Copyright
by
Rajasekhara Mummadi	
2008

The Dissertation Committee for Rajasekhara Mummadi Certifies that this is the approved version of the following dissertation 

Role of simple biomarkers in predicting fibrosis progression in HCV infection

Committee:

	
Ned Snyder  MD, Supervisor	
Don Powell  MD , Co-Supervisor	
Gagan Sood  MD , , Co-Supervisor	

__________________
Dean, Graduate School

 Role of simple biomarkers in predicting fibrosis progression in HCV infection

by
Rajasekhara Mummadi

Dissertation
Presented to the Faculty of the Graduate School of 
The University of Texas Medical Branch
in Partial Fulfillment 
of the Requirements
for the Degree of 

Masters of Science, Clinical Sciences

The University of Texas Medical Branch
November 2008 

Dedication

To my parents whose sacrifices helped me reach where I am today. To Swapna for enduring the infinite boredom I inflict up on her in the name of academic activity. To Vaishu, the apple of my eye 

Acknowledgements

I would like to thank Dr.Snyder for his invaluable mentorship throughout the   graduate program. I would like to thank Dr.Sood for   being there when I always needed him. I would like to thank Dr.Powell for agreeing to review my thesis in spite of being extremely busy with administrative responsibilities.

I would like to express my gratitude to all my three thesis committee members for taking the time to review my thesis during the distressing times post Hurricane Ike.

 Role of simple biomarkers in predicting fibrosis progression in HCV infection

Publication No._____________

Rajasekhara Mummadi   M.D., Masters of Science (M.S)
The University of Texas Medical Branch, 2008

Supervisor:  Ned Snyder  

ABSTRACT

Hepatic fibrosis biomarkers have been found to be variably accurate in the estimation of fibrosis in chronic Hepatitis C hepatitis (HCV). There is little information whether they are useful longitudinal predictors of changes of fibrosis stage; i.e., progression of fibrosis to cirrhosis. The aim of the study is to examine the accuracy of biomarker panels AST to Platelet Ration Index (APRI ) and FIB-4 in predicting changes in liver histology in patients with chronic HCV.The study is a retrospective cohort study. Data was extracted from a HCV data base that contained clinical and lab information about patients presenting for HCV evaluation to a tertiary academic health-care center (University of Texas Medical Branch at Galveston- UTMB) from 1999-2007. 

The data base was screened for patients with repeat liver biopsies separated at least by a year.  Patients  having a sustained viral response (SVR) with treatment, alcohol intake of >15 grams/day ,hepatitis B infection (HBV), metabolic liver disease,, hepatocellular carcinoma (HCC), human inmunodeficieny virus infection (HIV), and stage 4 liver disease on the first biopsy were excluded. All biopsies were read by a single pathologist using Batts Ludwig criteria (F0-F4). Results: A total of 36 patients met the inclusion criteria. The median age at the time of initial biopsies was 47 (Range 25-68).  The median interval between biopsies was 4 yrs (IQR 3-5 yrs).The majority of the patients were Stage 1 on initial biopsy (50%) followed by Stage 2( 25%) and Stage 3 (22%).One patient had Stage 0 fibrosis. Out of a total of 36 patients   19 patients (53%) had progression of fibrosis on repeat biopsies, 16 patients (44%) showed no change in stage and 1 patient (3%) showed improvement in fibrosis on repeat biopsy. Among the patient factors examined (Table 3), the group that showed progression had significantly higher ALT and AST values when compared to the group that did not show progression .There was no statistically significant difference between   baseline APRI, FIB-4 index , stage at initial biopsy, .age or length of time between biopsies in progressors compared to non progressors. The correlation between change in stage and change in APRI is 0.627 (r)   (r² = 0.393377)  (p value=0.00001).The correlation between change in stage and change in FIB-4 is 0.56 ( r)  ( r² = 0.313431) p=.00004. A change in APRI ( delta APRI ) of 0.18 had 80%Positive Predictive Value( PPV) and 67 %Negative Predictive Value( NPV )in predicting progression of fibrosis . An analysis of ROC curve revealed that change in FIB-4 (delta Fib-4) of 0.39 had 75% PPV AND  75 % NPV for predicting progression of  fibrosis.

 APRI and Fib-4 at the time of second biopsy had excellent predictive value for progression in fibrosis. The Area Under ROC Curve (AUC) of APRI at the time of second biopsy was  0.824 (95 % CI =0.660 to 0.930)(p value=0.0001). APRI  >.0.69 had a 79 % PPV and APRI <0.69 had  a   78% NPV for progression of fibrosis. FIB-4 index at the time of second biopsy had an AUC  of 0.78 (95% CI 0.608 to .899) (p value of 0.0004).FIB-4 of >1.65 had a  81 % PPV and 70% NPV for prediction of fibrosis by any stage. APRI and FIB-4  at the time of repeat biopsy  had an ideal predictive value for two stage progression. APRI had an AUC of 1.0 (95% CI 0.85 to 1.00).APRI of greater than 1.94 had a 100% PPV and 100% NPV  for  prediction of two stage change in fibrosis.FIB-4  had an AUC of 0.911(95% CI 0.717 to 0.986) (p value =0.0001).FIB-4 of greater than 3.01 had a 100% NPV and 62 % PPV for 2 stage change in fibrosis. The limitations of the study are 1)Retrospective study 2) Relatively small sale size
 Conclusions: 1) Changes in APRI and FIB-4 between first and second biopsies show significant correlation with change in fibrosis stage. 2) APRI and FIB-4   have good accuracy in predicting  fibrosis progression on   biopsies 3) Clinicians are justified in  using the APRI and FIB-4 to follow hepatic histologic progression to cirrhosis in chronic HCV patients

Table of Contents
Abstract
List of Tables
List of Figures
Introduction
Limitations of Liver biopsy
Noninvasive Markers
APRI and Fib-4
Aim
Literature   Review
Existing literature on APRI
Studies in UTMB
Existing literature on fib-4
Need for Present Study
Materials and Methods
Brief Overview
Study design
Sample Size estimate
Inclusion Criteria
Exclusion Criteria
Measurement of lab values
Biomarker Panels
Liver histology
Statistical Analysis
Human Subjects Research
Results
Discussion
Glossary
References
Vita

List of Tables

Table 1. Meta-analysis of   Predictive value of APRI for significant fibrosis and Cirrhosis
Table 2. Baseline Characteristics of subjects included in the study
Table 3.   Differences in parameters between Progressors and Non Progressors at Initial biopsy
Table  4. Differences between progressors and Non progressors on repeat biopsies
Table 5.  AUC of the markers at initial biopsy for prediction of fibrosis progression
Table 6.  AUC of the markers at repeat  biopsy for prediction of fibrosis progression
Table 7.  AUC of the change in biomarkers a for prediction of fibrosis progression

List of Figures

Figure 1. ROC of initial APRI in predicting progression of fibrosis
Figure 2. ROC of initial FIB 4 in predicting progression of fibrosis
Figure 3.  ROC of initial ALT in predicting progression of fibrosis
Figure 4.  ROC of initial AST  in predicting progression of fibrosis
Figure 5 . ROC of APRI at second biopsy  in predicting progression of fibrosis
Figure  6.  ROC of Fib-4 at second biopsy in predicting progression of fibrosis
Figure 7.Linear Regression Plot showing correlation between change in APRI  and change in stage.
Figure 8. ROC for predictive value of change in APRI
Figure 9. Linear Regression Plot showing correlation between change in Fib-4 and change in stage
Figure 10. ROC for predictive value of change in Fib-4 for change in stage

Introduction 

Limitations of Liver biopsy

 Hepatitis C virus(HCV) infection  is one of the most common causes of chronic liver disease, affecting  3 % of world’s population (Gangadharan, Antrobus, Dwek, & Zitzmann, 2007).Treatment and prognosis of this disease is guided by the histopathological  findings on liver biopsy. Liver biopsy is an expensive and invasive procedure with complications ranging from pain(around 30 %) to death (0.3 %)(Zaman, 2006) Liver biopsy is also limited by high inter and intra- observer variability.  Thirty three percent discordance rates for stage of fibrosis have been reported in biopsy specimens.(Halfon et al., 2006) The frequency of sampling errors can be estimated from the fact that a standard percutaneous liver biopsy specimen is calculated to be 1/50,000 part of liver(Pinzani, Vizzutti, Arena, & Marra, 2008).
Noninvasive Markers
Considering the expense and limitations of liver biopsy there is felt to be a growing need to study non invasive bio markers of liver fibrosis that can replace liver biopsy as a guide to therapy and as prognostic indicators. Currently studies have shown that non invasive markers can predict the absence of disease or the presence of advanced  disease, but it is not clear that they can predict  step wise progression of fibrosis(Pinzani et al., 2008).It is also not clear if they have prognostic value  regarding  the long term clinical outcomes .If they prove to be  useful longitudinally, it would be of great benefit to patients and  clinicians.
Noninvasive markers can be broadly divided into two major groups- radiological and serum markers. Transient elastography using ultrasound waves to measure stiffness of liver is an example of a radiological method but has limitations in terms of cost, technical complexity and availability. It currently is not approved for clinical use in the United States by the Food and Drug Administration (FDA). Serum biomarkers have been proposed as an inexpensive and effective alternative to replace liver biopsy. Serum biomarkers are broadly classified into two distinct categories –markers that reflect Extracellular Matrix(ECM) turn over  otherwise  called direct markers and  routinely available liver function tests that are surrogate markers of hepatic fibrosis-indirect markers. Direct markers like Matrix MetalloProteinases(MMP),Hyalorunic Acid(HA)have been incorporated into panels like the  ELF,( European Liver Fibrosis)  And Fibrospect II  . Direct markers are expensive (due to  their  proprietary nature) and have limited availability only at reference laboratories.
APRI and Fib-4

Indirect marker panels are made up of commonly   available liver function tests. They are inexpensive and available universally. Two of the indirect marker panels namely APRI (AST-Platelet Ratio Index) and Fib-4 are composed of panels that are done routinely in all patients with liver disease. Other indirect marker panels consisting of variables like age, platelet count   Prothrombin Time(PT) have been proposed but less well studied.	
Most of the studies of biomarkers in fibrosis examine their diagnostic accuracy in predicting the   presence of severe fibrosis (cirrhosis ) or ruling out presence of  mild  fibrosis  and use liver biopsy as the gold standard(Pinzani et al., 2008). There are two limitations to these approaches. :  1)   The limitations of liver biopsy make it a less than ideal reference standard and 2) it is not clear if these biomarkers can help identify patients in whom fibrosis would progress. It is important to develop a non invasive tool of predicting progression of early fibrosis as these patients might benefit from clinical interventions like antiviral therapy and or more frequent and intense monitoring.
Hence we propose to examine the role of two indirect biomarker panels –APRI and Fib-4 in predicting progression of liver fibrosis and clinical outcomes.

Aim

This study aims to 1) Correlate longitudinal changes in APRI and Fib-4 in patients with chronic HCV infection with the change in stage of liver fibrosis 2) Examine the predictive value of APRI and Fib-4   in identifying patients who would progress on liver biopsy.

Literature   Review

This chapter reviews  the existing literature on the  use of APRI and Fib-4 in chronic HCV infection  justifies the need for present study
Reliable staging of hepatic fibrosis in HCV patients is essential to guide decisions regarding antiviral therapy and to identify patients who are at risk of progressing to decompensated liver disease. 
Existing literature on APRI

Wai et al derived and validated APRI in their land mark study  in 2003(Wai et al., 2003).They concluded that APRI has high Area under Receiver Operating Characteristic (ROC) curve(AUC) for prediction of significant fibrosis (0.80). In their study, an APRI of less than 0.5 had a negative predictive value (NPV) of 86% and an APRI of greater than 1.5 had a Positive Predictive Value (PPV) of 88 % for prediction of significant fibrosis. Since then, ~ 40 studies and 2 meta-analyses (Shaheen & Myers, 2007, 2008) have been published investigating the role of APRI. The outcomes for   interest  were 1) the predictive value of APRI for significant fibrosis (Metavir or Batts Ludwig Stages F2-F4 and Ishak stage F3-F6)   2) the predictive value of APRI for cirrhosis on liver biopsy. The results of the meta-analysis(Shaheen & Myers, 2007) are summarized in Table 1.The study concluded that APRI  at different thresholds  was most  useful to exclude significant fibrosis and cirrhosis. APRI of less than 0.5 had NPV of   75 % for significant fibrosis and APRI of less than 1.0 had a NPV of 91% for cirrhosis. Initial concerns existed regarding the usefulness of APRI in HCV patients co-infected with Human Immunodeficiency  virus(HIV) since liver function abnormalities and thrombocytopenia can be multi factorial in origin in these patients. But several studies and a meta-analysis(Shaheen & Myers, 2008)have shown that APRI is useful in this subset of patients also.(Table 1).
Studies in UTMB 

Significant research into the value of non invasive biomarkers has been performed in our institution (UTMB).  Snyder et al reported that  cut offs  of 0.42 and 1.2 have better performance characteristics than cut offs  originally proposed by Wai [5] (0.5 and 1.5). These cut off values are used to triage patients  eligible for antiviral  treatment  in the Texas Department of Criminal Justice (TDCJ) correctional care system managed  by UTMB.

Table 1. Meta-analysis of   Predictive value of APRI for significant fibrosis and Cirrhosis

S. No	All HCV patients  	HCV/HIV co infected patients	
Number of Studies 

	         22	4	

Number of patients studied

        4266	
404	

Summary AUC  for significant fibrosis 

0.76 ( 95% CI  0.74-0.79)	

0.79(95%0.70-0.86)

  Summary  AUC   for  cirrhosis 

  0.82( 95% CI 0.79-0.86)	

0.83(0.69-0.95)	

Summary Sensitivity   and Specificity   of  APRI at threshold of 0.5  for significant fibrosis 
	           
 81 % sensitivity  and  50 %     specificity 	
86% sensitivity and 41 % specificity	
Summary Sensitivity and specificity of APRI at threshold of 1.0   for cirrhosis	35 % sensitivity and 91% specificity 	50 % sensitivity and 92% specificity	
Diagnostic Odds Ratio  of APRI for significant  fibrosis 
	5.7  (95% CI 4.3-7.5 )	6.8(95% CI  3.8-12.1)	
Diagnostic  Odds Ratio of  APRI for cirrhosis 	11.3 (95% CI 7.9-16.0)	11(4.6-26.2)	

Existing literature on fib-4

Fib-4 Index   is a noninvasive biomarker that  was first proposed by the investigators of the APRICOT (AIDS Pegasys Ribavirin International Co infection Trial)   study group (Sterling et al., 2006).Fib-4 is derived from the following formula   Fib 4 =Age x Aspartate Amino Transferase (AST) (U/ml)/Platelets x ALT1/2 (U/ml).This formula takes into account, age and  blood  Alanine  Amino Transferase(ALT) values  in addition to the parameters used  for calculation of APRI. In the initial study Sterling et al conducted a retrospective analysis of 832 patients and concluded  that  FIB-4 of <1.45 had a negative predictive value of 90 % in ruling out advanced fibrosis and FIB-4 of >3.25 had a positive predictive value of 65% in ruling in advanced fibrosis in patients  with HCV/HIV  co infection  Subsequent to this report, multiple studies have been published that validated the predictive value of  Fib-4 in HCV patients (Trang, Petersen, & Snyder, 2008; Vallet-Pichard et al., 2007; Vallet-Pichard, Mallet, & Pol, 2006).In a study of 592 patients Vallet-Pichard(Vallet-Pichard et al., 2007) et al reported that  the AUC of FIB-4  for  severe fibrosis(F3-F4) is  0.85 (95% CI 0.82-0.89) and 0.91 (95% CI 0.86-0.93) for cirrhosis  . An FIB-4 index<1.45 had NPV of 94.7% to exclude severe fibrosis with a sensitivity of 74.3%. An FIB-4 index > 3.25 had a PPV  to confirm the existence of a significant fibrosis (F3-F4) of 82.1% with a specificity of 98.2%. 

Trang et al from UTMB(Trang et al., 2008) reported that Fib-4 Index of  <1.39 had  a NPV of 70%for significant fibrosis and Fib-4 of > 2.05 had  PPV of 81.8 % in HCV/HIV co infection .They concluded that FIB-4 was superior to the APRI in identifying mild from significant fibrosis in  HIV co-infected patients .

Most of the existing studies regarding APRI and FIB-4 were designed to examine their accuracy in predicting the stage of fibrosis on liver biopsy .Ngo et al (Ngo et al., 2006) performed a prospective analysis of the prognostic ability of APRI. APRI scores were predictive of 5 year complication free survival (AUC=.82).APRI  ,measured 6 months after end of  HCV therapy  has been shown to predict development of Hepato Cellular Carcinoma (AUC=0.87)(Yu et al., 2006).
Need for Present Study

Even though abundant literature exists regarding the role of biomarkers in Chronic HCV infection, data regarding   serial changes in biomarkers and their correlation with changes in fibrosis stage on liver biopsy is scarce. An ideal biomarker would show longitudinal changes that will correlate with changes in liver biopsy and also has ability to predict progression of fibrosis. This is essential for clinicians treating HCV as the threshold for initiating antiviral treatment is Stage 2 (F2) and patients with Stage 0- Stage 1 disease are usually followed with serial liver biopsies .A biomarker that predicts progression in fibrosis would eliminate the need for serial liver biopsies .We aim to study the behavior of two simple biomarkers APRI and FIB-4 longitudinally and examine their correlation with changes in serial liver biopsies. 

Materials and Methods 

Brief Overview

Liver biopsies occupy  central role in management of HCV infection. Since treatment carries significant toxicity and there is only a 40-50% response rate in patients infected with certain viral genotypes, patient selection is crucial. Many clinicians prefer not to treat patients with Stage 0 or Stage 1 Fibrosis on liver biopsy since the rate of progression is low .These patients are followed clinically and most of them undergo repeat liver biopsies 3-5 years later to reevaluate the clinical management strategy. In the present study, information regarding the liver biopsies and liver function tests was obtained from   The University of Texas Medical Branch (UTMB) Hospital Electronic Medical Record (EMR) 
Study design

The study is a retrospective cohort study. All patients who underwent liver biopsies for staging Hepatitis C virus fibrosis at UTMB from 1999 to 2007 formed the patient cohort.

Sample Size estimate

 Assuming that the AUROC for APRI and Fib -4   would be 0.8 if  the null hypothesis is rejected and 0.5 if the null hypothesis is valid, a sample size of 29 is needed to detect the difference at an alpha value of 0.05 and beta value of 0.8.
 Inclusion Criteria 

•	All patients with more than one liver biopsy separated by at least one year   meet the inclusion criteria for the study.
Exclusion Criteria 

•	Patients with repeat biopsies less than one year apart were excluded from the study as repeat liver biopsies less than an year interval are not performed routinely in management of Hepatitis C and indicate a coexistent problem or are usually done for study purposes 
•	Patients with history of active alcohol abuse 
•	Patients with  HIV and HBV infection
•	Patients with coexisting immune or  metabolic hepatic disease 

Measurement of lab values
 The platelets were measured using the Sysmex SE 9500 (Sysmex, Mundelein, IL) and aminotransferases were measured using either the Vitros 950 or 5,1 FS (Ortho Clinical diagnostics, Raitan, NJ).
Biomarker Panels

We intend to study two biomarker panels-namely APRI and FIB-4 index.
APRI is a numerical value that is calculated using the following formula  
                 APRI=AST (U/ml)/Upper Limit of Norma l(U/Ml)    x 100/Platelets (109/litre)
FIB-4 index   is also a numerical value  that is calculated  using the following formula
                      Fib 4 =Age x AST (U/ml)/Platelets(109 x ALT1/2 (U/ml).
The values for the ALT, AST, and Platelets were extracted from the patients EMR. 
Liver histology 

 All liver biopsies were read by a single pathologist (S.X.) and were graded by the Ludwig Batts scoring system (described below). Since all   biopsies were interpreted using a uniform scoring system by a single pathologist, the grading reported in the pathology report will be accepted. The fibrosis score is rated on a five point scale (0 = no fibrosis, 1 = portal fibrosis, 2 = periportal fibrosis, 3 = septal (or bridging)fibrosis, 4 = cirrhosis).
 
Statistical Analysis 

In the initial analysis the median AST,ALT  ,Platelets, APRI and Fib-4 scores  were compared between patients who progressed on liver biopsies and  those not progressing (non progressors) using a nonparametric test (Mann-Whitney U test).Logistic regression was used to calculate the accuracy of markers like ALT,AST,platelets APRI and FIB-4 in predicting progression of fibrosis. ROC curves were calculated for biomarkers at the time of initial biopsy and at repeat biopsy. Progression of fibrosis (0 for no progression 1 for progression) was the variable to explain and each individual biomarker was the explanatory variable .Area Under ROC curves (AUROC) ,sensitivity, specificity, Positive Predictive Value PPV), Negative Predictive Value (NPV) were calculated. Non parametric tests were done using Stats Direct statistical software  (Cheshire,United Kingdom) and ROC curves were constructed  using MedCalc statistical software (Mariakerke,Belgium)
Human Subjects Research

  The Institutional Review Board (IRB) at UTMB has approved the study. Patient identifying  data  of all types was removed prior to analysis. All data was stored in the computer work station in  the Principal Investigator’s (PI) locked office and was password protected.

Results

 A total of 36 patients met the inclusion criteria. The baseline characteristics of the included subjects is described in Table 1.The median age at the time of initial biopsies was 47 (Range 25-68). Seventy five percent (27/36) of patients were males. Fifty percent of the included subjects (18/36) were Caucasians, 31% (11/36) African Americans and 19 %( 7/36) were Hispanics. Majority of the patients were Stage 1 on biopsy (50%) followed by Stage 2( 25%) and Stage 3 (22%).One patient had Stage 0 fibrosis on biopsy and there were no patients with cirrhosis since they were excluded from the study. The median duration between biopsies is 4 years (Range 2-9 years).

Table 2: Baseline Characteristics of subjects included in the study

Total Number of  Subjects included (n)

	36	
Number of Males 
	27/36(75%)	
 Age (in Years)	47(25-68)	
 Race 	Caucasian                  18/36     (50%)       

African American     11/36      (31%)

Hispanic                    7/36         (19%)

 Fibrosis  Stage  at the time of initial biopsy

	Stage 0  -1/36  (3%)
Stage 1- 18/36 (50%)
Stage 2- 9/36   (25%)
Stage 3- 8/36   (22%)
Stage4- 0/36
	
ALT  (IU/ML)	73(14-322) 	
AST(IU/ML)	49(22-266)	
Platelets(109/l)	235.5(185-362)	
Median duration between biopsies 	4 yrs( 2-9)	

All continuous variables are expressed as median (minimum –maximum).Categorical variables are expressed as percentage.
ALT-Alanine Leucine Aminotransferase, AST-Aspartate Amino Transferase 

Out of a total of 36 patients   19 patients (53%) had progression of fibrosis on repeat biopsies, 16 patients (44%) showed no change in stage and 1 patient (3%) showed improvement in fibrosis on repeat biopsy. Among the patient factors examined (Table 3) the group that showed progression had significantly higher ALT and AST when compared to the group that did not show progression .There was no statistically significant difference between baseline APRI, FIB-4 index , Stage at initial biopsy  age or duration between biopsies between progressors and non progressors.

Table   3.   Differences in parameters between Progressors and Non Progressors at Initial biopsy
 
	Progressors 	Non Progressors	p value 	
Number (n)	19/36
(14 -1 Stage change)
( 5  --2 Stage  change)
	17/36	0.999	
Age(yrs)
	50	52	0.621	
Sex
	Males -   13	Males - 14	>0.05(ns)	
	Females  -6	Females -3		
APRI 
	
0.655738	
0.561497	0.1725	
FIB-4 
	1.307	1.347	0.2675	
ALT (IU/ml)	92	47	 (0.003)	
AST (IU/ml(
	100	38	 (0.001)	
Platelets X 109/L	235	243	0.956	
Stage 
	2	1	0.477	
Duration between biopsies  (in yrs)	4	4	0.2	

  Differences in parameters were explored between the two groups at the time of repeat biopsies .ALT, AST, APRI and Fib-4 were significantly higher in patients who showed progression of fibrosis 

Table   4. Differences between progressors and Non progressors on repeat biopsies 

	Progressors	Non Progressors	P  value	
APRI 

	1.293	0.413	0.0006	
Fib-4  	2.816	1.36	0.0038	

ALT(IU/ML)	
92
	
47	
0.003	

AST(IU/ML)	100	38	0.0001	
Platelets (109/l)
	184	221	0.116	

ROC curves were constructed for  predictive value of APRI,  FIB-4 , ALT,AST  at the time of initial biopsy  for progression  of fibrosis (Figure 1 through 5 )  .The AUC s of initial markers are listed in Table 5 .AST had an AUC of 0.683 this  is  statistically significant 

Table 5.   AUC of the markers at initial biopsy for prediction of fibrosis progression 

	AUC
(95%CI)	P value 	
APRI	0.635
(0.458 to 0.788)	0.148	
FIB-4	0.610
(0.433 to 0.767)	0.2446
	
ALT
	0.639
(0.463 to 0.792)	0.1324	
AST
	0.683
(0.507 to 0.827)	0.04	

                                        

Initial APRI 

0

20

40

60

80

100

100

80

60

40

20

0

100-Specificity

Sensitivity

Figure 1. ROC of initial APRI in predicting progression of fibrosis

Figure 2. ROC of initial FIB 4 in predicting progression of fibrosis

Figure 3.  ROC of initial ALT in predicting progression of fibrosis

Figure 4.  ROC of initial AST  in predicting progression of fibrosis

 APRI and Fib-4 at the time of second biopsy had excellent predictive value for progression in fibrosis(Table 6)(Figure 4,5). APRI  >.0.69 had a 79 % PPV and APRI <0.69 had  a   78% NPV for progression of fibrosis..FIB-4 of >1.65 had a  81 % PPV and 70% NPV for prediction of fibrosis by any stage .

Table 6.   AUC of the bio markers at repeat  biopsy for prediction of fibrosis progression 

	AUC
(95%CI)	P value 	
APRI	0.824 
(0.660 to 0.930)	0.0001	
FIB-4	0.78
(0.608 to 0.899)	0.004

APRI and FIB-4  at the time of repeat biopsy  had an ideal predictive value for two stage progression. APRI had an AUC of 1.0 (95% CI 0.85 to 1.00).APRI of greater than 1.94 had a 100% PPV and 100% NPV  for  prediction of two stage change in fibrosis.FIB-4  had an AUC of 0.911(95% CI 0.717 to 0.986) (p value =0.0001).FIB-4 of greater than 3.01 had a 100% NPV and 62 % PPV for 2 stage change in fibrosis .

Figure 5 . ROC of APRI at second biopsy  in predicting progression of fibrosis

Figure  6.  ROC of Fib-4 at second biopsy in predicting progression of fibrosis

Changes in APRI and FIB-4 correlated well with change in stage of fibrosis. The correlation between change in stage and change in APRI is 0.627 (r)   (r² = 0.393377)  (p value=0.00001) ( Fig  7,8) .The regression equation is  change in stage = 0.504117 change in  APRI + 0.443435. AUROCs were constructed for  predictive value of change in APRI for change in stage .A change in APRI ( delta APRI ) of 0.18 had 80% PPV and 67 % NPV in predicting progression of fibrosis .
The correlation between change in stage and change in FIB-4 is 0.56 ( r)  ( r² = 0.313431) p=.00004.(Fig 9,10) The regression equation is change in stage = 0.342243 change in  FIB-4 + 0.315739 . An analysis of ROC curve revealed that change in FIB-4 (delta Fib-4 ) of 0.39 had 75% PPV AND  75 % NPV for predicting progression of  fibrosis.

Table 6.   AUC of the change in biomarkers at repeat   biopsy for prediction of fibrosis progression 

	AUC
(95%CI)	P value 	
APRI	0.703
(0.528 to 0.843)	0.001	
FIB-4	0.737
(0.564 to 0.869)
	0.004

 	 

-2

0

2

4

-2

-1

0

1

2

change in stage

Change in APRI

Figure 7.Linear Regression Plot showing correlation between change in APRI  and change in stage.

Change in  APRI

0

20

40

60

80

100

100

80

60

40

20

0

100-Specificity

Sensitivity

Figure 8. ROC for predictive value of change in APRI 

-2

0

2

4

6

-2

-1

0

1

2

change in stage

Change in Fib-4

Figure 9. Linear Regression Plot showing correlation between change in Fib-4 and change in stage

Figure 10. ROC for predictive value of change in Fib-4 for change in stage

 Discussion

Liver biopsy is the gold standard for estimating fibrosis in patients with liver disease and is used as a guide for therapy in HCV infection.  Fibrosis progresses only in 20-30% patients with Chronic HCV infection(Afdhal, 2004).  Antiviral therapy with pegylated interferon and Ribavirin carries significant toxicity and hence is usually offered to patients whose risk of progressive liver damage is high. Current strategy involves using the stage of fibrosis on initial liver biopsy to determine the risk of progressive liver damage. 

Apart from the complications associated with the invasive nature of liver biopsy, several limitations exist as to its diagnostic accuracy. Significant heterogeneity in hepatic fibrosis has been demonstrated in HCV patients that gives rise to sampling variability(Regev et al., 2002; Siddique, El-Naga, Madda, Memon, & Hasan, 2003).Inter and Intra- observer variability also affect the accuracy of liver biopsy. The size of the sample has also been shown to influence the quality of results of liver biopsy Studies have shown that only 14 % of liver biopsies sample the ideal desired size of 25 mm(Bedossa, Dargere, & Paradis, 2003; Poynard et al., 2004) and the chances of detection of significant fibrosis increase with increasing  amount of portal areas in biopsy sample(Fontana et al., 2006). The expense of liver biopsy ( around $1200  in our institution) is another major limiting factor in treating a widely prevalent disease like HCV. Factors that determine the varying rates of  fibrosis progression are poorly  understood ,partially because of the logistical difficulties  for studies in performing serial liver  biopsies.
Noninvasive biomarkers that reflect hepatic fibrosis have been proposed to overcome these limitations. An ideal biomarker of hepatic fibrosis is inexpensive, easily available, and reflects changes in fibrosis accurately. Two biomarker panels that incorporate routinely done tests for all patients, APRI and FIB-4 are promising. Multiple studies have confirmed the accuracy of APRI and FIB- 4 in predicting fibrosis stage on liver biopsy(Adler et al., 2008; Castera et al., 2005; Gangadharan et al., 2007; Le Calvez et al., 2004; Lin, Chang, Yang, Yeh, & Lin, 2008; Ramos Paesa et al., 2007; Shaheen & Myers, 2007, 2008; N Snyder et al., 2006; Snyder et al., 2007; Trang et al., 2008; Vallet-Pichard et al., 2007; Vallet-Pichard et al., 2006; Wai et al., 2003; Yu et al., 2006).None of the studies examined the longitudinal behavior of the biomarkers. Our study addressed the longitudinal behavior of these markers in individuals who underwent serial biopsies and examined the role of these biomarkers in predicting progression.

The baseline characteristics of the study population did not differ between progressors and non progressors except for aminotransferases which were significantly higher in the group that later showed progression of fibrosis .This effect was independent of the initial stage of fibrosis which did not differ significantly between both groups. It seems likely that even with the same stage of fibrosis higher enzyme levels are a marker for subsequent progression. The effect of interval between biopsies on fibrosis progression in this study has also been ruled out as there was no significant difference between duration of biopsies in the progressor and non progressor group. 

The same parameters at the time of repeat biopsy were examined for differences between progressors and non progressors. Patients with progression of fibrosis had significantly higher APRI and FIB-4 levels. This offers preliminary evidence for the possibility that APRI and FIB-4 might accurately reflect changes in fibrosis.

Changes in APRI and FIB-4 correlated well with change in stage of fibrosis. The correlation between change in stage and change in APRI and Fib-4  is 0.627 (r)   (r² = 0.393377)  (p value=0.00001)  and 0.56 ( r)  ( r² = 0.313431) p=.00004 respectively..A change in APRI (delta APRI) of 0.18 had 80% PPV and 67 % NPV in predicting progression of fibrosis and change in FIB-4 (delta Fib-4) of 0.39 had 75% PPV and 75 % NPV for predicting progression of fibrosis. These results are significant as they reflect longitudinal changes of APRI and FIB-4 markers .The significant correlation and good predictive value of change in APRI and Fib-4 with change in stage make them promising tools that can be used by clinicians to follow patients with HCV infection 

APRI and FIB-4 at the time of initial biopsy had poor predictive value for progression of fibrosis .The AUC of APRI and FIB-4 were 0.635(p = 0.148) and 0.610(p =0.2446) respectively. Among all the markers that were examined AST had the best AUC -0.683(p value=.004).
APRI and FIB-4 at the time of repeat biopsy had good predictive value for progression of fibrosis. APRI had an AUC of 0.82(p=0.0001) .APRI value of >0.69 had 79% sensitivity and 78 % specificity in predicting progression of fibrosis. Similarly Fib-4  at repeat biopsy also had a good predictive value in predicting progression.
A one stage change in fibrosis on repeat biopsies may reflect sampling variability, hence we examined the predictive value of APRI and FIB-4  in a 2 stage change. APRI and FIB-4 at the time of repeat biopsy had an ideal predictive value for a 2 stage change in fibrosis APRI had an AUC of 1 and   FIB-4   had an AUC of 0.911 for predicting a 2 stage change in fibrosis.  
An ideal biomarker that can replace serial liver biopsies to follow patients with HCV infection should be inexpensive and universally available and high have accuracy. APRI and FIB-4 do fulfill the above criteria
. The principal usefulness of APRI  and FIB-4 in serial follow up of HCV patients  is for ruling out  development of  significant fibrosis and avoidance of liver biopsy. In this study APRI of less than 0.42 correctly ruled out progression of fibrosis in 85 % of patients and FIB-4 of less than 1.4 correctly ruled out progression of fibrosis in 90% of patients.
This study has several limitations .Most of them are secondary to the retrospective nature of the study. Factors like inter current illnesses that  influence aminotransferases and platelets could not be determined by history in some of the patients .Another significant limitation is  the relatively  small  sample size .Well designed prospective trials can  further confirm the  role  of  biomarkers in predicting fibrosis progression.
In summary APRI and FIB-4 have good accuracy in predicting progression of fibrosis , changes in APRI and FIB-4  correlate well  with changes  in  stage  of fibrosis and can be used  by clinicians to follow patients  with HCV. More rigorously controlled prospective trials are needed to confirm the results.

Glossary 
•	ALT- Alanine Leucine Amino Transferase
•	APRI- AST to Platelet Ratio Index
•	AST-  Aspartate Amino Transferase
•	AUC- Area under ROC curve
•	HCV- Hepatitis C Virus 
•	ROC-Receiver Operating Characteristic

References 
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Vita

Rajasekhara Mummadi was born in Vijayawada, India on March 29 1975, to parents Narayana Reddy Mummadi and Leelavathi Mummadi. After graduating from N.S.M Public School, Vijayawada, India he completed his medical school at Osmania Medical College Hyderabad, India and received a Doctor of Medicine degree in 1997 . He completed a 3 year residency in Internal Medicine in Gandhi Medical College, Hyderabad, India. He joined the Internal Medicine residency program in the University of Texas Medical Branch, Galveston, Texas.After completing residency in 2006, he joined the University of Texas Medical Branch for a clinical fellowship in Gastroenterology and to pursue a Masters Degree in Clinical Sciences.

Permanent address:	465 Magnolia Crossing Street ,League City ,Texas  77573
This dissertation was typed by Rajasekhara Mummadi