A Simple Noninvasive Score Based on Routine Parameters can Predict Liver Cirrhosis in Patients With Chronic Hepatitis C

AUTHORS

Ivan Gentile 1 , * , Nicola Coppola 2 , Giuseppe Pasquale 2 , Raffaele Liuzzi 3 , Maria D’Armiento 4 , Maria Emma Di Lorenzo 1 , Nicolina Capoluongo 2 , Antonio Riccardo Buonomo 1 , Evangelista Sagnelli 2 , Filomena Morisco 1 , Nicola Caporaso 1 , Guglielmo Borgia 1

1 Department of Clinical Medicine and Surgery, University of Naples Federico II, via S. Pansini, Naples, Italy

2 Department of Public Medicine, Section of Infectious Disease, Second University of Naples, via L. Armanni, Naples, Italy

3 Institute of Biostructure and Bioimaging, National Research Council (CNR), Naples, Italy

4 Department of Advanced Biomedical Science, University of Naples Federico II, via S. Pansini, Naples, Italy

How to Cite: Gentile I, Coppola N, Pasquale G, Liuzzi R, D’Armiento M, et al. A Simple Noninvasive Score Based on Routine Parameters can Predict Liver Cirrhosis in Patients With Chronic Hepatitis C, Hepat Mon. 2013 ; 13(5):e8352. doi: 10.5812/hepatmon.8352.

ARTICLE INFORMATION

Hepatitis Monthly: 13 (5); e8352
Published Online: May 8, 2013
Article Type: Research Article
Received: September 27, 2012
Revised: January 14, 2013
Accepted: February 18, 2013
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Abstract

Background: Liver biopsy has remained the gold standard for the diagnosis of chronic hepatitis C; even though, it has a low but non-negligible rate of both false negative and complications. Several authors have proposed noninvasive tools to diagnose cirrhosis. But none of them showed complete concordance with liver biopsy.

Objectives: To devise a score based on noninvasive routine parameters that discriminate between patients with a high risk, and those with a low risk of cirrhosis among patients with chronic hepatitis C without performing liver biopsy, and to compare this score with other ones using routine parameters devoted to this aim.

Patients and Methods: We reviewed the charts of patients with chronic hepatitis C who performed a liver biopsy between 2000 and 2004. Multivariate analysis was used to identify independent predictors of cirrhosis. An independent group of patients with chronic hepatitis C admitted for a liver biopsy between 2007 and 2012 constituted the validation set.

Results: We enrolled 249 patients who had complete laboratoristic data, and sufficient liver tissue for fibrosis staging. Age, AST, prothrombin activity, and platelets were identified as independent predictors of histological cirrhosis. We categorized these variables, and devised a novel score called CISCUN (Cirrhosis Score University of Naples), giving one point to each of the following predictors: age > 40 years; AST > 2 upper normal values; platelet count < 160.000/mmc; prothrombin activity < 100%. Cirrhosis rate was 2.9% for the 103 patients with a CISCUN = 0 or 1, 23.4% for the 124 patients with a CISCUN of 2 or 3, and 86.4% for the 22 patients with a CISCUN = 4. These results were confirmed in the independent validation group of 285 patients with similar characteristics.

Conclusions: Patients with chronic hepatitis C and with a CISCUN ≤ 1 had a very low rate of cirrhosis while those with a CISCUN = 4 had a high risk of cirrhosis. Patients with CISCUN = 2 or 3 had an intermediate rate of cirrhosis, and therefore needed to perform a liver biopsy to receive a reliable diagnosis.

Keywords

Liver Cirrhosis Fibrosis Biopsy Noninvasive Ventilation

Copyright © 2013, Kowsar Corp. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

1. Background

Hepatitis C virus (HCV) is estimated to infect approximately 2.2–3.0% of the world’s population which corresponds to 130–170 million people (1). After penetrating in the host HCV gives rise to an acute infection which becomes chronic in about 70% of infected people (2, 3). About 25% of these patients would develop liver cirrhosis in about 20-30 years (4). About 4% per year of patients with cirrhosis would evolve toward a decompensated disease with an annual death rate between 15% and 30% (2). Finally about 1.6% of patients with cirrhosis develop hepatocellular carcinoma (HCC) yearly (2). Therefore the presence of cirrhosis is a main determinant of poor prognosis in patients with chronic HCV infection. Moreover, in the presence of cirrhosis, ultrasound screening for HCC, and upper endoscopy for varices detection are mandatory (5). Finally, the presence of cirrhosis influences decisions about antiviral treatment. Patients with cirrhosis have the most urgent need for treatment, but also a low response rate, and require a close monitoring during treatment (6). Liver biopsy is still considered the standard method for the assessment of liver cirrhosis, even though sampling errors and interpreter variability may reduce its diagnostic accuracy (7-10). Moreover liver biopsy is an invasive test, and therefore it has a small but not negligible rate of complications (0.3-0.8%), and death (0.01-0.3%) (11-14). Several groups have proposed noninvasive means to diagnose liver cirrhosis (15, 16). The most studied tools include the measurement of liver stiffness (17), and the use of a panel of selected blood tests, and a proprietary algorithm called Fibrotest (18) or a combination of both (19). Moreover several authors have proposed the use of nonroutinely available analytes (20-27) or routinely-available parameters to predict cirrhosis (6, 16, 28-40) or a combination of clinical and laboratoristic parameters (41). The accuracy of the noninvasive tools to predict cirrhosis in comparison to liver biopsy remains to be confirmed. The ideal noninvasive model that is easy-to-perform, accurately discriminate between patients with or without cirrhosis remains to be found.

2. Objectives

The aim of our study is to devise a score based on noninvasive routine parameters which can be helpful for physicians to identify both patients with a high risk and those with a low risk of having cirrhosis among a population of patients with chronic hepatitis C without performing liver biopsy (therefore that can safely avoid it), and to compare our score with other ones using routine parameters.

3. Patients and Methods

We reviewed the charts of all patients with chronic hepatitis C admitted for a liver biopsy to the Department of Public Medicine and Social Security – Section of Infectious Diseases (University of Naples “Federico II”, Italy), and to Department of Public Medicine, section of Infectious Disease (Second University of Naples) between 01.01.2000 to 31.12.2004. Liver biopsy was sampled through an 18G needle under ultrasound guidance. The liver specimens, in each case more than 2 cm in length, were fixed in 10% neutral buffered formalin, embedded in paraffin, and stained with hematoxylin and eosin, and with the Masson’s trichrome method. A sample of liver biopsies was examined by two pathologists who were unaware of the virological and clinical data, and who had a k index of 85.5%. Fibrosis was assessed according to the Ishak scoring system (42). Patients with stage 5 or 6 were considered having histological cirrhosis. Inclusion criteria were positivity for anti-HCV and HCV RNA in serum, no contraindication to liver biopsy. All the biopsies were performed at least 12 months after the completion of an eventual course of interferon therapy. Exclusion criteria were presence of other causes of liver disease; HBV or HIV coinfection, hepatocellular carcinoma, ascites, prior liver transplantation, insufficient liver tissue for staging of fibrosis, and incomplete data on blood counts or liver panel. Patients with a diagnosis of cirrhosis based on laboratory or ultrasound evaluation were excluded as well. Except for HCV genotype, only laboratory results performed within 1 month from the date of the liver biopsy were used. We recorded variables listed in Table 1. We also calculated the AST/ALT ratio. Splenomegaly was diagnosed when spleen length exceeded 12 cm at ultrasound examination (43). Alcohol consumption was defined as drinking more than 30 g per day of alcohol for at least 6 months. This was corroborated by the patient’s family. After the construction of the score, we validated it in an independent group of patients admitted for a liver biopsy to our department between 01.01.2007 and 30.06.2012. Inclusion and exclusion criteria were similar to those of the first group of patients.

Table 1. Demographic, Laboratory, Ultrasound, and Histological Features of the First Group of Patients (n = 249)
ItemsData
Age, y, No. (Median)48 (35-54.5)
Sex
Male, %61.8
Female, %38.2
Alcohol consumption, %12
Pervious Antiviral treatment, %13.2
Splenomegaly at ultrasound, %23
Iron, μg/mL, No. (Median)118 (85.5-148.5)
Glucose, mg/dL, No. (Median)90 (80.25-101)
Total Bilirubin, mg/dL, No. (Median)0.66 (0.50-0.87)
ASTa, U/L, No. (Median)62 (40.5-93)
ALTa, U/L, No. (Median)106 (59-176)
Alkaline phosphatase,U/L, No. (Median)149 (103.5-208)
Albumin, g/dL, No. (Median)4.4 (4.2-4.6)
Cholinesterase,U/L, No. (Median)10,754 (8,334-12613)
White blood cells,cells/ μL, No. (Median)6,400 (5,370-7700)
Red blood cells,cells/ μL, No. (Median)4,870,000 (4,625,000-5,190,000)
Platelets, elements/μL, No. (Median)193,000 (159,000-234,000)
Hemoglobin,g/dL, No. (Median)15.1 (14.2-15.9)
Prothrombin activity, %99 (90-107)
Alpha-fetoprotein,pg/mL, No. (Median)4 (2.3-8.1)
Ferritin,ng/mL, No. (Median)162 (80-265.75)
HCVaRNA, UI/mL, No. (Median)515,000 (200,000 – 850,000)
Cirrhosis at liver biopsy, %20.5
Staging at liver biopsyb, %
01.6
134.5
221.7
312.4
49.2
57.2
613.3

aAbbreviations: ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; HCV, hepatitis C virus

bAccording to Ishak classification

3.1. Statistical Analysis

The Kolmogorov-Smirnov test was used to check quantitative variables for Gaussian distribution. In case of Gaussian distribution, data were reported as mean ± standard deviation (SD), while in case of non-Gaussian distribution they were reported as median and interquartile range (IQR). In case of Gaussian distribution, the Student’s t-test for unpaired variables was applied, while the Mann-Whitney U was used in case of non-Gaussian distribution. The chi-square test with Yates correction (or Fisher’s exact test where appropriate) was used for categorical variables. A P < 0.05 at two-sided test was considered statistically significant. Any independent variable statistically different in the two groups or with a P < = 0.2 at univariate analysis was included in binary logistic regression analysis using the forward conditional stepwise method. The cut-off values used for the stepwise method were: P = 0.05 for entry into the model, and P = 0.10 for its removal. We constructed a receiver operator characteristic (ROC) curve for each continuous variable. The curve shows the capacity of the variable to discriminate between patients with cirrhosis and those without it. The larger the area under the curve (AUC), the better the discriminating ability. An AUC above 0.7 is considered useful, and an AUC above 0.8 indicates excellent accuracy (44). All continuous variables were categorized to maximize easiness of use in clinical practice. Cutoffs were constructed by the means of ROC curve analysis based on the value that maximized the sum of sensitivity and specificity. AUCs of different ROC curves were compared according to the method of DeLong (45). We calculated specificity, sensitivity, positive predictive value, negative predictive value, diagnostic accuracy, positive likelihood ratio, negative likelihood ratio for our score, and compared these values with other scores available to the aim of predicting cirrhosis by noninvasive means. All statistical analyses were performed using the Statistical Package for the Social Sciences version 18.0 (SPSS Inc. Chicago, Ill).

4. Results

We enrolled 281 patients. We excluded 17 patients for insufficient liver tissue for staging of fibrosis, and 16 for incomplete biochemical or blood count data (one of them had also insufficient liver tissue). Therefore all analysis was performed on the remaining 249 patients. The main features of the 249 patients are shown in Table 1. In 51/249 patients (20.5%) histologic evaluation showed liver cirrhosis. Table 2 shows the demographic, laboratory, and ultrasound features of the 249 patients stratified according to the presence/absence of cirrhosis, and the results of univariate analysis. We put variables associated with presence of cirrhosis or with a P ≤ 0.2 at univariate analysis in a binary logistic regression analysis to identify independent predictors of cirrhosis. As shown in Table 3 higher age and AST, lower prothrombin activity and platelets were independent predictors of histological cirrhosis. We categorized these variables according to the ROC curves (see method section). The cutoff points generated were: age > 40 years; AST >2 upper normal values; platelet count < 160.000/mmc; prothrombin activity < 100%. We devised a score based on these predictors assigning 1 point per predictor. We called this novel score CISCUN (Cirrhosis Score University of Naples). Cirrhosis rate was 0% in the 28 patients with CISCUN = 0; 4% in the 75 patients with CISCUN = 1; 17.1% in the 76 patients with CISCUN = 2; 33.3% in the 48 patients with CISCUN = 3; and 86.4% in the 22 patients with CISCUN = 4 (see Figure 1). Therefore, as patients with CISCUN 0 or 1 had similar very low rate of cirrhosis, and those with CISCUN = 2 or 3 had similar intermediate risk compared to those with CISCUN = 4 which had a very different and high risk of cirrhosis, we decided to merge patients with CISCUN = 0 and 1, and consider them as patients at low risk, and to merge patients with CISCUN = 2 or 3 and consider them as patients in the “grey” area. Consequently, patients with a CISCUN of 0 or 1 had a very low risk of cirrhosis (2.9%) while patients with a score = 4 had a very high risk of cirrhosis (86.4%). Patients with a CISCUN of 2 or 3 showed an intermediate risk of cirrhosis (23.4%). We calculated the sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), diagnostic accuracy (DA), positive likelihood ratio (PLR), negative likelihood ratio (NLR) of the CISCUN using a cutoff of 1 (0-1 vs. 2-4), and using a cutoff of 3 (0-3 vs.4), and compared these with other scores predicting cirrhosis (see Table 4). We calculated AUC of the CISCUN and compared this value with other scores in the ability to predict cirrhosis (see Table 5). AUC of CISCUN was significantly higher than that of AST/AST ratio and Lok score. A trend toward higher AUC was observed for CISCUN compared to APRI and GUCI. In contrast AUC for CISCUN was not different from that of King’s college score. We validated CISCUN ability to discriminate between patients with and without cirrhosis in the independent set of patients with chronic hepatitis C. This group was composed of 285 patients admitted for a liver biopsy to our Departments between 01.01.2007 and 30.06.2012. Median age was 50 years (IQR: 39-58); median AST level was 52 U/L (IQR: 40-80); median Prothrombin activity was 101% (IQR: 94-108); median PLT levels was 217,000/µL (IQR: 178,000 – 279,500). Male represented 57.2% of subjects. Cirrhosis was identified in 27 (9.5%) cases. In these patients cirrhosis rate was 2.6% in the 39 patients with CISCUN = 0; 0.9% in the 117 patients with CISCUN = 1; 2.7% in the 73 patients with CISCUN = 2; 21.1% in the 38 patients with CISCUN = 3; and 83.3% in the 18 patients with CISCUN = 4. Therefore, even in this group it is confirmed that subjects with a CISCUN = 0 or 1 had a very low risk of cirrhosis (1.28%) while patients with a score = 4 had a very high risk of cirrhosis (83.3%). Patients with a CISCUN of 2 or 3 showed a non-negligible risk of cirrhosis (9%). Table 6 shows sensitivity, specificity, PPV, NPV, DA, PLR, and NLR of the CISCUN in the validation set.

Table 2. Demographic, Laboratory, Ultrasound Features of the First Group of Patients Stratified by Presence or Absence of Histological Cirrhosis (n = 249)b
Patients without cirrhosis (n = 198)Patients with cirrhosis (n = 51)P value
Age, y, No (Median)46 (33-54)52 (45-56)< 0.001
Sex, %0.037
Male58.674.5
Female41.425.5
Alcohol consumption, %13.417.50.500
Pervious Antiviral treatment, %11.619.60.133
Splenomegaly at ultrasound, %2236.20.045
Iron, µg/mL114 (83-143)151 (112-183)< 0.001
Glucose, mg/dL88 (79-99)93 (86-109)0.002
Total Bilirubin, mg/dL0.61 (0.48-0.80)0.73 (0.59-1.04)0.002
ASTa, U/L56 (39-83)94 (64-119)< 0.001
ALTa, U/L93 (54-159)139 (83-198)0.001
Alkaline phosphatase, U/L147 (95-205)219.5 (130-296)0.001
Albumin, g/dL4.4 (4.2-4.6)4.3 (4.1-4.5)0.020
Cholinesterase,U/L11,062 (8,799-12,996)8,949 (6,892-11,270)0.001
White blood cells,cells/ μL6,450 (5,300-7,800)6,400 (5,500-7,300)0.541
Red blood cells,cells/ μL4,860,000 (4,630,000-5,190,000)4,910,000 (4,570,000-5,190,000)0.765
Platelets, elements/μL198,500 (171,750-245,000)149,500 (118,000-188,000)< 0.001
Hemoglobin,g/dL15.0 (14.1-15.9)15.4 (14.7-16.1)0.044
Prothrombin activity,%101 (93-108)92 (86-99)< 0.001
Alpha-fetoprotein,pg/mL3.3 (2.2-5.8)8.1 (3.9-17.2)< 0.001
Ferritin, ng/mL137 (61-234)195 (130-445)0.037
HCVaRNA, UI/mL511,000 (200,000–850,000)698,000 (126,500–980,000)0.541

aAbbreviations: ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; HCV, hepatitis C virus

bData were compared using χ 2 test for categorical variables and Mann-Whitney U test for quantitative variables

Table 3. Independent Predictors of Cirrhosis at Logistic Regression Analysis in the First Group of Patients (n = 249)
Regression coefficientStandard ErrorOdds Ratio, (95% CIa)P value
Age, y0.0590.0191.061 (1.021-1.102)0.002
ASTa, U/L0.040.021.004 (1.001-1.008)0.046
Prothrombin activity,%-0.530.0170.949 (0.919-0.980)0.002
Platelets,103elements/μL-0.0140.0040.986 (0.978-0.994)0.001

aAbbreviation: AST, Aspartate Aminotransferase; CI, Confidence Interval

Table 4. Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Diagnostic Accuracy, Positive Likelihood Ratio, Negative Likelihood Ratio for CISCUN, and For Other Noninvasive Scores Predicting Cirrhosis in the First Group of Patients (n = 249)
LevelAll Patients, No.Patients. with cirrhosis, No. (%)SEa, %SPa, %PPVa, %NPVa, %DAa, %PLRaNLRa
CISCUN0-11033 (2.9)94.150.532.997.159.41.900.12
2-414648 (32.9)
0-322732 (14.1)37.398.586.485.985.924.590.64
42219 (86.4)
Lok score (6)≤ 0.2 996 (6.1)88.2473093.955.41.660.25
>0.215045 (30)
≤ 0.522236 (16.2)29.493.955.683.880.74.850.75
> 0.52715 (55.6)
AST/ALTaratio (39)< 122746 (20.3)9.891.422.779.774.71.140.99
≥ 1225 (22.7)
GUCI(33)≤ 11297 (5.4)86.361.636.794.666.72.250.22
> 112044 (36.7)
King’s college (29)< 16.61359 (6.7)82.463.636.893.367.52.260.28
≥ 16.611442 (36.8)
APRIa (40)≤ 115112 (7.9)76.570.239.892.171.52.570.34
> 19839 (39.8)
≤ 221736 (16.6)29.491.446.983.478.73.430.77
> 23215 (46.9)
PLTa (35)≥ 150.000/mmc20325 (12.3)5189.956.587.781.95.050.55
< 150.000/mmc4626 (56.5)

aAbbreviations: SE, Sensitivity; SP, Specificity; PPV, Positive Predictive Value; NPV, Negative Predictive Value; DA, Diagnostic Accuracy; PLR, Positive Likelihood Ratio; NLR, Negative Likelihood Ratio; AST, Aspartate Aminotransferase; ALT, Alanine Aminotransferase; GUCI, Goteborg University Cirrhosis Index; APRI, AST to platelet ratio index; PLT, Platelet Count

Table 5. Comparison of CISCUN With Other Cirrhosis Scores
AUCaStandard Error95% CIaP valueb
CISCUN0.8420.03000.790 to 0.885
APRIa0.7930.03320.737 to 0.8420.0604
AST/ALT ratioa0.6000.04050.536 to 0.662< 0.0001
GUCIa0.7990.03280.743 to 0.8470.0800
King 0.8130.03150.758 to 0.8600.1496
Lok0.7810.03680.725 to 0.8310.0493

aAbbreviations: AUC, Area Under the Curve; CI, Confidence Interval; APRI, AST to platelet ratio index; AST, Aspartate Aminotransferase; ALT, Alanine Aminotransferase; GUCI, Goteborg University Cirrhosis Index

bComparison with CISCUN

Table 6. Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Diagnostic accuracy, Positive Likelihood Ratio, Negative Likelihood Ratio for CISCUN in the Validation Set (n = 281)
LevelAll patients, No.Patients with cirrhosis, No. (%)SEa, %SPa, %PPVa, %NPVa, %DAa, %PLRaNLRa
CISCUN0-11562 (1.3)92.659.719.498.762.82.300.12
2-412925 (19.4)
0-326712 (4.5)55.698.883.395.594.747.780.45
41815 (83.3)

aAbbreviations: SE: Sensitivity; SP: Specificity; PPV, Positive Predictive Value; NPV, Negative Predictive Value; DA; Diagnostic Accuracy; PLR, Positive Likelihood Ratio; NLR, Negative Likelihood Ratio

Rate of Cirrhosis in Patients With CISCUN 0-4 in the First Group of Patients (n = 249)
Figure 1. Rate of Cirrhosis in Patients With CISCUN 0-4 in the First Group of Patients (n = 249)

5. Discussion

In our study we identified 4 predictors of cirrhosis in a population of patients with chronic hepatitis C. These predictors were categorized to maximize the easiness of use in clinical practice. Patients with none or one of these predictors (CISCUN = 0 or 1) presented a very low risk of cirrhosis, while patients with all predictors (CISCUN = 4) had a high risk of cirrhosis. The patients in the “grey” zone (CISCUN 2 or 3) presented an intermediate risk of cirrhosis. As about a half of patients present a SCORE of 0, 1 or 4, liver biopsy may be avoided in about a half of patients with a low risk of misclassifying patients. We underline that the patients misclassified are 4.8% of patients with a score of 0, 1 or 4 and this Figure 2 is very low considering that liver biopsy itself has a rate of misclassification that reaches 11-20% in same studies (8, 46). These results were confirmed in an independent validation set of patients with HCV-related chronic liver disease. In this latter group more than 60% of liver biopsy (patients with CISCUN = 0, 1, or 4) may be avoided with an even lower risk of misclassification (2.9%). This is particularly interesting because the validation set showed a reduced rate of cirrhosis compared to the first group of patients. The diagnosis of cirrhosis remains a key element from both prognostic and therapeutic points of view. For example, the recent availability of protease inhibitors–containing combinations for patients infected with genotype 1, poses a series of problems. Costs and side effects of these drugs may represent a limitation to their use, considering that other and better tolerated drugs are in an advanced phase of clinical development. Decisions regarding immediate treatment or “wait and see” strategy cannot disregard a precise staging of the disease. The presence of cirrhosis is certainly a factor inducing an immediate treatment with the best options available, and liver biopsy still represents the gold standard to diagnose liver cirrhosis. However, liver biopsy is an invasive test associated with both complications and costs (11-14). Therefore a score able to diagnose cirrhosis by noninvasive means can be useful and cost-effective for the clinicians. Our score has several advantages over other ones devised for the same aim:

ROC Curves for the Scores Predicting Cirrhosis
Figure 2. ROC Curves for the Scores Predicting Cirrhosis

1) It includes parameters that are routinely available and unbiased in contrast with other studies that include analyses that are difficult-to-perform in clinical practice (20-27) or include subjective variables (41). Moreover the cost of the determination of our parameters is very low.

2) There is no need for mathematic formulae as in some other studies (6, 28-30, 32-34, 40). This represents a clear advantage for easiness which is a key element to make the score potentially widely used at the bedside

3) Our score showed a very high diagnostic performance in our cohort group of patients. In fact NPV (for CISCUN ≤ 1), and PPV (for CISCUN = 4) yielded the highest values among other scores devoted to cirrhosis prediction. It is noteworthy that the rate of false negative of CISCUN is very low (< 3%). This is a relevant point for a screening test.

Other approaches used to diagnose cirrhosis by noninvasive means include the measurement of liver stiffness by Fibroscan (17), and the employment of a proprietary algorithm on a panel of selected blood tests called Fibrotest (18). Both are useful predictors of cirrhosis. However, they are not universally available nor cost-saving tools compared to scores that use routine parameters. We underline that CISCUN includes only biologically plausible variables. In fact, age is associated with progression to cirrhosis (47). A low platelet count is associated with liver cirrhosis because of splenic sequestration due to portal hypertension. A reduced hepatic thrombopoietin production has also been claimed as a factor causing platelet decrease in patients with cirrhosis (48). It is noteworthy that platelet levels are included in almost all noninvasive scores predicting the presence of cirrhosis or esophageal varices (16, 49). AST levels are associated with liver cirrhosis as well. It is thought that this occurs because of a greater mitochondrial damage (and therefore to enhanced AST release) occurs in patients with cirrhosis. In our study AST/ALT ratio was not found to be associated with cirrhosis. Lastly prothrombin activity is directly related to hepatic synthetic function, and therefore worsens with progression of fibrosis and loss of hepatocytes (6). A potential limitation of our study (like all studies devoted to noninvasive prediction of cirrhosis) is that liver biopsy can provide additional information for physician such as quantify the degree of fibrosis, and show signs of overlapping conditions (e.g. NASH or iron overload).

In conclusion, we devised a novel score called CISCUN which is based on four predictors (Age > 40 years; AST > 2 upper normal values; platelet count < 160.000/mmc; Prothrombin activity < 100%), and assigns 1 point per predictor. CISCUN can identify patients with a very low rate of cirrhosis (CISCUN = 0 or 1), and patients with a high risk of having cirrhosis (CISCUN = 4) among subjects with chronic hepatitis C. Patients with a CISCUN = 2 or 3 (grey zone) present an intermediate risk of cirrhosis, and therefore should perform a liver biopsy to receive a reliable diagnosis.

Acknowledgements

Footnotes

References

  • 1.

    Lavanchy D. The global burden of hepatitis C. Liver Int. 2009; 29 Suppl 1 : 74 -81 [DOI][PubMed]

  • 2.

    Global burden of disease (GBD) for hepatitis C. J Clin Pharmacol. 2004; 44(1) : 20 -9 [DOI][PubMed]

  • 3.

    Coppola N, Pisapia R, Tonziello G, Masiello A, Martini S, Pisaturo M, et al. Improvement in the aetiological diagnosis of acute hepatitis C: a diagnostic protocol based on the anti-HCV-IgM titre and IgG Avidity Index. J Clin Virol. 2009; 46(3) : 222 -9 [DOI][PubMed]

  • 4.

    Hoofnagle JH. Hepatitis C: the clinical spectrum of disease. Hepatology. 1997; 26(3 Suppl 1) : 15S -20S [DOI][PubMed]

  • 5.

    Garcia-Tsao G, Sanyal AJ, Grace ND, Carey W. Prevention and management of gastroesophageal varices and variceal hemorrhage in cirrhosis. Hepatology. 2007; 46(3) : 922 -38 [DOI][PubMed]

  • 6.

    Lok AS, Ghany MG, Goodman ZD, Wright EC, Everson GT, Sterling RK, et al. Predicting cirrhosis in patients with hepatitis C based on standard laboratory tests: results of the HALT-C cohort. Hepatology. 2005; 42(2) : 282 -92 [DOI][PubMed]

  • 7.

    Bedossa P, Dargere D, Paradis V. Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology. 2003; 38(6) : 1449 -57 [DOI][PubMed]

  • 8.

    Pagliaro L, Rinaldi F, Craxi A, Di Piazza S, Filippazzo G, Gatto G, et al. Percutaneous blind biopsy versus laparoscopy with guided biopsy in diagnosis of cirrhosis. A prospective, randomized trial. Dig Dis Sci. 1983; 28(1) : 39 -43 [PubMed]

  • 9.

    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(10) : 2614 -8 [DOI][PubMed]

  • 10.

    Siddique I, El-Naga HA, Madda JP, Memon A, Hasan F. Sampling variability on percutaneous liver biopsy in patients with chronic hepatitis C virus infection. Scand J Gastroenterol. 2003; 38(4) : 427 -32 [PubMed]

  • 11.

    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(3) : 477 -81 [DOI][PubMed]

  • 12.

    Janes CH, Lindor KD. Outcome of patients hospitalized for complications after outpatient liver biopsy. Ann Intern Med. 1993; 118(2) : 96 -8 [PubMed]

  • 13.

    Perrault J, McGill DB, Ott BJ, Taylor WF. Liver biopsy: complications in 1000 inpatients and outpatients. Gastroenterology. 1978; 74(1) : 103 -6 [PubMed]

  • 14.

    Sagnelli E, Sagnelli C, Pisaturo MA, Coppola N, Pasquale G, Piccinino F. Liver biopsy in chronic hepatitis C: the experience of 15 Italian wards of infectious diseases. Infez Med. 2012; 20(1) : 31 -6 [PubMed]

  • 15.

    Poynard T, Morra R, Ingiliz P, Imbert-Bismut F, Thabut D, Messous D, et al. Assessment of liver fibrosis: noninvasive means. Saudi J Gastroenterol. 2008; 14(4) : 163 -73 [DOI][PubMed]

  • 16.

    Rockey DC, Bissell DM. Noninvasive measures of liver fibrosis. Hepatology. 2006; 43(2 Suppl 1) -20 [DOI][PubMed]

  • 17.

    Ziol M, Handra-Luca A, Kettaneh A, Christidis C, Mal F, Kazemi F, et al. Noninvasive assessment of liver fibrosis by measurement of stiffness in patients with chronic hepatitis C. Hepatology. 2005; 41(1) : 48 -54 [DOI][PubMed]

  • 18.

    Imbert-Bismut F, Ratziu V, Pieroni L, Charlotte F, Benhamou Y, Poynard T. Biochemical markers of liver fibrosis in patients with hepatitis C virus infection: a prospective study. Lancet. 2001; 357(9262) : 1069 -75 [DOI][PubMed]

  • 19.

    Patel K, Friedrich-Rust M, Lurie Y, Grigorescu M, Stanciu C, Lee CM, et al. FibroSURE and FibroScan in relation to treatment response in chronic hepatitis C virus. World J Gastroenterol. 2011; 17(41) : 4581 -9 [DOI][PubMed]

  • 20.

    Berres ML, Schlosser B, Berg T, Trautwein C, Wasmuth HE. Soluble urokinase plasminogen activator receptor is associated with progressive liver fibrosis in hepatitis C infection. J Clin Gastroenterol. 2012; 46(4) : 334 -8 [DOI][PubMed]

  • 21.

    Fortunato G, Castaldo G, Oriani G, Cerini R, Intrieri M, Molinaro E, et al. Multivariate discriminant function based on six biochemical markers in blood can predict the cirrhotic evolution of chronic hepatitis. Clin Chem. 2001; 47(9) : 1696 -700 [PubMed]

  • 22.

    Ho AS, Cheng CC, Lee SC, Liu ML, Lee JY, Wang WM, et al. Novel biomarkers predict liver fibrosis in hepatitis C patients: alpha 2 macroglobulin, vitamin D binding protein and apolipoprotein AI. J Biomed Sci. 2010; 17 : 58 [DOI][PubMed]

  • 23.

    Kovalenko E, Tacke F, Gressner OA, Zimmermann HW, Lahme B, Janetzko A, et al. Validation of connective tissue growth factor (CTGF/CCN2) and its gene polymorphisms as noninvasive biomarkers for the assessment of liver fibrosis. J Viral Hepat. 2009; 16(9) : 612 -20 [DOI][PubMed]

  • 24.

    Mehta P, Ploutz-Snyder R, Nandi J, Rawlins SR, Sanderson SO, Levine RA. Diagnostic accuracy of serum hyaluronic acid, FIBROSpect II, and YKL-40 for discriminating fibrosis stages in chronic hepatitis C. Am J Gastroenterol. 2008; 103(4) : 928 -36 [DOI][PubMed]

  • 25.

    Saitou Y, Shiraki K, Yamanaka Y, Yamaguchi Y, Kawakita T, Yamamoto N, et al. Noninvasive estimation of liver fibrosis and response to interferon therapy by a serum fibrogenesis marker, YKL-40, in patients with HCV-associated liver disease. World J Gastroenterol. 2005; 11(4) : 476 -81 [PubMed]

  • 26.

    Selim HS, El-Barrawy MA, Taha HA, Abd El-Hafiz DA. Evaluation of Interleukin-18 as a Non Invasive Marker of Liver Fibrosis among Chronic Hepatitis C Virus Patients. J Egypt Public Health Assoc. 2009; 84(5-6) : 391 -403 [PubMed]

  • 27.

    Valva P, Casciato P, Diaz Carrasco JM, Gadano A, Galdame O, Galoppo MC, et al. The role of serum biomarkers in predicting fibrosis progression in pediatric and adult hepatitis C virus chronic infection. PLoS One. 2011; 6(8)[DOI][PubMed]

  • 28.

    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(8) : 1302 -4 [PubMed]

  • 29.

    Cross TJ, Rizzi P, Berry PA, Bruce M, Portmann B, Harrison PM. King's Score: an accurate marker of cirrhosis in chronic hepatitis C. Eur J Gastroenterol Hepatol. 2009; 21(7) : 730 -8 [DOI][PubMed]

  • 30.

    Forns X, Ampurdanes S, Llovet JM, Aponte J, Quinto L, Martinez-Bauer E, et al. Identification of chronic hepatitis C patients without hepatic fibrosis by a simple predictive model. Hepatology. 2002; 36(4 Pt 1) : 986 -92 [DOI][PubMed]

  • 31.

    Giannini E, Risso D, Botta F, Chiarbonello B, Fasoli A, Malfatti F, et al. Validity and clinical utility of the aspartate aminotransferase-alanine aminotransferase ratio in assessing disease severity and prognosis in patients with hepatitis C virus-related chronic liver disease. Arch Intern Med. 2003; 163(2) : 218 -24 [PubMed]

  • 32.

    Imperiale TF, Said AT, Cummings OW, Born LJ. Need for validation of clinical decision aids: use of the AST/ALT ratio in predicting cirrhosis in chronic hepatitis C. Am J Gastroenterol. 2000; 95(9) : 2328 -32 [DOI][PubMed]

  • 33.

    Islam S, Antonsson L, Westin J, Lagging M. Cirrhosis in hepatitis C virus-infected patients can be excluded using an index of standard biochemical serum markers. Scand J Gastroenterol. 2005; 40(7) : 867 -72 [DOI][PubMed]

  • 34.

    Kim BK, Kim do Y, Park JY, Ahn SH, Chon CY, Kim JK, et al. Validation of FIB-4 and comparison with other simple noninvasive indices for predicting liver fibrosis and cirrhosis in hepatitis B virus-infected patients. Liver Int. 2010; 30(4) : 546 -53 [DOI][PubMed]

  • 35.

    Lackner C, Struber G, Liegl B, Leibl S, Ofner P, Bankuti C, et al. Comparison and validation of simple noninvasive tests for prediction of fibrosis in chronic hepatitis C. Hepatology. 2005; 41(6) : 1376 -82 [DOI][PubMed]

  • 36.

    Luo JC, Hwang SJ, Chang FY, Chu CW, Lai CR, Wang YJ, et al. Simple blood tests can predict compensated liver cirrhosis in patients with chronic hepatitis C. Hepatogastroenterology. 2002; 49(44) : 478 -81 [PubMed]

  • 37.

    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; 15(4) : 386 -90 [PubMed]

  • 38.

    Pohl A, Behling C, Oliver D, Kilani M, Monson P, Hassanein T. Serum aminotransferase levels and platelet counts as predictors of degree of fibrosis in chronic hepatitis C virus infection. Am J Gastroenterol. 2001; 96(11) : 3142 -6 [DOI][PubMed]

  • 39.

    Sheth SG, Flamm SL, Gordon FD, Chopra S. AST/ALT ratio predicts cirrhosis in patients with chronic hepatitis C virus infection. Am J Gastroenterol. 1998; 93(1) : 44 -8 [DOI][PubMed]

  • 40.

    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(2) : 518 -26 [DOI][PubMed]

  • 41.

    Kaul V, Friedenberg FK, Braitman LE, Anis U, Zaeri N, Fazili J, et al. Development and validation of a model to diagnose cirrhosis in patients with hepatitis C. Am J Gastroenterol. 2002; 97(10) : 2623 -8 [DOI][PubMed]

  • 42.

    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(6) : 696 -9 [PubMed]

  • 43.

    Frank K, Linhart P, Kortsik C, Wohlenberg H. [Sonographic determination of spleen size: normal dimensions in adults with a healthy spleen]. Ultraschall Med. 1986; 7(3) : 134 -7 [DOI][PubMed]

  • 44.

    Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982; 143(1) : 29 -36 [PubMed]

  • 45.

    DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988; 44(3) : 837 -45 [PubMed]

  • 46.

    Gentile I, Viola C, Graf M, Liuzzi R, Quarto M, Cerini R, et al. A simple noninvasive score predicts gastroesophageal varices in patients with chronic viral hepatitis. J Clin Gastroenterol. 2009; 43(1) : 81 -7 [DOI][PubMed]

  • 47.

    Pradat P, Voirin N, Tillmann HL, Chevallier M, Trepo C. Progression to cirrhosis in hepatitis C patients: an age-dependent process. Liver Int. 2007; 27(3) : 335 -9 [DOI][PubMed]

  • 48.

    Peck-Radosavljevic M, Zacherl J, Meng YG, Pidlich J, Lipinski E, Langle F, et al. Is inadequate thrombopoietin production a major cause of thrombocytopenia in cirrhosis of the liver? J Hepatol. 1997; 27(1) : 127 -31 [PubMed]

  • 49.

    Gentile I, Thabut D. Noninvasive prediction of oesophageal varices: as simple as blood count? Liver Int. 2010; 30(8) : 1091 -3 [DOI][PubMed]

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