Author Affiliations: Student Research Committee & Department of Biochemistry and Biophysics (Babaei Z), and Cellular and Molecular Biology Research Center, Health Research Institute (Parsian H), Babol University of Medical Sciences, Babol, Iran

Corresponding Author: Hadi Parsian, PhD, Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences, Ganjafrooz Ave, Babol, Iran (Tel/Fax: +98-1132190569; Email: hadiparsian@yahoo.com)

 

© 2016, Hepatobiliary Pancreat Dis Int. All rights reserved.

doi: 10.1016/S1499-3872(16)60062-0

Published online January 13, 2016.

 

 

Acknowledgement: The authors greatly appreciate Dr. Evangeline “vangie” Foronda for editing this manuscript.

Contributors: PH proposed the study. BZ and PH collected the data and wrote the first draft. Both authors contributed to the design and interpretation of the study and to further drafts. PH is the guarantor.

Funding: This study was supported by a grant from the Babol University of Medical Sciences, Babol, Iran (No. 2093).

Ethical approval: Not needed.

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 estimation of liver fibrosis is usually dependent on liver biopsy evaluation. Because of its disadvantages and side effects, researchers try to find non-invasive methods for the assessment of liver injuries. Hyaluronic acid has been proposed as an index for scoring the severity of fibrosis, alone or in algorithm models. The algorithm model in which hyaluronic acid was used as a major constituent was more reliable and accurate in diagnosis than hyaluronic acid alone. This review described various hyaluronic acid algorithm-based models for assessing liver fibrosis.

 

DATA SOURCE: A PubMed database search was performed to identify the articles relevant to hyaluronic acid algorithm-based models for estimating liver fibrosis.

 

RESULT: The use of hyaluronic acid in an algorithm model is an extra and valuable tool for assessing liver fibrosis.

 

CONCLUSIONS: Although hyaluronic acid algorithm-based models have good diagnostic power in liver fibrosis assessment, they cannot render the need for liver biopsy obsolete and it is better to use them in parallel with liver biopsy. They can be used when frequent liver biopsy is not possible in situations such as highlighting the efficacy of treatment protocol for liver fibrosis.

 

(Hepatobiliary Pancreat Dis Int 2016;15:131-140)

KEY WORDS: algorithm; hyaluronic acid; liver disease; liver fibrosis

Hepatotropic viruses, alcoholic or nonalcoholic steatohepatitis, liver immune disease and inherited metabolic diseases are the known causes of liver disease, leading to fibrosis.^[1-3] After liver cell injury, scar appears, resulting in fibrogenesis and ultimately increased level of liver fibrillar extracellular matrix (ECM).^{[1, 4]} Accumulation of ECM components can lead to fibrosis. Studies have reported that fibrosis and cirrhosis are the results of an imbalance between synthesis and degradation of ECM components.^{[1, 4, 5]} When the liver becomes fibrotic, both quantity and quality of ECM will change. Qualitative changes include forming of fibril collagens such as collagen types I and III, noncollagenous glycoproteins, proteoglycans and various glycosaminoglycan including hyaluronic acid (HA).^{[1, 4-7]} Because of the progressive nature of liver fibrosis, diagnosis and estimation of liver fibrosis severity are very important.^[1,4,6] Liver biopsy is the major technique for grading and staging of liver necroinflammatory injuries.^{[8, 9]} Biopsy samples are evaluated by semi-quantitative systems such as Ishak,^[10] Knodell,^[11] METAVIR,^[12] Scheuer,^[13] Brunt^[14] and Kleiner scores.^[15] But liver biopsy limitations make it an unfavorable method for patients.^{[2, 4, 9, 16]} Therefore, it is necessary to find an appropriate non-invasive method.

 

Blood tests and imaging tests including transient elastography, magnetic resonance imaging and computed tomography are the other methods for the assessment of liver fibrosis that are safe, cost-effective and have no serious side effects.^{[1, 4]} It is proposed that some blood markers individually or in algorithm models are useful to estimate stages of liver fibrosis.^[16] Individual biochemical markers of liver fibrosis are classified into direct and indirect markers. Indirect markers reflect only the alteration of liver function. Platelets count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin, gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP) and prothrombin time (PT) are examples of indirect markers. Direct markers such as HA, N-terminal propeptide of type III collagen (PIIINP), metalloproteinases and their tissue inhibitors (TIMP-1) reflect the ECM turnover.^[16-20]

 

Many studies^{[6, 18, 19, 21, 22]} have indicated that serum HA evaluation is a useful tool for estimation of the severity of liver fibrosis. Increase in this serum marker was observed in parallel with the severity of liver damage in various chronic liver diseases. Some researches illustrated that serum HA alone has limited accuracy in predicting the severity of liver fibrosis.^{[2, 7, 16, 23]} Confounding variables such as food ingestion, aging, diurnal variations, exercise and some diseases such as cancers can affect the levels of HA in blood.^{[7, 24-26]} To overcome these problems, scientists combined the results of panels of markers and proposed various algorithms.^{[2, 3, 16, 23]} Rossi et al^[2] reported that the use of multiple markers improved our knowledge about the estimation of liver fibrosis severity. Another report^[27] described that a model consisting of HA, PIIINP and transforming growth factor β (TGF-β) had the best diagnostic accuracy than single markers. This finding indicated that various HA-based algorithms are more reliable and more accurate in diagnosis than HA alone. Since none of the review articles described the diagnostic accuracy of serum HA algorithm-based models for assessment of liver fibrosis, we carried out the present study. A brief explanation on liver fibrosis formation was given and then we introduced each model and compared their diagnostic power according to the etiology of the disease.

 

 

The Hepascore model was first reported by Adams et al in 2005, who analyzed 10 biochemical markers in 211 chronic hepatitis C (CHC) patients. Age, gender, bilirubin, GGT, HA and α2-macroglobulin are the major constituents of this model. Adams and colleagues reported that the area under the curve of receiver operating characteristic (AUROC) of Hepascore for the diagnosis of significant (METAVIR F2, F3 and F4) and extensive fibrosis (F3 and F4) and cirrhosis (F4) was 0.820, 0.900 and 0.890, respectively. Hepascore was validated in hepatitis C patients, when a score of ≥0.5 provided a positive predictive value (PPV) of 88% for significant fibrosis (METAVIR ≥F2) and a score of <0.5 had a negative predictive value (NPV) of 95% for the absence of advanced fibrosis (METAVIR ≥F3).^[28] Kalantari et al^[29] in a study on 80 CHC patients stated that Hepascore is highly valuable in the diagnosis of cirrhosis. They reported that the sensitivity, specificity, PPV and NPV of Hepascore were 100%, 97%, 89% and 100% for discrimination of cirrhotic patients, respectively. Bourliere et al^[30] reported that Hepascore is a powerful predictive algorithm for the discrimination of significant fibrosis (METAVIR ≥F2) and cirrhosis in patients with hepatitis C virus (HCV). In another study^[31] in which researchers tested the diagnostic accuracy of Hepascore, an AUROC of 0.830 and 0.810 was observed for significant fibrosis (METAVIR F2-F4) in training and validation sets, respectively. They reported that a cut-off score of ≥0.55 was best for predicting significant fibrosis, with a sensitivity and specificity of 82% and 65% and PPV and NPV of 70% and 78%, respectively. Costelloe et al^[32] studied 73 patients with mixed liver diseases and showed that Hepascore could not accurately predict any fibrosis (METAVIR F1-F4) with an AUROC of 0.660. Chou et al^[33] in a systematic review indicated that the diagnostic accuracy of Hepascore test is to some extent similar to AST to platelet ratio index (APRI) and FibroTest for the diagnosis of advanced fibrosis; but it is relatively more powerful than others for cirrhosis detection. Ba?ar et al^[34] evaluated the performance of a series of non-invasive tests for detecting liver fibrosis in patients with chronic hepatitis B (CHB). According to their study, Hepascore had a diagnostic accuracy better than APRI, Forn’s index and Shanghai Liver Fibrosis Group’s index.

 

PATEL index is another index for estimating liver fibrosis, proposed in 2004.^[35] HA, TIMP-1 and α2-macroglobulin are the constituents of this panel which is also called FIBROSpect II. Patel et al^[35] reported an AUROC of 0.831 for significant fibrosis (METAVIR F2-F4). In their study on 696 CHC patients, a cut-off score of less than 0.36 excluded significant fibrosis with a NPV of 75.8% and a score of more than 0.36 detected significant fibrosis with a PPV of 74.3%. This panel of markers may reliably differentiate advanced fibrosis from that in the early-stage.^[35]

 

Several studieshave reported that this index is able to discriminate various fibrosis stages. Zaman et al^[36] studied 108 CHC patients and found an AUROC of 0.826 for differentiation of advanced fibrosis (METAVIR F2-F4). The sensitivity, specificity, PPV and NPV were 71.8%, 73.9%, 60.9% and 82.3% respectively, and the overall accuracy was 73.1%. A positive and strong correlation of PATEL score with histological evaluation of liver fibrosis was found by another study. The AUROC for prediction of significant fibrosis on CHC patients was reasonable and this score was accurate in differentiation of mild from moderate-to-severe liver fibrosis (METAVIR F0-F1 vs F2-F4).^[37] Direct comparison by AUROC showed that the FIBROSpect II (0.860) was better than APRI (0.770), FibroTest (0.790) and serum HA alone (0.750) in detecting significant fibrosis.^[33] Other studies on 142 CHC patients have also indicated that PATEL index is able to discriminate significant fibrosis (METAVIR F2-F4) from mild one (METAVIR F0-F1). In addition, a cut-off value of 0.42 demonstrated the values of PPV and NPV were 63% and 94%, respectively.^[38] Guajardo-Salinas and Hilmy^[39] used this score for assessing the severity of liver fibrosis in 129 patients with nonalcoholic fatty liver disease (NAFLD). Using a cut-off value of less than 20, they reported a PPV of 15%, a NPV of 100%, with a sensitivity of 100% and a specificity of 42% for prediction of stage 2 fibrosis or higher (Kleiner-Brunt classification system). In addition, one study reported similar results with respect to bariatric patients who had undergone gastric bypass surgery.^[40]

 

In 2006, Attallah et al^[41] studied the ability of HA, its degrading enzymes (N-acetyl-β-D-glucosaminidase) and degradation products (glucuronic acid and glucosamine) to predict severe fibrosis in 153 CHC patients. They introduced cirrhosis discriminant score (CDS) in their study. The results of their study showed that CDS model can accurately discriminate cirrhotic patients with high diagnostic power characteristics (PPV=95%, NPV=97% and AUROC=0.930).

 

Hepatitis C antiviral long-term treatment against cirrhosis trial (HALT-C) model is another score for assessing liver fibrosis proposed by Fontana et al in 2008.^[42] Constituents of this score are HA, TIMP-1 and platelets count. Fontana et al^[42] studied on 513 CHC patients and reported an AUROC equal to 0.813 for estimating the presence of cirrhosis (Ishak stages 5 and 6). To estimate the absence of cirrhosis, a cut-off value of <0.2 leads to a sensitivity of 88% with a NPV of 86%. Similarly, to assess the presence of cirrhosis, a cut-off value of ≥0.5 gives a specificity of 92% with a PPV of 78%.

 

In 2010, Carrión et al^[43] proposed a new model combining three biomarkers for identification of hepatitis C recurrence after liver transplantation and named it 3-M-ALG. Serum levels of HA, PIIINP and TIMP-1 in 133 HCV patients were analyzed after liver transplantation. The results of the Carrión’s study expressed the AUROC of 0.670, 0.770 and 0.780 for identification of significant fibrosis (Scheuer scoring F ≥2) at 3, 6 and 12 months after transplantation, respectively. A 3-M-ALG cut-off value of <1 showed a high NPV (98%), while a 3-M-ALG cut-off value of ≥2 was associated with a PPV of 83%. According to the authors’ claim, 3-M-ALG model is a useful tool to discriminate patients with mild hepatitis C recurrence from progressive ones.^[43] But there is no study that confirms the diagnostic accuracy of this model in CHC patients; therefore, it is essential to conduct more studies to reach a conclusive statement.

 

In 2006, a new algorithm was introduced via simplifying the European liver fibrosis group score (constituents of age, HA, PIIINP and TIMP-1) and the authors named it enhanced liver fibrosis score (ELF). The constituents of ELF test were similar to the European liver fibrosis score, but they removed the age variable from the formula and observed that performance score did not decrease. It was shown that this score had good diagnostic accuracy for the detection of moderate and severe fibrosis.^{[44, 45]} Parkes et al^[45] who examined 347 CHC patients found that ELF had enough diagnostic accuracy to predict severe fibrosis (Ishak stages 4-6; METAVIR F3 and F4) with an AUROC of 0.850. In presence of an ELF score of 10.48, they found a specificity of 89%, sensitivity of 62%, PPV of 73% and NPV of 83% for severe fibrosis. A recent study^[46] has indicated that the ELF score was significantly higher in men than in women, and that age was a notable influencing factor. Accordingly, influencing factors need to be taken into account. Researchers^[46] reported three cut-off values: 7.7 for a high sensitivity exclusion of fibrosis, 9.8 for a high specificity estimation of moderate fibrosis, and 11.3 for discriminating cirrhosis (a sensitivity of 83% and a specificity of 97%). There are other studies^[47-51] on the accuracy of this model in diagnosis of both fibrosis and cirrhosis in CHC patients. Gümü?ay et al^[52] investigated 58 CHB patients and calculated some non-invasive fibrosis models such as European liver fibrosis panel, ELF panel, PP score (combining of platelet count and PIIINP), APRI and FIB-4 indices. Fibrosis stage was determined using the Ishak scoring system. The results of this study showed that the combination of ELF and APRI has a high diagnostic accuracy for predicting fibrosis of F3 or more with a sensitivity of 90%, specificity of 100%, PPV of 100%, and NPV of 96.4%.^[52] ELF AUROCs in the mentioned systematic review were 0.810 and 0.880 for significant fibrosis and cirrhosis, respectively.^[33]

 

In 2013, El-Kamary et al^[53] proposed a novel multistage stepwise fibrosis classification algorithm for liver fibrosis staging of 355 CHC patients. This model consists of HA, TGF-β1, α2-macroglobulin, matrix metalloproteinase-1 (MMP-1), MMP-2, apolipoprotein-A1, urea, α-fetoprotein, haptoglobin, red blood cells, hemoglobin and TIMP-1. This algorithm can predict fibrosis stage in four classification steps: step 1 for discrimination between F0/F1 (METAVIR) and F2/F3/F4; step 2a for discrimination between F0 and F1; step 2b for discrimination between F2 and F3/F4; and step 3 for discrimination between F3 and cirrhosis i.e. F4. FibroSteps predicted fibrosis stage using four steps: an AUROC of 0.973, 0.923 (step 1); 0.943, 0.872 (step2a); 0.916, 0.883 (step 2b) and 0.944, 0.946 (step 3) in the training and validation sets, respectively. The authors concluded that FibroSteps algorithm is a powerful tool to differentiate the individual liver fibrosis stages in CHC patients, but more reliable studies are still needed to probably replace this score with liver biopsy.^[53]

 

Fibrosis discriminant score (FDS) was proposed by El-mezayen et al (in 2013) for estimation of liver fibrosis stage.^[54] This score is based on serum HA, β-glucuronidase, glucosamine, ferritin, AST and ALT. It was shown that a discriminant cut-off value of 0.55 has a good diagnostic accuracy (sensitivity 100%, specificity 73%, PPV 91% and NPV 97%) for predicting severe fibrosis (METAVIR F3) in 210 CHC patients.^[54] We did not find other reports on the diagnostic accuracy of this score.

 

In 2014, a new version of Angers algorithm (combination of the results of liver stiffness evaluation+FibroMeter results) was presented and the researchers named it E-FibroMeter^2G. Major constituents of this score are transient elastography (Fibroscan) and FibroMeter^2G score. The test was constructed by adding all 8 single markers (platelets, PT, AST, HA, α2-macroglobulin, gender and age) of FibroMeter^2G and Fibroscan for the diagnosis of significant fibrosis (METAVIR ≥F2) or cirrhosis (F4). The results of the study showed that combining individual markers of FibroMeter with Fibroscan was more accurate than Angers algorithm. By this novel algorithm, an AUROC of 0.926 was used to estimate cirrhosis (METAVIR F4), whereas the AUROC of Fibroscan and FibroMeter was as low as 0.905 and 0.861, respectively.^[55] This finding indicated that the combination of blood test with transient elastometry can improve the diagnostic accuracy of non-invasive test for estimation and classification of the severity of liver fibrosis.

 

 

Zeng et al^[22] studied 372 patients with HBeAg-positive CHB and proposed this model in 2005. This index (Zeng score or Shanghai Liver Fibrosis Group’s index) comprises α2-macroglobulin, age, GGT and HA. Zeng et al^[22] stated that this index is highly valuable to discriminate between HBeAg-positive CHB patients with and without significant fibrosis (Scheuer stages F2-F4 from F0-F1).A cut-off value of >8.7 could be used to correctly determine the presence of significant fibrosis. Its high accuracy (91.1% PPV, 51.6% NPV and 95.2% specificity) was obvious in the training group (n=200), and similarly (84.8% PPV, 52.4% NPV and 90.4% specificity) in the validation group (n=172).^[22] In their study on 78 CHB patients, Wu et al^[56] reported an AUROC of 0.950, 0.930 and 0.940 for severe fibrosis (METAVIR ≥F3) by Hepascore, Shanghai Liver Fibrosis Group’s index, and mibrometer models, respectively. Obviously, these models are of diagnostic value in the assessment of liver fibrosis.

 

 

Nonalcoholic steatohepatitis (NASH) index was introduced in 2009 for assessing the severity of NAFLD. Miele et al^[57] studied 46 patients with NAFLD, who were divided into two groups based on the presence or absence of NASH. They found that the patients with NASH were older with a higher mean body mass index, and an increased prevalence of diabetes, metabolic syndrome and mild or moderate steatosis with severe fibrosis. The NASH group had significantly higher mean serum levels of HA and TIMP-1, but the two groups were not significantly different in terms of serum levels of the other parameters. HA, TIMP-1 and age were selected as having the best predictive accuracy for assessing the severity of NAFLD. Miele et al^[57] reported an acceptable diagnostic accuracy for this index: sensitivity, specificity, NPV and PPV of 85.7%, 87.1%, 96.4% and 60.0%, respectively for the identification of patients with NASH. However, small sample size and lack of reliable studies are the major limitations of this index.

 

 

In 2005, Kelleher et al^[58] evaluated the diagnostic accuracy of serum fibrosis biomarkers for detecting liver fibrosis in 95 HIV/HCV coinfected patients. Biopsy samples of the patients were scored according to the Ishak modified histological activity index (MHAI) scoring system. They observed that HA, albumin and AST were useful for discriminating mild and advanced liver fibrosis (MHAI 0-2 vs 3-6). Finally, they developed a predictive panel and named it SHASTA index. A cut-off value of 0.80 of this index showed a specificity of 100% and PPV of 100%, respectively. A cut-off value of <0.30 was associated with a sensitivity of >88% and NPV of >94%, respectively. Cacoub et al^[59] compared seven non-invasive biomarkers of liver fibrosis to differentiate HIV/HCV co-infected patients with mild to moderate fibrosis (METAVIR ≥F2) from those with advanced fibrosis (≥F3). They concluded that FibroMeter, Hepascore and FibroTest with AUROCs of 0.890, 0.840 and 0.780, respectively for the diagnosis of ≥F2 are better than other blood tests, i.e. SHASTA (0.750), APRI (0.740), Forns index (0.730) and FIB-4 (0.770).^{[23, 59]} The results of these studies should be evaluated in large sample size studies.

 

 

In 1997, Oberti et al^[60] in a prospective study determined the diagnostic accuracy of some non-invasive tests including clinical examination, biochemical markers, Doppler ultrasonic and endoscopic signs for the evaluation of liver fibrosis or cirrhosis. They found that HA and prothrombin index were the best predictive factors compared with others. This was one of the pioneering works to assess the stage of liver fibrosis using various parameters.

 

In 2004, Rosenberg et al^[61] used this score in 1021 patients with various chronic liver diseases. This score comprises age, HA, PIIINP and TIMP-1. It can be calculated for two liver fibrosis staging systems, i.e. Scheuer and Ishak. As the authors observed similar results for both mentioned staging systems, they presented only the diagnostic accuracy of Scheuer stages. The results of AUROC of Scheuer score were 0.773, 0.870 and 0.944 in patients with CHC, NAFLD and alcoholic liver disease (ALD), respectively. The results of this study indicated that the performance of this score was excellent for ALD and NAFLD. Therefore, this model can be used in a wide range of chronic liver diseases for detection of patients with little or no fibrosis (NPV 92%).^[61]

 

FibroMeter is another score proposed by Calès et al in 2005, who used different combinations of blood markers for evaluation of the severity and area of liver fibrosis in various types of liver diseases, too. A combination of platelets, PT, AST, α2-macroglobulin, HA, urea and age was used to evaluate the severity of liver fibrosis in 383 patients with viral hepatitis.^[62] In another model of this score, the combination of PT, α2-macroglobulin, HA, and age, was used for 95 ALD patients.^[62] The authors reported that the AUROC of FibroMeter for METAVIR stages F2-F4 was 0.892 and 0.962 in patients with viral and ALD, respectively. It is clear that this index can be used to determine the pathological stage and area of liver fibrosis with high diagnostic accuracy in viral hepatitis and also ALD.^[62]

 

In 2008, researchers by adding gender into the formula improved it for diagnosis of significant fibrosis in CHC patients. This second generation test was called FibroMeter^2G.^[63] The third generation of FibroMeter, i.e. FibroMeter^3G, was obtained by replacing HA with GGT. By this change, no significant alteration was observed in the performance of the test.^[64] Leroy et al^[65] in their meta-analysis study reported that FibroMeter had higher AUROC (0.840) than FibroTest (0.800), APRI (0.790) or Hepascore (0.781) for significant fibrosis (METAVIR≥F2). Diagnostic accuracy of this index has been confirmed in another study.^[66] Researchers found that FibroMeter is the only test which is able to differentiate all HCV patients without fibrosis or with cirrhosis. In addition, its high diagnostic accuracy is confirmed in other diseases such as ALD and NAFLD (AUROC: 0.960 and 0.940, respectively, for significant fibrosis).^[67] Data from comparative studies showed that FibroMeter has an excellent accuracy in diagnosis of cirrhosis (AUROC: 0.910) in CHC patients.^[33]

 

In 2012, a group of researchers introduced a simple scoring system (PHP index) for diagnosis of liver cirrhosis.^[68] They found that platelet count, HA and PIIINP were the independent predictors of cirrhosis in their initial statistical analysis using multivariate logistic regression analysis. Cheong et al^[68] assigned score values for each predictive variable: platelet (×10⁹/L): <100=2.4, 100-150=1, >150=0; PIIINP (µg/L): <8=0, >8=1.3; HA (ng/mL): <120=0, 120-360=1.2 and >360=1.5.

 

PHP index was constructed to predict cirrhosis by adding the assigned scores according to serum measurements for platelet count, HA, and PIIINP. The AUROC of 0.824 and 0.759 was reported for estimation and validation groups, respectively. The presence of cirrhosis was predicted precisely using a cut-off score of 4.0 (NPV 89.1%, PPV 87.5%, sensitivity 88.7% and specificity 99.6%). Hence, PHP index is a valuable scoring system to predict cirrhosis in patients with chronic viral hepatitis.^[68]

Angers algorithm was first described to chronic liver diseases patients in 2009. This model is calculated with the summation of the results of liver stiffness evaluation (LSE) by transient elastography+FibroMeter results. According to the report of Boursier et al,^[69] this algorithm is a useful tool to diagnose significant fibrosis (METAVIR ≥F2) and cirrhosis. In their study on 390 patients with chronic liver diseases, the model had a specificity of 72.7% and a sensitivity of 98.8% for significant fibrosis, whereas the values for cirrhosis were 97.1% and 74.7%, respectively. It seems that a combination of imaging tests with blood test is helpful to better understand what is happening in the liver.

 

 

Few studies have been focused on the role of HA-based algorithms in the diagnosis of liver fibrosis caused by other chronic liver diseases such as primary biliary cirrhosis, NAFLD and ALD. For example, Abignano et al^[70] evaluated the ELF test for estimation of fibrosis severity in patients with systemic sclerosis and reported that ELF correlated with disease severity, activity and disability. ELF score can be used as a non-invasive technique to measure the severity of primary biliary cirrhosis and provide useful prognostic information.^[71] Nobili et al^[72] examined 112 subjects with NAFLD and reported that the ELF score can be used to assess the level of liver fibrosis in pediatric patients. An additional study on patients with NAFLD has recently reported that ELF is able to detect severe, moderate and no fibrosis at AUROCs of 0.900, 0.820 and 0.760, respectively.^[73] In NAFLD patients, Dvorak et al^[74] found that HA (AUROC=0.940), Rosenberg index (AUROC=0.968) and ELF score (AUROC=0.972) were the most significant parameters to differentiate between significant liver fibrosis (F3 and F4) and mild to moderate or no fibrosis (F0-F2). Overall, according to the above reported studies, ELF score is an additional available tool to assess liver fibrosis stages, in CHC, primary biliary cirrhosis, NAFLD and systemic sclerosis. Thus, routine use of this score in the mentioned diseases should be justified by comprehensive studies. Legros et al^[75] applied HA and transient elastography to assess the severe fibrosis in C282Y homozygous HFE hemochromatosis patients with serum ferritin levels >1000 µg/L and/or increased serum transaminase levels. They reported that although HA was higher in patients with severe fibrosis, it did not accurately predict severe fibrosis; whereas transient elastography was able to precisely predict fibrosis stage in 77% of patients. Chwist et al^[76] suggested a panel of variables including age, platelet count, the ratio of erythrocyte count to red blood cell distribution width, visfatin, tissue polypeptide-specific antigen and HA to detect advanced fibrosis (Kleiner-Brunt stages 2 and 3) in NAFLD. This scoring system based on the above variables could predict advanced fibrosis with a high sensitivity of 75% and a specificity of 100%.

 

The formula, cut-off values, AUROC, sensitivity and specificity of the above mentioned algorithms for estimation of liver fibrosis severity in patients with chronic viral hepatitis and/or NAFLD/ NASH are summarized in the Table.

 

 

Physicians are dependent on liver biopsy in the assessment of liver fibrosis in various chronic liver diseases for nearly two centuries.^[77] With the understanding of disadvantages of this method, both patients and physicians prefer to abolish the old and invasive techniques that may be harmful to the health of humans.^[78] To reach this objective, a variety of safe and non-invasive tests have been introduced to improve human health, basic scientists, clinicians and clinical scientists are trying to translate basic scientific discoveries into clinical practice. HA-based algorithms are examples of attempt to overcome these human limitations. The algorithm scores based on HA are less invasive than liver biopsy, and may be used to assess liver fibrosis severity. Moreover, physicians can perform frequent analysis in assessing the effectiveness of the treatment.^[2] But most of these algorithms were largely investigated by cross-sectional studies,^[79] so studies should be shifted from cross-sectional to longitudinal to investigate the changes of chronic liver diseases. Thus, these tests have limited applications in clinics at present. With further research, it will not take a long time to highlight the practical value of these novel non-invasive tests.

 

 

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