Determinants of Bonacini score.
Liver is one of the most important organs in our body and any damage on this vital organ may be life threatening. Various factors such as toxins, heavy metals, drugs and infection with hepatitis viruses are able to influence on the function of this organ. One of the most important effects of these factors on liver is gradual necrosis of active liver cells and if this disturbance remained untreated, can lead to liver fibrosis, cirrhosis and finally death. Therefore, it is necessary to diagnose the liver fibrosis. Nowadays physicians rely on liver biopsy to evaluate liver fibrosis. This method was named as ‘gold standard’ for many years, but it seems researchers prefer to say “The best, not the gold standard”. Because of liver biopsy limitations (inter-observer variation amongst pathologists, fibrosis staging systems and sampling errors), patients and also physicians like to estimate the liver fibrosis stages and inflammation grades noninvasively. Therefore we had to find non invasive markers for estimation of liver fibrosis stage.
There are various serum based biomarkers, individually or in an algorithm model for estimation of liver fibrosis stage. The list of serum marker algorithms for assessment of liver fibrosis is increasing. [1-6]. In the last decade, the list of liver fibrosis noninvasive tools is increased rapidly. Some of them (such as transient ultrasound elastography [FibroScan], acoustic radiation force impulse [ARFI], magnetic resonance imaging [MRI], doppler analysis, computed tomography [CT], real-time elastography, tissue strain imaging, supersonic shear imaging, diffusion-weighted MRI, magnetic resonance spectroscopy, positron emission tomography [PET] and single photon emission computed tomography [SPECT] [5-8]) are beyond the scope of this chapter. In this chapter, we will focus on serum noninvasive algorithm-based scores surrogate of liver biopsy.
2. List of serum noninvasive algorithm-based scores
There are some serum-based noninvasive markers individually or in an algorithm-based score that proposed instead of liver biopsy. These markers are usable in clinic, because the physicians are able to request analysis of them frequently, during the treatment and for assessing the treatment efficacy, too.
The lists of individual markers are long. The most common of them that are introduced from the past are: aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin, alkaline phosphatase, albumin, prothrombin time and gamma glutamyl transferase [2-6]. Now it is proposed that some of these simple tests are not able to predict liver injury accurately. For example Seto showed that an elevation of serum ALT levels is not able to predict liver injury .
There are some markers that newly introduced and most well-known of them are: α-2-macroglobulin, apolipoprotein A1, collagen markers such as procollagen I carboxyl terminal peptide (PICP), procollagen III amino terminal peptide (PIIINP), procollagen IV C peptide, procollagen IV N peptide (7-S collagen) and collagen IV, collagenases (metalloproteinase) and their inhibitors (tissue inhibitors of metalloproteinase), glycoproteins such as human cartilage glycoprotein (YKL-40), fibronectin, laminin, osteonectin, tenascin, glycosaminoglycans such as perlecan, hyaluronic acid, decorin, aggrecan, lumican, fibromodulin and the others [2-4, 10-12]. Some of these parameters increased in various diseases such as cancer; therefore before the using of these parameters for estimation of liver fibrosis, we had to exclude the other diseases .
It seems that these serum individual markers are useful for establishing the presence, but not absence of the fibrosis. For overcome on this problem, researchers combined the results of panels of markers and proposed various algorithms. Interestingly, in some algorithms improve in diagnostic accuracy of these noninvasive markers were observed. Again the lists of these markers are long. Some of the most well-known of them are:
AST to platelet ratio index, Age-platelet index, PGA index, Forns, Bonacini, De Ritis, PATEL, Leroy, FibroSpect, European Liver Fibrosis score, Fibrometer, Hepascore, SHASTA Index, FIB-4, SteatoTest, NAFLD Fibrosis Score, Cirrhosis discriminate score, BARD score, Hui model, FibroMeter NAFLD, Fibrosis Probability Index, Lok index, Fibro Q and the others.
The aim of this chapter is describing the formula, usability and diagnostic accuracy of some of the most common and available serum noninvasive algorithm-based scores in various liver diseases in a simple manner.
3. Age-Platelet (AP) index
Age and platelet counts are major constituents of this index. This index is calculated according to the following instruction:
Sum of age and platelets counts scores are AP index . Various studies proposed that this index is a good index for estimating liver fibrosis stage. For example in a study that carried out by Lackner  on HCV patients, area under the ROC curve (AUROC) was 0.740 for prediction of significant fibrosis and for prediction of cirrhosis, AUROC was 0.910 (according to Ishak score). In another study on 62 hepatitis patients, Parsian et al reported an AUROC of 0.818 for discrimination of patients with liver fibrosis vs. control group and an AUROC of 0.518 for discrimination of patients with mild from those with significant fibrosis . There are other studies that discussed diagnostic accuracy of this simple index .
4. AST to Platelet Ratio Index (APRI)
APRI score was initially described by Wai et al . This index is dependent to two routine tests, i.e. aspartate aminotransferase and platelet count and simply calculated by this formula:
Several studies have suggested that the APRI may be a useful noninvasive marker of hepatic fibrosis. Shin et al showed that in chronic hepatitis B patients, the predictive power of detecting signiﬁcant ﬁbrosis based on the AUROC is 0.850–0.950 and concluded that this index is a useful indirect marker for estimating signiﬁcant ﬁbrosis .
The AUROC of APRI in HCV patients was reported better in female (0.871) than in male (0.753). Among female cases, an APRI value >1.4 was 91% sensitive and 75% specific in detecting a staging score >2. The corresponding values among male cases were 60% and 77%, respectively .
5. AAR index (Deritis index)
There is another simple index that used for evaluation of liver fibrosis and researchers named it AAR index. By dividing the levels of two routine enzymes, i.e. AST and ALT, we are able to calculate it .
In a study that carried out on 111 NAFLD patients, researchers found an AUC of 0.61 with an AST/ALT ratio of 0.8 for the prediction of advanced fibrosis . Park et al reported that AST/ALT > or = 1 is highly specific but not diagnostic for the presence of cirrhosis in patients with chronic HCV infection and concluded that the ratio reflects the grade of fibrosis in these patients .
6. Fibro Q
Fibro Q is a index that is more complicated than previous indices and is dependent to age (years), AST level, platelet count and also PT International Normalized Ratio (INR). PT INR measures the extrinsic pathway of coagulation. We are able to calculate this index by the following formula .
In 2012, Hsieh et al showed that FibroQ is a simple and useful test for predicting significant fibrosis in patients with chronic hepatitis C . In our previous study, we found that this index had a reasonable AUROC for discrimination of patients with liver fibrosis vs. control and not for discrimination of patients with severe liver fibrosis vs. mild liver fibrosis .
Other simple index that related to age (years), AST and ALT levels (U/L) and platelet count, is FIB-4 . By the following formula, one can calculate its value:
Yang et al reported that FIB-4 index had a significant power for differentiation between patients with mild and significant fibrosis in nonalcoholic fatty liver disease (0.24 ± 0.12 vs. 0.31 ± 0.21, P = 0.010) and the AUROC of FIB4 was 0.810. They reported that FIB4 might be useful as a noninvasive hepatic fibrosis scores for predicting hepatic fibrosis in patients with NAFLD . Shah et al  in a study on 541 NAFLD adults found that among various serum based algorithms, FIB4 had better diagnostic accuracy for estimation of liver fibrosis.
8. BARD score
In 2008, Harrison et al proposed an index and named it BARD score. The constituents of this score are body-mass index (BMI), AST/ALT ratio (AAR), and presence of type 2 diabetes mellitus (DMt2). They combined these three simple variables to propose a score for predicting advanced fibrosis. Calculating of this score is simple:
BMI ≥ 28 kg/m2 = 1 point, BMI <28 kg/m2 = 0 point; AST/ALT ratio ≥ 0.8 = 2 points, AST/ALT ratio < 0.8 = 0 points; freshly recognized or preexisting DMt2 = 1 point.
A total of 2-4 points (sum of the score of BMI, AST/ALT ratio and presence or absence of DMt2) indicate significant fibrosis . In a study that carried out by Wyszomirska in 2010, validation of this score on nonalcoholic fatty liver disease (NAFLD) evaluated. They reported that this scoring system has value for diagnosis of advanced fibrosis in NAFLD patients and if it is possible using of this system in clinic, most patients don’t need to undergo liver biopsy . As another try to determine the validation of this score, Ruffillo et al, evaluated the diagnostic accuracy of this score in NAFLD patients and concluded that this score is useful in identifying patients without severe fibrosis .
9. Enhanced Liver Fibrosis (ELF)
This liver fibrosis score is derived from markers that are not available in routine clinical laboratories [29-30]. It was shown that this score has good diagnostic accuracy for the detection of moderate and severe fibrosis .
ELF panel constituents are matrix metalloproteinase 1 inhibitor, hyaluronic acid, and aminoterminal peptide of pro-collagen III.
ELF score can calculate by this formula :
In a study that carried out by Nobili et al on 112 NAFLD subjects, concluded that the ELF test is able to accurately discriminate patients with liver fibrosis and can be used for assessing the level of liver fibrosis in pediatric patients .
10. Cirrhosis Discriminate Score (CDS) Bonacini
This simple score is proposed by Bonacini  and its constituents are AST/ALT, PT-INR and platelet count in a simple manner as described in table 1 . According to this score, different points are given to ingredients of this index and ultimately they added together.
There are various studies that analyzed the accuracy of this index for assessing liver fibrosis. For example in a study by Colli et al  on 176 patients with chronic HCV infection, they observed that this score was able to identify 67% of patients with a high (>75%) or low (<10%) probability of cirrhosis, and ultimately 33% of the HCV patients need liver biopsy for assessing their liver fibrosis score. Against the above study, there are some studies that found this score was not able to detect severe fibrosis and observed this score has low power of discrimination .
11. Fibrotest (FT)
Fibrotest is consisting of a panel of five biomarkers that proposed for evaluation of liver fibrosis stage. Constituents of this index are alpha2-macroglobulin, haptoglobin, apolipoprotein A1, gamma glutamyl transpeptidase and total bilirubin  and the formula of this index is:
Fibrotest=4467 × log [alpha2-macroglobulin (gr/l)] – 1357 × log [haptoglobin (gr/l)] + 0.0821 × [age (years)] +1737 × log [bilirubin (μmol/l)] - 1184 × [apolipoprotein A1 (gr/l)] + 0.301 × sex (male=1, female=0) – 5.054
FT is the most studied test and now there are various studies that proposed this test has considerable ability for discrimination of liver fibrosis without significant divergence among liver diseases. For example its ability on hepatitis C, hepatitis B, alcoholic and nonalcoholic fatty liver disease was shown [37-40].
In a study that carried out by Sebastiani et al  the ability of several noninvasive markers including FT on 110 CHB patients were assessed. They found that the Fibrotest and APRI had the highest diagnostic accuracy for discrimination of patients with severe stage of liver fibrosis. In another study on 221 chronic alcoholic liver disease patients, Naveau et al  reported that in heavy alcohol drinkers, this test is able to estimate the liver fibrosis stage and by this test is possible to decrease the need for liver biopsy.
Fibrometer is a regression algorithm that proposed for evaluation of liver fibrosis stage in liver diseases. Glucose, AST, ferritin, platelet, ALT, body weight and age are the variables that this score is derived from them. First time, this score proposed by Cales et al . Calculation of this score is simple by the following formula:
Various groups of researchers determined the diagnostic accuracy of this index versus liver biopsy in liver disease. For example Cale et al  analyzed the accuracy of this index on 235 NAFLD patients. They observed that this score, in comparison with the other scores such as APRI, had higher accuracy rate for significant fibrosis and had lower misclassification rate. Finally, they concluded that Fibrometer had a reasonable performance.
13. Fibrosis Probability Index (FPI) or sud index
This index is depended on: any history of alcohol use, patient age, cholesterol level, AST activity and HOMA-IR index (Homeostasis model assessment: insulin resistance) . First time Sud et al proposed this index and published the results of their study in hepatology journal . They considered and analyzed thirty-five variables in 176 HCV patients and proposed a fibrosis probability index (FPI) for estimation of the liver fibrosis stages by some of these parameters. In addition, they tested the accuracy of this index in another study consists of 126 patients.
It seems, this index that proposed by this research team can be used as a noninvasive tools for estimation of liver fibrosis and this confirmed by others .
14. Forns score
Another score for assessing the liver fibrosis score is Forn score . Constituents of this score are age, platelet count, GGT, and cholesterol. One can calculate this index by this equation:
In their study on 476 CHC patients, Forn et al reported the AUROC of this score was 0.86 for the estimation group and 0.81 for the validation group. Yang et al  evaluated the accuracy of Forn score in 77 NAFLD children and according to the severity of their necroinflmmatory injuries, divided the patients to two groups: mild fibrosis (stage 0-1) vs. significant fibrosis (stage 2-4). They analyzed diagnostic accuracy of some simple algorhitms such as AAR, APARI and FIB-4, in addition to Forn score. They observed that among these algorithms, only APRI and FIB4 had a significant difference between patients with mild and significant fibrosis.
Hepascore is a more complicated score for estimation of liver fibrosis stage. Age, gender, bilirubin, GGT, hyaluronic acid and alpha2-macroglobulin are constituents of this score . In this score that was proposed by Adams et al for the first time, they added a single direct biomarker of hepatic fibrosis i.e. hyaluronic acid in their algorithm.
By this formula, we can calculate this score:
In their study, Adams et al on 221 HCV patients reported AUROCs of 0.82, 0.90 and 0.89 for the diagnosis of significant fibrosis, extensive fibrosis and cirrhosis, respectively.
In another study on patients with chronic hepatitis C virus, Becker et al  reported that Hepascore is able to predict liver fibrosis and maybe by this score a need for liver biopsy is decrease. Bourliere et al reported that the accuracy of Hepascore for excluding the cirrhosis was excellent .
16. HUI model
Hui et al performed a retrospective study on 235 CHB patients and by univariate analysis and multivariate logistic regression generated this predictive model. They concluded that this model is able to predict absence of significant fibrosis, accurately. Body mass index (BMI), platelet count, serum albumin, and total bilirubin levels are the constituents of this model . Calculation of this model is very simple and we can calculate this model score by this following formula.
In a study that carried out by Sebastiani on 110 CHB patients, some indices such as, APRI, Forns’ index, Hui’s model and Fibrotest were determined. They reported that performance of these methods for discrimination of patients with significant fibrosis as positive predictive values (PPV) was excellent, i.e. 100% for Forns and greater than 92% for APRI, GUCI, Fibrotest and Hui model .
17. Leroy score or MP3 model
Leroy et al used extracellular matrix component i.e. procollagen III amino terminal peptide (PIIINP) and matrix metalloproteinases-1 (MMP-1) for proposing this model. They determined serum levels of hyaluronate (HA), PIIINP, MMP-1, MMP-2, MMP-9 and their tissue inhibitors of metalloproteinases (TIMP-1 and TIMP-2) in 194 chronic hepatitis C patients and 194 healthy people. They found that six markers (MMP-2, TIMP-1, HA, PIIINP, MMP-1 and MMP-9) were significantly correlated with liver fibrosis and by using regression model they proposed their index according to the following formula:
They showed that their scoring system is able to differentiate between mild and significant fibrosis and pointed that maybe this score can provide a useful tool for evaluating liver fibrosis .
18. Lok index
Another simple scoring system for estimation of liver fibrosis stage that proposed by Lok et al is Lok index . This index is based on serum AST and ALT levels, platelet count and PT-INR. One can calculate this index according to a simple formula:
For this scoring system two cut points introduced: 0.2 to rule out cirrhosis and 0.5 to confirm cirrhosis. All values that are between these cut points are considered indeterminate. Lok et al in their cohort study on 1,141 patients with CHC, introduced this score and reported an AUROC of 0.78-0.81 to detect cirrhosis.
There are various studies that used this score for estimation of liver fibrosis stage. Castera et al reported that this index has a reasonable performance . Masuzaki studied 386 CHC patients for determining the diagnostic accuracy of this index in comparison with the other noninvasive indices such as APRI. They reported that Lok index and APRI were correlated with histological fibrosis stages (rho=0.581, and 0.460, respectively) and reported an AUROC (95% CI) of Lok index and APRI of 0.787 (0.741-0.832) and 0.692(0.639-0.745), respectively .
19. FIBROSpect or PATEL index
Patel et al  by combing the serum levels of hyaluronic acid (HA), tissue inhibitors of metalloproteinase 1 (TIMP-1) and alpha2-macroglobulin (A2M), introduced this index that is also has another name, FibroSpect. Against the much scores that are based on the routine and simple laboratory tests, this score is based on the specific and non routine laboratory test. In a study that carried out by their research team on 696 HCV patients, they introduced this scoring system and reported an AUROC of 0.830 for the diagnosis of significant fibrosis.
There are some studies that confirmed the diagnostic accuracy of this scoring system and reported the excellency of this scoring system for estimation of liver fibrosis .
20. PGA index
PGA index can be calculated by the sum of three tests, i.e., P, prothrombin time; G, gamma-glutamyl transpeptidase and A, apoliprotein AI and is ranged from 0 to 12 .
There are various studies that showed the accuracy of this index in patients with various chronic liver diseases [60-61]. These studies reported that this score has highest accuracy for detecting cirrhosis in patients with alcoholic liver disease.
Against the above mentioned studies, Yang et al didn’t find similar results . In their study on 77 NAFLD children, diagnostic accuracy of PGA index (AUROC=0.45) in addition to the other tests such as AAR (0.53), APRI (0.70), Forns index (0.73) and FIB-4 (0.81) were compared. They concluded that among these indices, APRI and FIB4 had the highest performance for discrimination of patients with mild from significant fibrosis.
21. SHASTA index
This index first time introduced by Kelleher et al . They studied 95 patients that had HIV/HCV co-infection and determined the serum levels or activity of ALT, AST, APRI, albumin, total bilirubin, hyaluronic acid (HA) and YKL-40. They observed that among these tests, only serum HA, albumin and AST were useful for discrimination of mild from advanced fibrosis. Finally they constructed a predictive model by these factors (HA, albumin and AST) and named it SHASTA Index. This index can be calculated by the following formula:
A cut off of 0.8 of this index showed specificity and positive predictive value of 100%. A cutoff of <0.30 was associated with a sensitivity and negative predictive value of 88% and 94%, respectively.
22. STEATO test panel
This index is an index that proposed by researchers for estimation of liver steatosis in alcoholic, NAFLD, hepatitis C and B patients. By combining the simple parameters such as age, sex, and BMI with AST, ALT, bilirubin, GGT, alpha2-macroglobulin, apolipoprotein AI, haptoglobin, glucose, cholesterol, and triglyceride this index proposed. First time, Poynard et al proposed this index by the above mentioned variables. They found that steatoTest is able to predict liver steatosis and can decrease the need for liver biopsy .
In a study that carried out by Lassailly, diagnostic accuracy of this panel confirmed on two hundred and eighty-eight patients with morbid obesity and concluded that this panel is able to reduce the need for biopsy .
23. NAFLD fibrosis score
Components of this score that proposed by Angulo et al are age (years), body mass index, impaired fasting glucose (IFG) or presence the diabetes, AST, ALT, platelet count, and serum albumin levels . They proposed this index as a predictive model for estimation of liver fibrosis. By using the following formula, we are able to calculate this scoring system.
A score value < -1.455 (lowest cut point) is able to exclude advanced liver fibrosis and a value > 0.676 (high cutoff) is able to predict advanced liver. Values between these two values are considered as indeterminate liver fibrosis and maybe patients with this result had to undergo liver biopsy.
Shah et al studied diagnostic accuracy of this scoring model in 541 adults with NAFLD for estimation of liver fibrosis and found an AUROC (95% confidence interval [CI]) of 0.768; (0.720 – 0.816) for this model . In another study that carried out by Ruffillo et al on 138 NAFLD patients, they concluded that this scoring model is useful for identifying patients without severe fibrosis, but it has some indeterminate results . In another study that carried out by Yang et al on 77 NAFLD children, AUROC of NAFLD fibrosis score was determined 0.58 for discrimination of mild from significant liver fibrosis .
24. Göteborg University Cirrhosis Index (GUCI index)
First time, this index proposed by Kandemir et al .They analyzed blood samples from 68 CHC patients for aspartate aminotransferase, prothrombin INR, and platelet count and found a strong association between the fibrosis stage and their proposed index. The GUCI index can be calculate by this formula:
They concluded that this index can discriminate patients with severe fibrosis (stage 3-4) from those without severe fibrosis (stage 0-2).
25. King score
This score introduced by Cross et al and is a simple index that derived from age, AST activity INR and platelet count, as follow for the estimation of liver fibrosis.
Cross et al in their study on 923 CHC patients introduced this scoring model. They reported AUROCs for predicting of cirrhosis and significant fibrosis of 0.91 and 0.79 (respectively) and the results was confirmed in a validation study. They concluded that this scoring is an applicable index for predicting of cirrhosis in chronic hepatitis C patients .
Diagnostic accuracy of this scoring model determined in another study. Cross et al performed another study on 187 CHC patients and evaluated the diagnostic accuracy of King score (KS) model and Fibroscan (FS). For diagnosis of significant fibrosis, AUROCs for FS, KS and FS + KS were 0.83, 0.82 and 0.85 (respectively) and for the diagnosis of cirrhosis these values were 0.96, 0.89 and 0.93, respectively .
26. Predicted Liver Fibrosis score (PLF score)
Bota et al in their study on 212 CHC patients proposed a scoring system for estimation of liver fibrosis and named it predicted liver fibrosis score . Constituents of their score are transient elastography (TE), APRI score, Forns score, FIB-4 score and King score. Their scoring formula is:
They found a good correlation (r = 0.68) for their score with liver fibrosis and concluded that this complex formula that derived from the other algorithms, is able to predict sever fibrosis and its performance is better than TE.
The cut off values, AUROC, positive and negative predictive values of the above mentioned algorithms for estimation of liver fibrosis severity in chronic viral hepatitis and/or NAFLD/NASH is summarized in the table 2.
|AAR||AST and ALT||HCV||≥1||NA||73.7||88.1|
|AP||Age and platelets counts||HCV||≥6||t=0.76
|APRI||AST and platelets counts||HCV||≥1.5
|BARD score||BMI, AST, ALT and type 2 diabetes mellitus||NAFLD/
|Advanced liver fibrosis (F3-4)|
|CDS||PT-INR, AST, ALT and platelets counts||HCV||>8||NA||92.6||NA||≈Knodell 3-4|
|ELF||Matrix metalloproteinase 1 inhibitor, hyaluronic acid, and aminoterminal peptide of pro-collagen III||HBV
|0.8||35||92||≈ Scheuer 3-4
≈ Scheuer 0-2
|FIB4||Age, AST, ALT and platelet count||HCV||<1.45
|Fibro Q||Age, AST, platelet count and PT-INR||HBV
|Fibrometer||Glucose, AST, ferritin, platelet, ALT, body weight and age||NAFLD||NA||NA||88||92|
|FIBROSpect||Hyaluronic acid, tissue inhibitors of metalloproteinase 1 and alpha2-macroglobulin||HCV||0.82-0.97||NA||90||58||For stage ≥F2|
|FORNS SCORE||Age, platelet count, GGT, and cholesterol||HCV||≥6.9
|66||96||≈ Scheuer 2-4
≈ Scheuer 0-1
|FPI||Alcohol use, age, cholesterol, AST and HOMA-IR index||HCV||<0.2
|FT||Alpha2-macroglobulin, haptoglobin, apolipoprotein A1, gamma glutamyl transpeptidase and bilirubin||HCV
|Göteborg University Cirrhosis Index (GUCI index)||AST,PT-INR, and platelet count||HCV||NA||F4=0.85||F4=31||F4=97|
|Hepascore||Age, gender, bilirubin, GGT, hyaluronic acid and alpha2-macroglobulin||HCV||≥0.5
|HUI model||BMI, platelet count, albumin and bilirubin||HBV||<0.15||t=0.803
|King Score||Age, AST, INR and platelet count||HCV||≥12.3
|Leroy score or MP3 model||Procollagen III amino terminal peptide and matrix metalloproteinases-1||HCV||NA||≥F2, 0.82||≥F2=89||≥F2=84|
|Lok index||AST, ALT, platelet count and PT-INR||HCV||NA||F4=0.78-.0.81||F4=32-75||F4= 84-91|
|NAFLD fibrosis score||Age, BMI, impaired fasting glucose (IFG) or presence the diabetes, AST, ALT, platelet count, and serum albumin levels||NAFLD/
|PLF score||Transient elastography, APRI score, Forns score, FIB-4 score and King score||HCV||F ≥ 1= 1.77
F ≥ 2= 2.88
F ≥ 3=2.47
F=4, = 2.98
|SHASTA index||ALT, AST, APRI, albumin, total bilirubin, hyaluronic acid and YKL-40||HCV||>.08
|STEATO test panel||ALT, α2-macroglobulin , apolipoprotein A-I, haptoglobin, total bilirubin, GGT,
cholesterol, triglycerides, glucose, age, gender and BMI
|For predicting liver steatosis greater than 5%.|
AUROC, area under receiver operating characteristic curve; NA, Not available; PPV, positive predictive value; NPV, negative predictive value; ALT, alanine aminotransferase; AST, aspartate aminotransferase; MMP, metalloproteinases TIMP, tissue inhibitors of matrix metalloproteinases; HOMA-IR, homeostasis model assessment of insulin resistance; INR, international normalized ratio for prothrombie; t, training group; v, validation group [3, 15, 22,23, 52, 63, 67, 69, 71].
Liver biopsy still has an important role in the estimation of liver fibrosis stage, but it is not very far that this invasive method will completely replace with the other noninvasive methods such as serum based biomarkers . Therefore development of non-invasive methods is still needed and we should work harder for finding an appropriate method. Maybe with finding some appropriate biomarkers, we can completely obsolete the liver biopsy.
AcknowledgmentsThe authors greatly thanks from all patients that permitted to the researchers to analyze their serum or tissue for research purposes.
Bedossa P, Carrat F. Liver biopsy: The best, not the gold standard. Journal of Hepatology 2009; 50: 1–3.
Stella M. Martınez, Gonzalo Crespo, Miquel Navasa, and Xavier Forns. Noninvasive Assessment of Liver Fibrosis. Hepatology 2011; 53: 325-35.
Papastergiou V, Tsochatzis E, Burroughs AK. Non-invasive assessment of liver fibrosis. Annals of Gastroenterology 2012; 25: 1-14.
Rossi E, Adams LA, Bulsara M, Jeffrey GP. Assessing Liver Fibrosis with Serum Marker Models. Clin Biochem Rev 2007; 28: 1-10.
Baranova A, La P, Birerdinc A and Younossi ZM. Non-Invasive markers for hepatic fibrosis. BMC Gastroenterology 2011; 11: 91.
Yang H R, Kim H R, Kim M J, Ko J S, and Seo J K. Noninvasive Parameters and hepatic fibrosis scores in children with nonalcoholic fatty liver disease. World J Gastroenterol. 2012 7; 18(13): 1525–30.
Diarmuid S, Manning Nezam, H. Afdhal. Diagnosis and Quantitation of Fibrosis. Gastroenterology 2008; 134: 1670–81.
Shah AG, Lydecker A, Murray K, Tetri BN, Contos MJ, Sanyal AJ; Nash Clinical Research Network. Comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2009 Oct; 7(10): 1104-12. Epub 2009 Jun 10.
Seto W-K, Lai C-L, Ip PPC, Fung J, Wong DK-H, et al. A Large Population Histology Study Showing the Lack of Association between ALT Elevation and Significant Fibrosis in Chronic Hepatitis B. PLoS ONE 2012; 7(2): e32622. doi:10.1371/journal.pone.0032622.
Mato JM, Lu SC. Where are we in the search for noninvasive nonalcoholic steatohepatitis biomarkers? Hepatology 2011 Oct; 54(4): 1115-7. doi: 10.1002/hep.24642.
Parsian H, Nouri M, Rahimipour A, Somi MH, Qujeq D. Comparison of five liver fibrosis indexes with serum levels of laminin and N terminal peptide of procollagen type III in chronic hepatitis patients. Liver Biopsy. Rijeka, Croatia: InTech. 2011.
Parsian H, Rahimipour A, Nouri M, Somi M Hand Qujeq D. Assessment of liver fibrosis development in chronic hepatitis b patients by serum hyaluronic acid and laminin levels. Acta Clin Croat 2010; 49: 257-65.
Aghcheli K, Parsian H,, Qujeq D, Talebi M, Mosapour A, Khalilipour E, Islami F, Semnani S, Malekzadeh R. Serum hyaluronic acid and laminin as potential tumor markers for upper gastrointestinal cancers. European Journal of Internal Medicine 2012; 23: 58–64.
Poynard T, Bedossa P. Age and platelet count: a simple index for predicting the presence of histological lesions in patients with antibodies to hepatitis C virus. METAVIR and CLINIVIR Cooperative Study Groups. J Viral Hepatol 1997; 4: 199-208.
Lackner C, Struber G, Liegl B, Leibl S, Ofner P, Bankuti C, Bauer B, Stauber RE. Comparison and Validation of Simple Noninvasive Tests for Prediction of Fibrosis in Chronic Hepatitis C. Hepatology 2005; 41(6): 1376-82.
Parsian H. Is the aspartate transaminase to platelet ratio adequate for the assessment of liver fibrosis in patients with chronic liver disease? Hepat Mon. 2011; 11(5): 380-81.
Yilmaz Y, Yonal O, Kurt R, Bayrak M, Aktas B, Ozdogan O. Noninvasive assessment of liver fibrosis with the aspartate transaminaseto platelet ratio index (APRI): Usefulness in patients with chronic liver disease. Hepat Mon 2011; 11(2): 103-7.
Wai CT, Greenson JK, Fontana RJ, Kalbfleisch JD, Marrero JA, Conje evaram HS, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 2003; 38(2): 518-26.
Shin WG, Park SH, Jang MK, Hahn TH, Kim JB, Lee MD, Kim DJ,. Jun SY, Park CK. Aspartate aminotransferase to platelet ratio index (APRI) can predict liver ﬁbrosis in chronic hepatitis B. Digestive and Liver Disease 2008; 40: 267–74.
Williams AL, Hoofnagle JH. Ratio of serum aspartate to alanine aminotransferase in chronic hepatitis Relationship to cirrhosis. Gastroenterology 1988; 95: 734-9.
Kruger FC, Daniels CR, Kidd M, Swart G, Brundyn K, Rensburg KV, Kotze M. APRI: A simple bedside marker for advanced fibrosis that can avoid liver biopsy in patients with NAFLD/NASH. S Afr Med J 2011; 101: 477-80.
Park GJ, Lin BP, Ngu MC, Jones DB, Katelaris PH. Aspartate aminotransferase: alanine aminotransferase ratio in chronic hepatitis C infection: is it a useful predictor of cirrhosis?J Gastroenterol Hepatol 2000 Apr; 15(4): 386-90.
Hsieh YY, Tung SY, Lee IL, Lee K, Shen CH, Wei KL, Chang TC, Chuang CS, Wu CS, Lin YH. FibroQ: An Easy and Useful Noninvasive Test for Predicting Liver Fibrosis in Patients with Chronic Viral Hepatitis. Chang Gung Med J 2009; 32(6): 614-22.
Hsieh YY, Tung SY, Lee K, Wu CS, Wei KL, Shen CH, Chang TS and Lin YH. Routine blood tests to predict liver fibrosis in chronic hepatitis C. World J Gastroenterol 2012; 18(8): 746-53.
Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 2006; 43: 1317-25.
Harrison SA, Oliver D, Arnold HL, Gogia S, Neuschwander-Tetri BA. Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease. Gut. 2008 Oct; 57(10): 1441-7. Epub 2008 Apr 4.
Raszeja-Wyszomirska J, Szymanik B, Ławniczak M, Kajor M, Chwist A, Milkiewicz P, Hartleb M. Validation of the BARD scoring system in Polish patients with nonalcoholic fatty liver disease (NAFLD). BMC Gastroenterol 2010 Jun; 28(10): 67.
Ruffillo G, Fassio E, Alvarez E, Landeira G, Longo C, Domínguez N, Gualano G. Comparison of NAFLD fibrosis score and BARD score in predicting fibrosis in nonalcoholic fatty liver disease. J Hepatol 2011 Jan; 54(1): 160-3. Epub 2010 Aug 22.
Rosenberg WM, Voelker M, Thiel R, et al. Serum markers detect the presence of liver fibrosis: a cohort study. Gastroenterology 2004; 127: 1704–13.
Nobili V, Parkes J, Bottazzo G, Marcellini M, Cross R, Newman D, Vizzutti F, Pinzani M, Rosenberg WM. Performance of ELF serum markers in predicting fibrosis stage in pediatric non-alcoholic fatty liver disease. Gastroenterology. 2009 Jan; 136(1): 160-7. Epub 2008 Sep 20.
Parkes J, Guha IN, Roderick P, Harris S, Cross R, Manos MM, Irving W, Zaitoun A, Wheatley M, Ryder S, Rosenberg W. Enhanced Liver Fibrosis (ELF) test accurately identifies liver fibrosis in patients with chronic hepatitis C. J Viral Hepat. 2011 Jan; 18(1): 23-31. doi: 10.1111/j.1365-2893.2009.01263.x.
Parkes J, Bialek SR, Bell BP, et al. ELF serum markers accurately distinguish fibrosis severity in chronic hepatitis C: an external validation study. Hepatology 2006; 44(Suppl 1): 321.
Bonacini M, Hadi G, Govindarajan S, Lindsay KL. Utility of a discriminant score for diagnosing advanced fibrosis or cirrhosis in patients with chronic hepatitis C virus infection. Am J Gastroenterol 1997; 92: 1302-4.
Agostino Colli, Alice Colucci, Silvia Paggi, Mirella Fraquelli, Sara Massironi, Marco Andreoletti, Vittorio Michela, Dario Conte. Accuracy of a predictive model for severe hepatic ﬁbrosis or cirrhosis in chronic hepatitis C. World J Gastroenterol 2005; 11(46): 7318-22.
Saadeh S, Cammell G, Carey WD, Younossi Z, Barnes D, Easley K. The role of liver biopsy in chronic hepatitis C. Hepatology 2001; 33: 196-200.
Contreras RH, Callewaert NLM. Serum marker for measuring liver fibrosis. U.S. patent 7244619; 2007.
Poynard T, Imbert-Bismut F, Munteanu M, Messous D, Myers RP, Thabut D, et al. Overview of the diagnostic value of biochemical markers of liver fibrosis (FibroTest, HCVFibrosure) and necrosis (ActiTest) in patients with chronic hepatitis C. Comp Hepatol 2004; 3: 8.
Poynard T, Imbert, Bismut F, Munteanu M, Ratziu V. FibroTest-FibroSURE: Towards a universal biomarker of liver fibrosis. Expert Rev Mol Diagn 2005; 5: 15–21.
Morra R, Munteanu M, Imbert Bismut F, Messous D, Ratziu V, Poynard T. FibroMAX: Towards a new universal biomarker of liver disease. Expert Rev Mol Diagn 2007; 7: 481–90.
Poynard T, Morra R, Halfon P, Castera L, Ratziu V, Imbert,Bismut F, et al. Meta-analyses of Fibrotest diagnostic value in chronic liver disease. BMC Gastroenterol 2007; 7: 40.
Sebastiani G, Vario A, Guido M, Alberti A. Sequential algorithms combining non-invasive markers and biopsy for the assessment of liver fibrosis in chronic hepatitis B. World J Gastroenterol 2007; 13: 525-31.
Naveau S, Raynard B, Ratziu V, Abella A, Imbert-Bismut F, Messous D, Beuzen F, Capron F, Thabut D, Munteanu M, Chaput JC, Poynard T. Biomarkers for the prediction of liver fibrosis in patients with chronic alcoholic liver disease. Clin Gastroenterol Hepatol 2005 Feb; 3(2): 167-74.
Cales P, Oberti F, Michalak S, Hubert-Fouchard I, Rousselet MC, Konate A, et al. A novel panel of blood markers to assess the degree of liver fibrosis. Hepatology 2005; 42: 1373–81.
Cale P, Laine F, Boursier J, Deugnier Y, Moal V, Oberti F, Hunault G, Rousselet MC, Hubert I, Laaﬁ J, Ducluzeaux PH, Lunel F. Comparison of blood tests for liver ﬁbrosis speciﬁc or not to NAFLD. Journal of Hepatology 2009; 50: 165–73.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28(7): 412-9.
Sud A, Hui JM, Farrell GC, Bandara P, Kench JG, Fung C, Lin R, Samarasinghe D, Liddle C, McCaughan GW, George J. Improved prediction of fibrosis in chronic hepatitis C using measures of insulin resistance in a probability index. Hepatology 2004; 39:1239-47.
Laurent Castera. Noninvasive Methods to Assess Liver Disease in Patients with Hepatitis B or C. Gastroenterology 2012; 142: 1293–1302.
Forns X, Ampurdanes S, Llovet JM, et al. Identification of chronic hepatitis C patients without hepatic fibrosis by a simple predictive model. Hepatology 2002; 36: 986-92.
Adams LA, Bulsara M, Rossi E, DeBoer B, Speers D, George J, et al. Hepascore: an accurate validated predictor of liver fibrosis in chronic hepatitis C infection. Clin Chem 2005; 51: 1867-73.
Becker L, Salameh W, Sferruzza A, Zhang K, ng Chen R, Malik R, Reitz R, Nasser I, Afdhal NH. Validation of hepascore, compared with simple indices of fibrosis, in patients with chronic hepatitis C virus infection in United States. Clin Gastroenterol Hepatol 2009 Jun; 7(6): 696-701.
Bourliere M, Penaranda G, Ouzan D, Renou C, Botta-Fridlund D, Tran A, et al. Optimized stepwise combination algorithms of non-invasive liver fibrosis scores including Hepascore in hepatitis C virus patients. Aliment Pharmacol Ther 2008; 28: 458-67.
Hui AY, Chan HL, Wong VW, et al. Identification of chronic hepatitis B patients without significant liver fibrosis by a simple noninvasive predictive model. Am J Gastroenterol 2005; 100: 616–23.
Leroy V, Monier F, Bottari S, Trocme C, Sturm N, Hilleret MN, Morel F,Zarski JP. Circulating matrix metalloproteinases 1, 2, 9 and their inhibitors TIMP-1 and TIMP-2 as serum markers of liver fibrosis in patients with chronic hepatitis C: comparison with PIIINP and hyaluronic acid. Am J Gastroenterol 2004; 99: 271-9.
Lok AS, Ghany MG, Goodman ZD, et al. Predicting cirrhosis in patients with hepatitis C based on standard laboratory tests: results of the HALT-C cohort. Hepatology 2005; 42: 282–92.
Castera L, Le Bail B, Roudot-Thoraval F, et al. Early detection in routine clinical practice of cirrhosis and oesophageal varices in chronic hepatitis C: comparison of transient elastography (FibroScan) with standard laboratory tests and non-invasive scores. J Hepatol 2009; 50: 59-68.
Masuzaki R, Tateishi R, Yoshida H, Goto E, Sato T, Ohki T, Goto T, Yoshida H, Sugioka Y, Ikeda H, Kanai F, Hamamura K, Shiina S, Kawabe T and Omata M. M1570 Prospective Comparison of Liver Stiffness Measurement and Lok Index in Chronic Hepatitis C Patients. Gastroenterology 2008; 134: (4) Supplement 1: Page A-795.).
Patel K, Gordon SC, Jacobson I, et al. Evaluation of a panel of non-invasive serum markers to differentiate mild from moderate-to-advanced liver fibrosis in chronic hepatitis C patients. J Hepatol 2004; 41: 935-42.
Christensen C, Bruden D, Livingston S, Deubner H, Homan C, Smith K, et al. Diagnostic accuracy of a fibrosis serum panel (FIBROSpect II) compared with Knodell and Ishak liver biopsy scores in chronic hepatitis C patients. J Viral Hepat 2006; 13: 652-8.
Poynard T, Aubert A, Bedossa P, Abella A, Naveau S, Paraf F, Chaput JC. A simple biological index for detection of alcoholic liver disease in drinkers. Gastroenterology 1991 May; 100(5 Pt 1): 1397-402.
Teare JP, Sherman D, Greenfield SM, Simpson J, Bray G, Catterall AP, Murray-Lyon IM, Peters TJ, Williams R, Thompson RP. Comparison of serum procollagen III peptide concentrations and PGA index for assessment of hepatic hepatic fibrosis. Lancet 1993; 342: 895-8.
Oberti F, Valsesia E, Pilette C, et al. Noninvasive diagnosis of hepatic fibrosis or cirrhosis. Gastroenterology 1997; 113: 1609–16.
Kelleher TB, Mehta SH, Bhaskar R, Sulkowski M, Astemborski J, Thomas DL, Moore RE, Afdhal NH. Prediction of hepatic ﬁbrosis in HIV/HCV co-infected patients using serum ﬁbrosis markers: The SHASTA index. Journal of Hepatology 2005; 43: 78–84.
Poynard T, Ratziu V, Naveau S, Thabut D, Charlotte F, Messous D, Capron D, Abella A, Massard J, Ngo Y, et al. The diagnostic value of biomarkers (SteatoTest) for the prediction of liver steatosis. Comp Hepatol 2005; 4: 10.
Lassailly G, Caiazzo R, Hollebecque A, Buob D, Leteurtre E, Arnalsteen L, Louvet A, Pigeyre M, Raverdy V, Six MF, Verkindt H, Eberle C, Patrice A, Dharancy S, Romon M, Pattou F, Mathurin P. Validation of noninvasive biomarkers (FibroTest, SteatoTest, and NashTest) for prediction of liver injury in patients with morbid obesity. Eur J Gastroenterol Hepatol 2011 Jun; 23(6): 499-506.
Angulo P, Hui JM, Marchesini G, Bugianesi E, George J, Farrell GC, et al. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology 2007; 45: 846–54.
Kandemir O, Polat G, Saraçoğlu G, Taşdelen B. The predictive role of AST level, prothrombin time, and platelet count in the detection of liver fibrosis in patients with chronic hepatitis C. Turk J Med Sci 2009; 39 (6): 857-862.
Cross TJS, Rizzi P, Berry PA, Bruce M, Portmann B, Harrison PM. King’s Score: an accurate marker of cirrhosis in chronic hepatitis C. Eur J Gastroen Hepat. 2009; 21(7): 730-8 10.1097/ MEG.0b013e32830dfcb3.
Cross TJS, Calvaruso V, Maimone S, Carey I, Chang TP, Pleguezuelo M, Manousou P, Quaglia A, Grillo F, Dhillon AP, Dusheiko GM, Burroughs AK and Harrison PM. Prospective comparison of Fibroscan, King score and liver biopsy for the assessment of cirrhosis in chronic hepatitis C infection. Journal of Viral Hepatitis 2010; 17: 546–54.
Bota S, Sirli R, Sporea I, Focsa M, Popescu A, Danila M, et al. A new scoring system for prediction of fibrosis in chronic hepatitis C. Hepat Mon 2011; 11(7): 548-55.
Parsian H, Alizadeh M, Negahdar H. Whould the Physicians Eventually Obsolete the Liver Biopsy for the Assessment of Liver Fibrosis? Hepat Mon 2012; 12(5): 353-4. DOI: 10.5812/hepatmon.6227.
Pinzani M, Vizzutti F, Arena U, Marra F. Technology Insight: noninvasive assessment of liver fibrosis by biochemical scores and elastography. Nat Clin Pract Gastroenterol Hepatol. 2008 Feb;5(2):95-106.