Author Affiliations: Department of Medical and Pediatric Sciences, Institute of Internal Medicine "A. Francaviglia", Section of Gastro-enterology, University of Catania, "G. Rodolico" Hospital, Via S. Sofia, 78-95123-Catania, Italy (Catanzaro R, Milazzo M, Arona S and Sapienza C); Laboratory analysis "MedLab-Analisi Cliniche", Catania, Italy (Vasta D); Specialist in diabetes and metabolic diseases, Catania, Italy (Arcoria D); and ReGenera Research Group for Aging Intervention, Milano, Italy (Marotta F)

Corresponding Author: Professor Roberto Catanzaro, Department of Medical and Pediatric Sciences, Institute of Internal Medicine "A. Francaviglia", Section of Gastroenterology, University of Catania, "G. Rodolico" Hospital, Pad. 4, I Piano, Stanza 17, Via S. Sofia, 78-95123- Catania, Italy (Tel: 39-95-3782902; Fax: 39-95-3782376; Email: rcatanza@unict.it)

 

© 2013, Hepatobiliary Pancreat Dis Int. All rights reserved.

doi: 10.1016/S1499-3872(13)60079-X

 

 

Contributors: VD and AD proposed the study. MM wrote the first draft, analyzed the data and contributed to the design and interpretation of the study. All authors participated in the data monitoring. MF coordinated the study. CR is the guarantor.

Funding: None.

Ethical approval: the study was approved by the local ethics committee according to the Declaration of Helsinki and Good Clinical Practice guidelines.

Competing interest: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.

 

 

BACKGROUND: The prognosis and clinical management of patients with chronic liver diseases are closely related to the severity of liver fibrosis. Liver biopsy is considered the gold standard for the staging of liver fibrosis. However, it is an invasive test sometimes related to complications. This study aimed to assess the diagnostic value of enhanced liver fibrosis (ELF) test to predict liver fibrosis in patients with chronic hepatitis C.

 

METHODS: This study included 162 patients with liver disease and 67 healthy controls. Hyaluronic acid, tissue inhibitor of matrix metalloproteinase type 1, and amino-terminal propeptide type III procollagen were measured by enzyme-linked immunosorbent assay with the ELF test ADVIA Centaur® (Siemens Healthcare Diagnostics Inc.). Fibrosis stage was determined using the Metavir scoring system.

 

RESULTS: In our study, for the diagnosis of significant fibrosis (Metavir F≥2) a cut-off value >7.72 provides a sensitivity of 93.0% and a specificity of 83.0%. The areas under the receiver operator characteristic curve, sensitivity, specificity, and positive and negative predictive values were 0.94, 93.3%, 81.0%, 93.3%, and 81.0%, respectively (P<0.001). For the diagnosis of cirrhosis (Metavir F=4) a cut-off value >9.3 provides a sensitivity of 93.0% and a specificity of 86.0%. The areas under the receiver operator characteristic curve, sensitivity, specificity, and positive and negative predictive values were 0.94, 79.1%, 90.8%, 75.6%, and 92.3%, respectively (P<0.001).

 

CONCLUSIONS: The ELF test is a promising non-invasive method for assessing liver fibrosis in patients with chronic hepatitis C. It is effective in the diagnosis of both fibrosis and cirrhosis.

 

(Hepatobiliary Pancreat Dis Int 2013;12:500-507)

 

KEY WORDS: enhanced liver fibrosis test; non-invasive diagnosis; liver fibrosis; blood marker

Hepatitis C virus (HCV) causes 350 000 deaths worldwide each year. Viral hepatitis is the leading cause of liver cirrhosis and liver cancer, which in turn ranks as the third cause of cancer in the world. In the WHO European region, approximately nine million people are chronically infected with HCV.^[1] Liver fibrosis is characterized by the course of chronic liver disease. It can progress to cirrhosis, resulting in complications such as portal hypertension, liver failure and hepatocellular carcinoma. The degree of liver fibrosis is decisive for the assessment and appropriate management of the disease and strongly is indicative of its prognosis. The severity of fibrosis is important to determine the need of treatment and follow-up. It can also predict the response to a treatment. The true gold standard for assessing the degree of liver fibrosis would be a histological analysis of the liver as a whole. Since this is not possible in living patients, liver biopsy has been adopted as the reference standard. However, this diagnostic method has some pitfalls that have led to several questions about its actual value.^{[2, 3]} The procedure involves certain risks such as pain, bleeding, perforation of other organs, and needs a histological interpretation from a special operator. Thus it affects the cost of medical care and causes strong anxiety of the patient.^[4-6]

 

Recent studies focused on alternative diagnostic methods for the assessment of the degree of fibrosis involving either elastic waves or serum markers.^{[7, 8]} The algorithms for the interpretation of these serum biomarkers have developed in the last decade. Among them, enhanced liver fibrosis (ELF) test, which combines hyaluronic acid (HA), the tissue inhibitor of matrix metalloproteinase type 1 (TIMP-1), and the amino-terminal propeptide of type III procollagen (PIIINP), has been accurate in detecting fibrosis in a large cohort of patients with chronic liver disease.^[9] Further studies^[10,11] have evaluated the diagnostic algorithm in patients with various liver diseases. The ELF test has been capable in predicting the progression of the disease in different clinical settings.^{[12, 13]} It is recognized that the heterogeneous etiology of the cohorts in these studies has been a major limitation. The present study aimed to evaluate prospectively the ELF test in the assessment of its effectiveness in the diagnosis of liver fibrosis in patients with chronic hepatitis C with reference to biopsy. It also aimed to examine the development, accuracy, clinical utility and limitations of biomarkers as diagnostic tools for the assessment of liver fibrosis.

 

The ELF test is useful to assess the stage and rate of progression of liver fibrosis. It comprises three serum biomarkers: HA, TIMP-1 and PIIINP.^{[13, 14]} Biomarkers are direct indicators or the metabolism and degradation of the extracellular matrix, which indicate liver fibrosis. A higher concentration of individual markers leads to a higher ELF score and indicates a greater likelihood of more severe fibrosis. The ELF test has received the Conformité Européenne mark in May 2007.^[15] The test for the staging of liver fibrosis has been validated in patients with chronic hepatitis C, alcoholic liver disease and non-alcoholic fatty liver disease.^[16] This method can predict the presence of fibrosis with a sensitivity of 90% and the absence of fibrosis with a negative value of 92%. The test does not show any precautions and contraindications. It uses direct markers of fibrogenesis (HA and TIMP-1) and therefore unreliable results will be seen in patients with chronic diseases characterized by fibrogenesis in other organs rather than the liver.^[17] Furthermore in this study, the ELF test was compared with the aspartate aminotransferase (AST)-to-platelet ratio index (APRI score), another diagnostic serologic test. APRI score=[(AST/ULN)/platelet count]×100.

 

 

The present prospective study included 162 patients with chronic hepatitis C. They were consecutively admitted to our Complex Unit for a liver biopsy in 27 months from January 2011 to March 2013. Only biopsies longer than 15 mm with at least 6 portal tracts were accepted. Exclusion criteria included the previous history of antiviral therapy, the presence of ascites, chronic kidney failure or chronic coinfection HBV/HCV or HIV/HCV, chronic liver disease of other etiology (HBV, non-alcoholic steatohepatitis, hemochromatosis, Wilson's disease, autoimmune hepatitis and α-1 anti-trypsin deficiency), liver failure, patients with alcohol abuse (taking more than 30 g/d of ethanol), heart failure or pregnancy, and patients with BMI >30 kg/m². The diagnosis of chronic hepatitis C was determined according to the positivity of anti-HCV and HCV-RNA for at least 6 months. The levels of HCV-RNA were determined by RNA extracted from serum, with reverse transcription and amplification of cDNA in real time PCR with TaqMan probes, with a sensitivity of 10 IU/mL.

 

Sixty-seven healthy volunteers with no indication for liver biopsy were recruited and served as controls. These subjects were regarded as healthy on the basis of normal liver function tests, negative serology for HBV, HCV and autoimmune hepatitis, normal abdominal ultrasonography, and normal renal function test. These healthy subjects had never suffered from hepatitis and had neither history of alcohol abuse nor use of hepatotoxic drugs. Written informed consents were obtained from all patients and healthy volunteers, and the study was approved by the local ethics committee according to the Declaration of Helsinki and Good Clinical Practice guidelines.

 

The ELF test was carried out in two weeks after liver biopsy. The patients were subjected to laboratory analysis of 0.3 mL of blood taken at MedLab of Catania. Alcohol affects many of the variables used in the ELF test. Abstinence from alcohol prior to sampling was respected.^[18-21] Serum sample was processed through the ELF test ADVIA Centaur® (Siemens Healthcare Diagnostics Inc.). As an in vitro diagnostic test for multivariate indices in the assessment of liver fibrosis, it generates a single score (ELF score) combined with doses of HA, PIIINP and TIMP-1. This newer algorithm has been shown to maintain its diagnostic performance compared to the Original European Liver Fibrosis (OELF) panel including the variable "age".^[22] This score measures the qualitative and quantitative variations of the extracellular matrix of the liver, allowing a dynamic assessment of the activity of fibrogenesis and fibrinolysis. The ELF score is a numerical value with no units of measurement. In calculating the ELF score, ADVIA Centaur analyzers use the ADVIA Centaur doses of HA, PIIINP and TIMP-1 in the following formulae: ELF score per ADVIA Centaur XP=2.278+0.851 ln[CHA]+0.751 ln[CPIIINP]+0.394 ln[CTIMP-1]; ELF score per ADVIA Centaur CP=2.494+0.846 ln[CHA]+0.735 ln[CPIIINP]+ 0.391 ln[CTIMP-1]. The test was based on the first formula. The ELF test is for exclusive use of the ADVIA Centaur®. The interpretation of the severity of liver fibrosis with the ELF score is shown as none/mild, ELF score <7.7; moderate, ELF score 7.7-9.8; and severe, ELF score ≥9.8.

 

Percutaneous liver biopsies were performed under ultrasound guidance by a specialist, using an 18-G disposable needle. The biopsy specimens were fixed with formalin and stained with hematoxylin and eosin. All of the liver biopsies were evaluated by expert pathologists, who were blinded to the patients' clinical histories. The stage of fibrosis was evaluated according to the histological staging of Metavir. Fibrosis was classified by the Metavir scoring system into five stages: 0 (no fibrosis), 1 (portal fibrosis without septa), 2 (portal fibrosis with rare septa), 3 (many septa without cirrhosis), and 4 (cirrhosis).^[23]

 

Quantitative variables were expressed as median (range) or mean±standard deviation and qualitative variables in percentage. The diagnostic values of the ELF test in predicting significant fibrosis and cirrhosis were assessed by cal­­­culating the areas under the receiver operator characteristic (AUROC) curve. AUROC was also expressed with standardization according to fibrosis stages, advanced and non-advanced, to prevent a spectrum bias.^[24] Adjusted AUROC (AdjAUROC) was independent from fibrosis stages and was calculated by observed AUROC (ObAUROC) using the formula AdjAUROC=ObAUROC+[0.1056×(2.5-ObDANA)], acco­­rding to the difference in advanced [F2-F3-F4] and non-advanced [F0-F1] fibrosis (DANA method). Best cut-off values were determined by optimization of the Younden index. Sensitivity (Se), specificity (Sp) and positive and negative predictive values (PPV and NPV) were calculated. Positive likelihood ratios were calculated based on the values of sensitivity and specificity.

 

Statistical analysis was performed with SPSS 20.0 (SPSS Inc., Chicago, IL, USA), except for AUROC comparisons performed with MedCalc 12.4.0. P values lower than 0.05 and 0.01 were considered significant and highly significant, respectively.

 

 

One hundred and sixty-two patients were enrolled in this study, with a mean age of 55.19±9.53 years and a female/male ratio of 1.8. Sixty-seven healthy subjects served as controls, with a mean age of 51.43±10.24 years and a female/male ratio of 1.5. The results were grouped by two different cut-off values: significant (Metavir F≥2) or non-significant fibrosis (Metavir F<2) and presence (Metavir F=4) or absence of cirrhosis (Metavir F<4).^[25] Following such classification, we divided the patients into two groups (Tables 1 and 2).

 

The increased mean values of the ELF test were in parallel to the stages of fibrosis (7.63±0.42 in F1, 8.70±0.88 in F2, 9.07±0.68 in F3 and 10.10±0.54 in F4; all P<0.05 between adjacent fibrosis stages) (Fig. 1). Moreover, there was no overlap between F=1 and F≥2 or between F<4 and F=4 (Fig. 2).

 

The AUROC for the diagnosis of significant fibrosis (Metavir F≥2) was 0.94 (95% CI: 0.89-0.97), with an optimal ELF test cut-off value for the diagnosis of significant fibrosis equal to 7.72 that may provide a sensitivity of 93.0% and a specificity of 83.0% (Fig. 3). The sensitivity and specificity of the ELF test in the diagnosis of significant fibrosis were 93.3% and 81.0%, respectively (Table 3).

 

The AUROC for the diagnosis of cirrhosis (Metavir F=4) was 0.94 (95% CI: 0.88-0.96), with an optimal ELF test cut-off value for the diagnosis of cirrhosis equal to 9.30 that may provide a sensitivity of 93.0% and a specificity of 86.0% (Fig. 4). The sensitivity and specificity of the ELF test in the diagnosis of cirrhosis are 79.1% and 90.8%, respectively (Table 3).

 

Furthermore, AUROC was calculated to distinguish the healthy controls from patients with liver fibrosis. It was 0.96 (95% CI: 0.92-0.98), with an optimal ELF test cut-off value for F≥1 equal to 7.35 that provides a sensitivity of 93.8% and a specificity of 85.1% (Fig. 5).

 

The same statistical analysis was performed for the APRI score (Figs. 3-5) and the results are shown in Table 4. For this dignostic serologic test, in the diagnosis of significant fibrosis in patients with chronic hepatitis C, a cut-off value ≥0.63 may provide a sensitivity of 87.5% and a specificity of 76.2%, whereas in the diagnosis of cirrhosis a cut-off value ≥1.19 is related to a sensitivity of 74.4% and a specificity of 87.4%, respectively. The AdjAUROCs for the ELF test and APRI score obtained by the DANA method are shown in Tables 5 and 6.

 

 

To assess the diagnostic accuracy of the ELF test, we considered its correlation with liver fibrosis in subjects who underwent liver biopsy. The multivariable logistic regression model develops a predictive algorithm whose accuracy was examined by the analysis of the ROC curve. Binary analysis was performed by dividing patients into two groups based on their fibrosis stage. The degree of fibrosis was dichotomized into significant fibrosis (no fibrosis/portal fibrosis without septa versus portal fibrosis with rare septa/many septa without cirrhosis/cirrhosis) and liver cirrhosis (present or absent). The validity of a biomarkers model should be verified through the examination of accuracy in different groups, including those with concomitant diseases, with different age groups and with liver disease of different etiology. Lichtinghagen et al^[26] have recently demonstrated the importance of variables such as age and gender. In our study we found a significant difference (P<0.01) between men (9.16±1.19) and women (8.64±1.06). Moreover, liver disease progression in response to treatment and prognosis in terms of morbidity and mortality are both important parameters. The accuracy of diagnostic biomarkers as mentioned above has been reported in terms of AUROC with sensitivity, specificity and predictive values calculated at specific cut-off points. Typically, a range of values at one end of the test result spectrum will produce a high sensitivity and a low specificity, while a range of values at the opposite end of the spectrum will have a low sensitivity and a high specificity. The intermediate results of the tests often determine moderate values of sensitivity and specificity, which are not clinically significant and, therefore, comprise an "indeterminate range". In our study the ELF test showed a number of results ranging between 7.10 and 11.03. For the diagnosis of significant fibrosis in patients with chronic liver disease, a cut-off value ≥7.72 may provide a sensitivity of 93.0% and a specificity of 83.0%. A cut-off value ≥9.30 for the diagnosis of cirrhosis may provide a sensitivity of 93.0% and a specificity of 86.0%. For example, a comparison can be made with the APRI score, which typically provides a number of results ranging from 0.1 to 8.0. In the diagnosis of significant fibrosis in patients with chronic hepatitis C, according to the literature, a cut-off value ≤0.5 provides a sensitivity of 81.0% and a specificity of 50.0%, whereas a cut-off value ≥1.5 is related to a sensitivity of 35.0% and a specificity 91.0% respectively.^{[27, 28]} Thus, most of the biomarkers will produce inconclusive results for patients who fall in the indeterminate range for a specific end-point of fibrosis. Parkes et al^[28] examined the performance of 10 algorithms serum biomarkers and found that 65% of subjects had indeterminate results for the prediction of significant fibrosis. However, the values in the indeterminate range for an end-point of fibrosis (for example, clinically significant fibrosis) can still be useful for the diagnosis of fibrosis with other end-points (for example, cirrhosis). The values of APRI between 0.5 and 1.5, for example, are not useful in determining a significant fibrosis, but those higher than 1.0 are related to a sensitivity of 89.0% and a specificity of 75.0% in the diagnosis of HCV-related cirrhosis.^[27]

 

The APRI score, in our cohort, showed similar results. The outcomes were slightly better than those reported in the literature.^[27] In the diagnosis of significant fibrosis in patients with chronic hepatitis C, a cut-off value ≥0.63 may provide a sensitivity of 87.5% and a specificity of 76.2%, whereas in the diagnosis of cirrhosis a cut-off value ≥1.19 is related to a sensitivity of 74.4% and a specificity of 87.4%, respectively.

 

In our study, the ELF test in the diagnosis of significant fibrosis (Metavir F≥2) showed a sensitivity of 93.3% and a specificity of 81.0% (P<0.001). In the diagnosis of liver cirrhosis (Metavir F=4), instead, the test showed a sensitivity of 79.1%, but a specificity of 90.8% (P<0.001). PPV and NPV represent a more useful interpretation of the test results performed on a small number of patients. Predictive values, sensitivity, and specificity depend on the prevalence of the underlying disease. Thus, a test can be highly specific for the diagnosis of cirrhosis, but it is related to a low PPV if the underlying prevalence is very low (Table 7). For example, an APRI cut-point of 2.0 has a specificity of 91.0% in the diagnosis of cirrhosis; however, if the prevalence of cirrhosis is only 15.0%, the PPV is 50.0%. Therefore, it is important to understand that the characteristics of the tests with biomarkers vary according to the setting. Selection bias may exist in studies which include patients who have undergone liver biopsy. In our study, the prevalence of significant fibrosis and cirrhosis was 74.1% and 26.5%, respectively, which are higher than those (2.8% and 0.3%) in the general community.^[27] Therefore, for a given biomarker, the PPV will be significantly lower but the NPV significantly higher in the general community than in a clinical trial (Table 7). In our study, the PPV and NPV in the diagnosis of significant fibrosis were 93.3% and 81.0%, respectively. In the diagnosis of liver cirrhosis, the PPV and NPV were 75.6% and 92.3%, respectively.

 

Moreover, the AUROC varies because of the prevalence of fibrosis at different stages. This represents a spectrum bias that has important implications of non-invasive methods, when analyzing outcomes of a diagnostic method used in different populations. If extreme stages of fibrosis (F0 and F4) are over-represented in a given population, the sensitivity and specificity of a diagnostic test will be higher than in another population of patients with middle stages of fibrosis (F1 and F2). Several ways of preventing "spectrum bias" have been proposed including the AdjAUROC using the DANA method.

 

Clinically, an accurate determination of liver fibrosis is not as important as in other pathological scoring systems. For each diagnostic method for evaluating patients with significant fibrosis, both sensitivity and specificity of above 85% can be considered sufficient since no relevant clinical consequences are found in case of false positives or false negatives.^[29] The present study showed that the ELF test is a promising non-invasive method for assessing liver fibrosis in patients with chronic hepatitis C. The test has proven to be useful in determining the intermediate stages of fibrosis or the presence or absence of cirrhosis.

 

The ELF test was proved more reliable than the APRI score in the diagnosis of significant fibrosis and cirrhosis. However, it was not effective in discriminating healthy volunteers from patients with liver fibrosis (Figs. 3-5).

 

The assessment of the liver fibrosis through the ELF test combined with other non-invasive diagnostic methods may provide high PPV or NPV, highlighting the stage of fibrosis with a high level of confidence. Thus a number of patients can avoid liver biopsy.

 

At present, liver biopsy is still considered as the essential method for the staging of fibrosis, and the ELF test might be one of the best non-invasive diagnostic methods in the selection of patients eligible for biopsy and in the follow-up of patients who already received antiviral therapy and/or dietary supplement.

 

 

1     Hatzakis A, Wait S, Bruix J, Buti M, Carballo M, Cavaleri M, et al. The state of hepatitis B and C in Europe: report from the hepatitis B and C summit conference. J Viral Hepat 2011;18:1-16. PMID: 21824223

2     Reiss G, Keeffe EB. Role of liver biopsy in the management of chronic liver disease: selective rather than routine. Rev Gastroenterol Disord 2005;5:195-205. PMID: 16369215

3     Crockett SD, Kaltenbach T, Keeffe EB. Do we still need a liver biopsy? Are the serum fibrosis tests ready for prime time? Clin Liver Dis 2006;10:513-534. PMID: 17162226

4     Dienstag JL. The role of liver biopsy in chronic hepatitis C. Hepatology 2002;36:S152-160. PMID: 12407589

5     Cadranel JF, Rufat P, Degos F. Practices of liver biopsy in France: results of a prospective nationwide survey. For the Group of Epidemiology of the French Association for the Study of the Liver (AFEF). Hepatology 2000;32:477-481. PMID: 10960438

6     Regev A, Berho M, Jeffers LJ, Milikowski C, Molina EG, Pyrsopoulos NT, et al. Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV infection. Am J Gastroenterol 2002;97:2614-2618. PMID: 12385448

7     Sandrin L, Fourquet B, Hasquenoph JM, Yon S, Fournier C, Mal F, et al. Transient elastography: a new noninvasive method for assessment of hepatic fibrosis. Ultrasound Med Biol 2003;29:1705-1713. PMID: 14698338

8     Castéra L, Vergniol J, Foucher J, Le Bail B, Chanteloup E, Haaser M, et al. Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology 2005;128:343-350. PMID: 15685546

9     Friedrich-Rust M, Rosenberg W, Parkes J, Herrmann E, Zeuzem S, Sarrazin C. Comparison of ELF, FibroTest and FibroScan for the non-invasive assessment of liver fibrosis. BMC Gastroenterol 2010;10:103. PMID: 20828377

10   Stevenson M, Lloyd-Jones M, Morgan MY, Wong R. Non-invasive diagnostic assessment tools for the detection of liver fibrosis in patients with suspected alcohol-related liver disease: a systematic review and economic evaluation. Health Technol Assess 2012;16:1-174. PMID: 22333291

11   Guha IN, Parkes J, Roderick P, Chattopadhyay D, Cross R, Harris S, et al. Noninvasive markers of fibrosis in nonalcoholic fatty liver disease: Validating the European Liver Fibrosis Panel and exploring simple markers. Hepatology 2008;47:455- 460. PMID: 18038452

12   Mayo MJ, Parkes J, Adams-Huet B, Combes B, Mills AS, Markin RS, et al. Prediction of clinical outcomes in primary biliary cirrhosis by serum enhanced liver fibrosis assay. Hepatology 2008;48:1549-1557. PMID: 18846542

13   Parkes J, Roderick P, Harris S, Day C, Mutimer D, Collier J, et al. Enhanced liver fibrosis test can predict clinical outcomes in patients with chronic liver disease. Gut 2010;59:1245-1251. PMID: 20675693

14   iQur Ltd. Enhanced Liver Fibrosis Test (ELFTM Test). 2006. Available from URL: http://www.iqur.com/Sampleprep.html.

15   National Institute for Health Research National Horizon Scanning Centre. Enhanced Liver Fibrosis Test (ELF) for evaluating liver fibrosis. Birmingham: National Horizon Scanning Centre 2008.

16   Rosenberg WM, Voelker M, Thiel R, Becka M, Burt A, Schuppan D, et al. Serum markers detect the presence of liver fibrosis: a cohort study. Gastroenterology 2004;127:1704-1713. PMID: 15578508

17   Castera L, Pinzani M. Biopsy and non-invasive methods for the diagnosis of liver fibrosis: does it take two to tango? Gut 2010;59:861-866. PMID: 20581229

18   Campbell S, Timms PM, Maxwell PR, Doherty EM, Rahman MZ, Lean ME, et al. Effect of alcohol withdrawal on liver transaminase levels and markers of liver fibrosis. J Gastroenterol Hepatol 2001;16:1254-1259. PMID: 11903744

19   Tsutsumi M, Urashima S, Takase S, Ueshima Y, Tsuchishima M, Shimanaka K, et al. Characteristics of serum hyaluronate concentrations in patients with alcoholic liver disease. Alcohol Clin Exp Res 1997;21:1716-1721. PMID: 9438536

20   Lieber CS, Weiss DG, Paronetto F, Veterans Affairs Cooperative Study 391 Group. Value of fibrosis markers for staging liver fibrosis in patients with precirrhotic alcoholic liver disease. Alcohol Clin Exp Res 2008;32:1031-1039. PMID: 18422837

21   Bedossa P, Poynard T. An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group. Hepatology 1996;24:289-293. PMID: 8690394

22   Parkes J, Guha IN, Roderick P, Harris S, Cross R, Manos MM, et al. Enhanced Liver Fibrosis (ELF) test accurately identifies liver fibrosis in patients with chronic hepatitis C. J Viral Hepat 2011;18:23-31. PMID: 20196799

23   Wai CT, Greenson JK, Fontana RJ, Kalbfleisch JD, Marrero JA, Conjeevaram HS, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 2003;38:518-526. PMID: 12883497

24   Poynard T, Halfon P, Castera L, Munteanu M, Imbert-Bismut F, Ratziu V, et al. Standardization of ROC curve areas for diagnostic evaluation of liver fibrosis markers based on prevalences of fibrosis stages. Clin Chem 2007;53:1615-1622. PMID: 17634213

25   Ishak K, Baptista A, Bianchi L, Callea F, De Groote J, Gudat F, et al. Histological grading and staging of chronic hepatitis. J Hepatol 1995;22:696-699. PMID: 7560864

26   Lichtinghagen R, Pietsch D, Bantel H, Manns MP, Brand K, Bahr MJ. The Enhanced Liver Fibrosis (ELF) score: Normal values, influence factors and proposed cut-off values. J Hepatol 2013;59:236-242. PMID: 23523583

27   Shaheen AA, Myers RP. Diagnostic accuracy of the aspartate aminotransferase-to-platelet ratio index for the prediction of hepatitis C-related fibrosis: a systematic review. Hepatology 2007;46:912-921. PMID: 17705266

28   Parkes J, Guha IN, Roderick P, Rosenberg W. Performance of serum marker panels for liver fibrosis in chronic hepatitis C. J Hepatol 2006;44:462-474. PMID: 16427156

29   Martínez SM, Crespo G, Navasa M, Forns X. Noninvasive assessment of liver fibrosis. Hepatology 2011;53:325-335. PMID: 21254180