[172] | Synchronous | Fibrometer, Fibroscan | 86.7% | 100 | 1 (1785) |
- Citation: Sebastiani G, Gkouvatsos K, Pantopoulos K. Chronic hepatitis C and liver fibrosis. World J Gastroenterol 2014; 20(32): 11033-11053
- URL: https://www.wjgnet.com/1007-9327/full/v20/i32/11033.htm
- DOI: https://dx.doi.org/10.3748/wjg.v20.i32.11033