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  Table of Contents    
ORIGINAL ARTICLE  
Year : 2013  |  Volume : 56  |  Issue : 3  |  Page : 196-199
Assessment of non-invasive models for liver fibrosis in chronic hepatitis B virus related liver disease patients in resource limited settings


1 Department of Microbiology, R. D. Gardi Medical College, Ujjain, Madhya Pradesh, India
2 Department of Microbiology, Army Hospital (Research and Referral), New Delhi, India
3 Department of Gastroenterology, Base Hospital, Delhi Cantonment, New Delhi, Delhi, India
4 Department of Microbiology, All India Institute of Medical Sciences, Bhubneshwar, Odisha, India
5 Department of Microbiology, Office of DGAFMS, New Delhi, India
6 Department of Pathology, Inlaks and Budhrani Hospital, Pune, India

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Date of Web Publication24-Oct-2013
 

   Abstract 

Context: A total of 350 million individuals are affected by chronic hepatitis B virus infection world-wide. Historically, liver biopsy has been instrumental in adequately assessing patients with chronic liver disease. A number of non-invasive models have been studied world-wide. Aim: The aim of this study is to assess the utility of non-invasive mathematical models of liver fibrosis in chronic hepatitis B (CHB). Indian patients in a resource limited setting using routinely performed non-invasive laboratory investigations. Settings and Design: A cross-sectional study carried out at a tertiary care center. Subjects and Methods: A total of 52 consecutive chronic liver disease patients who underwent percutaneous liver biopsy and 25 healthy controls were enrolled in the study. Routine laboratory investigations included serum aspartate aminotransferase (AST), Alanine aminotransferase (ALT), Gama glutamyl transpeptidase (GGT), total bilirubin, total cholesterol, prothrombin time and platelet count. Three non-invasive models for namely aspartate aminotransferase to platelet ratio index (APRI), Fibrosis 4 (FIB-4) and Forn's index were calculated. Outcomes were compared for the assessment of best predictor of fibrosis by calculating the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of each index. Statistical Analysis Used: Medcalc online software and by Microsoft Excel Worksheet. Chi-square test was used for significance. P value < 0.05 was taken as significant. Results: While the serum levels of AST, ALT and GGT were significantly higher in patients group as compare with the healthy controls (P < 0.01), the platelet counts were significantly lower in patient group as compared to the control group (P < 0.01). Mean value of all 3 indices were significantly higher in patients group as compare with the controls (P < 0.01). Conclusions: Out of the three indices, APRI index with a NPV of 95% appeared to be a better model for excluding significant liver fibrosis while FIB-4 with a PPV of 61% showed fair correlation with significant fibrosis. Thus, these two non-invasive models for predicting of liver fibrosis, namely APRI and FIB-4, can be utilized in combination as screening tools in monitoring of CHB patients, especially in resource limiting settings.

Keywords: Chronic hepatitis B, liver fibrosis, non-invasive model, serum marker

How to cite this article:
Shrivastava R, Sen S, Banerji D, Praharaj AK, Chopra GS, Gill SS. Assessment of non-invasive models for liver fibrosis in chronic hepatitis B virus related liver disease patients in resource limited settings. Indian J Pathol Microbiol 2013;56:196-9

How to cite this URL:
Shrivastava R, Sen S, Banerji D, Praharaj AK, Chopra GS, Gill SS. Assessment of non-invasive models for liver fibrosis in chronic hepatitis B virus related liver disease patients in resource limited settings. Indian J Pathol Microbiol [serial online] 2013 [cited 2020 Sep 23];56:196-9. Available from: http://www.ijpmonline.org/text.asp?2013/56/3/196/120359



   Introduction Top


World-wide 350 million individuals are affected by chronic hepatitis B virus (HBV) infection. At least one million people would die each year. [1] Both prognosis and potential treatment of chronic liver disease greatly depend on the progression of liver fibrosis, which is the ultimate outcome of chronic liver damage. Thus, it is important to prevent the progression of early liver fibrosis to cirrhosis. [2] Historically, liver biopsy has been instrumental in adequately assessing patients with chronic liver disease. However, it has several disadvantages, such as poor patient compliance, small sample size, complications and intra- and inter-observer variations. [3],[4],[5] Therefore, non-invasive markers are urgently needed. [6] A number of non-invasive models containing serum markers, such as serum aspartate aminotransferase (AST) aspartate aminotransferase to platelet ratio index (APRI), Fibrosis 4 (FIB-4), Forn's index, fibrometer, hepascore, shanghai liver fibrosis group's index (SLFG) have been studied world-wide. [7],[8],[9],[10],[11],[12] Multiple diagnostic tests have been developed for the staging of fibrosis using non-invasive methods, most of them in the setting of chronic hepatitis C. The present study was carried out at a tertiary care center to assess the utility of non-invasive mathematical model of liver fibrosis in chronic hepatitis B (CHB) Indian patients in a resource-limited setting using routinely performed non-invasive laboratory investigations.


   Subjects and Methods Top


Study participants

A total of 52 consecutive patients of CHB patients who underwent a liver biopsy at Gastroenterology unit in a tertiary care center were enrolled in the study. Chronic HBV infection was diagnosed based on persistence of hepatitis B surface antigen for more than 6 months. Patients with chronic liver disease owing to other causes, clinically overt cirrhosis, patients on antiviral or interferon therapy and Human immunodeficiency virus positive patients were excluded from the study. Blood samples were collected for the assessment of non-invasive markers 1 day prior to biopsy. In addition, 25 apparently healthy, matched (for age, gender and body mass index [BMI]) controls were enrolled in the study for assessment of non-invasive indices. Written informed consent was obtained from each study participant. The institutional ethical committee approved the study.

Liver biopsy

Liver tissue (1.5-2 cm × 0.5-1 cm) was obtained by percutaneous biopsy by the gastroenterologist and sent to Pathology Department where it was stained with H and E stain. Fibrosis staging was done according to modified Ishak grading system. [13],[14] Significant fibrosis was defined as Grade IV or more by modified Ishak grading. Liver biopsy was performed only in the patients where indicated, and not in healthy controls.

Laboratory investigations and non-invasive markers of liver fibrosis

Non-invasive parameters assayed in our study included AST, alanine aminotransferase (ALT), Gama glutamyl transpeptidase (GGT), Bilirubin, total cholesterol, prothrombin time and platelet count. These markers were evaluated in all patients and healthy controls.

Three non-invasive markers of liver fibrosis, namely APRI, FIB-4 and Forn's index were calculated as per the recommended formulae. [15],[16]

APRI = (AST/ [ULN]/PLT [109/L]) × 100

FIB-4 = (age [year] × AST [U/L]) / {(PLT [109/L]) × (ALT [U/L]) 1/2}

Forn's index = 7.811 - 3.131 × ln (PLT [109/L]) + 0.781 × ln (GGT [U/L]) + 3.467 × ln (age [year]) -0.014× (cholesterol [g/L]).

Statistical Analysis

Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) were calculated by using cut-offs according to the previous studies [Table 1]. [7],[8],[9],[10],[11],[12],[15]
Table 1: Sensitivity, specificity, PPV and NPV for different cut-offs taking stage 3 or more as significant fibrosis (Taking liver biopsy as a gold standard)

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The statistical analysis was performed using the Medcalc online software and by Microsoft Excel Worksheet. [17] P value <0.05 was taken as significant.


   Results Top


Patient Characteristics

The median age of the 52 study CHB patients was 32 years (range 20-60). Out of these 52, 49 patients were males. Median age of the 25 healthy controls was 31 years (range 26-41). Out of these 25, 24 were male. There was no significant difference between age of patients and controls (P > 0.05). The mean BMI of patients was 26.3 (R 22.1-28) and of controls was 26.1 (R 21.5-27.7) and there was no significant difference [Table 2].
Table 2: Characteristics of patients and controls

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As per the modified Ishak staging, out of total 52 patients, significant fibrosis of stage 4 or more was seen in six patients; stage 3 for liver fibrosis in eight patients; and stage 0-2 in 38 patients [Figure 4] and [Figure 5]. The serum levels of AST, ALT and GGT were significantly higher in patients group as compare to healthy controls and Platelet count was significantly lower in the patient group as compared to the control group (P < 0.01) [Table 2]. Mean value of all 3 indices namely APRI, FIB-4 and Forn's were significantly higher in patients group as compared to controls (P < 0.01). The mean value of different parameters and indices for the patient and control groups is given in [Table 2].

The correlation of three indices with liver fibrosis (as indicated by liver biopsy) in CHB patients and control groups is shown in [Figure 1], [Figure 2] and [Figure 3].
Figure 1: Aspartate aminotransferase to platelet ratio index in healthy controls and chronic hepatiti s B patients with fibrosis

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Figure 2: Fibrosis 4 index in healthy controls and chronic hepatitis B patients with fibrosis

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Figure 3: Forn's index in healthy controls and chronic hepatitis B patients with fibrosis

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Figure 4: Masson's trichrome stain showing liver fibrosis stage 5/6 (×10 and ×40)

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Figure 5: H and E stain showing liver fibrosis stage 4/6 (×10)

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Sensitivity, specificity, PPV and NPV for different cut-offs taking ishak stage 3 or more as significant fibrosis (n = 14) and taking Ishak stage 4 or more as significant fibrosis (n = 6) was calculated to find out most appropriate marker of liver fibrosis [Table 1] and [Table 3]. All the indices had a good NPV ranging from 73% to 95% while PPV was ranging from 15% to 61%. Sensitivity was highest for APRI index (78% and 83%) and Specificity was highest for FIB-4 index (97% and 98%). Taking a cut-off value of 0.5 gives maximum sensitivity in APRI index and a cut-off value of 3.25 gives a maximum specificity in FIB-4 index of 98%.
Table 3: Sensitivity, specificity, PPV and NPV for different cut-offs taking stage 4 or more as significantfi brosis (Taking liver biopsy as gold standard)

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   Discussion Top


Non-invasive models have been proposed for the assessment of liver fibrosis in various studies. In this study, we evaluated the performance of APRI, FIB-4 and Forn's index for the assessment of liver fibrosis in CHB patients. These models are mainly based on indirect serum markers, which have no direct link with liver fibrosis, but reflect liver dysfunction or other phenomena caused by fibrosis. We included routinely performed non-invasive markers in this study. The main aim was to identify a model or a combination of models, which can be used as a screening test to rule-out liver fibrosis in a peripheral setup. The best model in our study comes out to be APRI index with sensitivity up to 83% and NPV up to 95%. However, the diagnostic performance of these markers is low with a maximum specificity of 61% for FIB-4 Index.

It has been reported in the previous studies that APRI under the area under the receiver operating curves (AUROC) for significant fibrosis is 0.76 (95% confidence interval: 0.74-0.79). [17],[18] It has been shown that the accuracy of APRI under the AUROC for significant fibrosis is 0.63 and 0.72 in CHB patients. [19],[20] In another study, it was shown that APRI has low diagnostic accuracy of liver fibrosis. [21] FIB-4 has a high diagnostic accuracy of severe fibrosis. [22] Results in our study correlate with these observations. There are certain other models based on direct markers for liver fibrosis, like fibrometer SLFG, SLFG index and hepascore. Nevertheless, markers required to calculate these indices are not performed routinely and may not be available at peripheral laboratories.

Transient elastography (fibroScan) is another non-invasive method to detect the mean liver stiffness for diagnosing fibrosis. However, it is expensive and may be limited in those with narrow intercostal spaces, morbid obesity or significant ascites. [23],[24] These non-invasive models can be used in clinical management of CHB by offering an attractive alternative to liver biopsy.

In our study, since the sample size was small, further large scale studies are needed before these models can be used in clinical practice. In addition, other parameters such as obesity, alcohol consumption, viral infections and antiviral/interferon treatment may affect the non-invasive parameters and can alter the indices, which should be looked into. The results should also be validated against not only histological stage scores, but also against digital image analysis and clinical outcomes.

Non-invasive serologic models such as APRI, FIB-4, Forn's index could be a useful tool for screening of CHB patients at risk for developing liver fibrosis specially in resource constraint settings. However, large scale studies are needed to evaluate the usefulness of these markers and then possibly liver biopsy being invasive procedure could be limited to patients showing greater degree of fibrosis as indicated by non-invasive models.

 
   References Top

1.Lee WM. Hepatitis B virus infection. N Engl J Med 1997;337:1733-45.  Back to cited text no. 1
    
2.Afdhal NH, Nunes D. Evaluation of liver fibrosis: A concise review. Am J Gastroenterol 2004;99:1160-74.  Back to cited text no. 2
    
3.Little AF, Ferris JV, Dodd GD 3 rd , Baron RL. Image-guided percutaneous hepatic biopsy: Effect of ascites on the complication rate. Radiology 1996;199:79-83.  Back to cited text no. 3
    
4.Lindor KD, Bru C, Jorgensen RA, Rakela J, Bordas JM, Gross JB, et al. The role of ultrasonography and automatic-needle biopsy in outpatient percutaneous liver biopsy. Hepatology 1996;23:1079-83.  Back to cited text no. 4
    
5.Cadranel JF, Rufat P, Degos F. Practices of liver biopsy in France: Results of a prospective nationwide survey. For the Group of Epidemiology of the French Association for the Study of the Liver (AFEF). Hepatology 2000;32:477-81.  Back to cited text no. 5
    
6.Friedman SL. Liver fibrosis - From bench to bedside. J Hepatol 2003;38 Suppl 1:S38-53.  Back to cited text no. 6
    
7.Wai CT, Greenson JK, Fontana RJ, Kalbfleisch JD, Marrero JA, Conjeevaram HS, et al. A simple non-invasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 2003;38:518-26.  Back to cited text no. 7
    
8.Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, et al. Development of a simple non-invasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 2006;43:1317-25.  Back to cited text no. 8
    
9.Forns X, Ampurdanès S, Llovet JM, Aponte J, Quintó L, Martínez-Bauer E, et al. Identification of chronic hepatitis C patients without hepatic fibrosis by a simple predictive model. Hepatology 2002;36:986-92.  Back to cited text no. 9
    
10.Calès P, Oberti F, Michalak S, Hubert-Fouchard I, Rousselet MC, Konaté A, et al. A novel panel of blood markers to assess the degree of liver fibrosis. Hepatology 2005;42:1373-81.  Back to cited text no. 10
    
11.Zeng MD, Lu LG, Mao YM, Qiu DK, Li JQ, Wan MB, et al. Prediction of significant fibrosis in HBeAg-positive patients with chronic hepatitis B by a non-invasive model. Hepatology 2005;42:1437-45.  Back to cited text no. 11
    
12.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.  Back to cited text no. 12
    
13.Ishak KG. Chronic hepatitis: Morphology and nomenclature. Mod Pathol 1994;7:690-713.  Back to cited text no. 13
    
14.Ishak K, Baptista A, Bianchi L, Callea F, De Groote J, Gudat F, et al. Histological grading and staging of chronic hepatitis. J Hepatol 1995;22:696-9.  Back to cited text no. 14
    
15.Gressner AM, Gao CF, Gressner OA. Non-invasive biomarkers for monitoring the fibrogenic process in liver: A short survey. World J Gastroenterol 2009;15:2433-40.  Back to cited text no. 15
    
16.Wu SD, Wang JY, Li L. Staging of liver fibrosis in chronic hepatitis B patients with a composite predictive model: A comparative study. World J Gastroenterol 2010;16:501-7.  Back to cited text no. 16
    
17.MedCalc Software Version 11.5.1.0. Available from: http://www.medcalc.org. [Cited on 2011 Feb 23].  Back to cited text no. 17
    
18.Shaheen AA, Myers RP. Diagnostic accuracy of the aspartate aminotransferase-to-platelet ratio index for the prediction of hepatitis C-related fibrosis: A systematic review. Hepatology 2007;46:912-21.  Back to cited text no. 18
    
19.Wai CT, Cheng CL, Wee A, Dan YY, Chan E, Chua W, et al. Non-invasive models for predicting histology in patients with chronic hepatitis B. Liver Int 2006;26:666-72.  Back to cited text no. 19
    
20.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.  Back to cited text no. 20
    
21.Zhang YX, Wu WJ, Zhang YZ, Feng YL, Zhou XX, Pan Q. Noninvasive assessment of liver fibrosis with combined serum aminotransferase/platelet ratio index and hyaluronic acid in patients with chronic hepatitis B. World J Gastroenterol 2008;14:7117-21.  Back to cited text no. 21
    
22.Vallet-Pichard A, Mallet V, Nalpas B, Verkarre V, Nalpas A, Dhalluin-Venier V, et al. FIB-4: An inexpensive and accurate marker of fibrosis in HCV infection. comparison with liver biopsy and fibrotest. Hepatology 2007;46:32-6.  Back to cited text no. 22
    
23.Coco B, Oliveri F, Maina AM, Ciccorossi P, Sacco R, Colombatto P, et al. Transient elastography: A new surrogate marker of liver fibrosis influenced by major changes of transaminases. J Viral Hepat 2007;14:360-9.  Back to cited text no. 23
    
24.Cobbold JF, Morin S, Taylor-Robinson SD. Transient elastography for the assessment of chronic liver disease: Ready for the clinic? World J Gastroenterol 2007;13:4791-7.  Back to cited text no. 24
    

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Correspondence Address:
Sourav Sen
Department of Microbiology, Army hospital (Research and referral), Delhi - 110 010
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0377-4929.120359

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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]

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