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Gut. Mar 2006; 55(3): 409–414.
PMCID: PMC1856097

Prediction of liver fibrosis in human immunodeficiency virus/hepatitis C virus coinfected patients by simple non‐invasive indexes

Abstract

Background

Liver biopsy is an invasive technique with associated major complications. There is no information on the validity of five non‐invasive indexes based on routinely available parameters, estimated and validated in hepatitis C virus (HCV) monoinfected patients, in human immunodeficiency virus (HIV)/HCV coinfected patients.

Aim

To validate these predictive models of liver fibrosis in HIV/HCV coinfected patients.

Patients

A total of 357 (90%) of 398 patients from five hospitals were investigated, who underwent liver biopsy and who had complete data to validate all of the models considered.

Methods

The predictive accuracy of the indexes was tested by measuring areas under the receiver operating characteristic curves. Diagnostic accuracy was calculated by estimating sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values.

Results

The models performed better when liver biopsies [gt-or-equal, slanted]15 mm were used as reference. In this setting, the Forns and Wai indexes, models aimed at discriminating significant fibrosis, showed PPV of 94% and 87%, respectively. Using these models, 27–34% of patients could benefit from exclusion of liver biopsy. If both models were applied sequentially, 41% of liver biopsies could be spared. The indexes aimed at predicting cirrhosis achieved NPV of up to 100%. However, they showed very low PPV.

Conclusions

The diagnostic accuracy of these models was lower in HIV/HCV coinfected patients than in the validation studies performed in HCV monoinfected patients. However, simple fibrosis tests may render liver biopsy unnecessary in deciding anti‐HCV treatment in over one third of patients with HIV infection and chronic hepatitis C.

Keywords: human immunodeficiency virus, hepatitis C virus, coinfection, liver biopsy, liver fibrosis, predictive models

Human immunodeficiency virus (HIV) and hepatitis C virus (HCV) dual infection is highly prevalent among intravenous drug users as a result of shared transmission routes.1 In addition, chronic hepatitis C seems to follow an accelerated course in HIV infection.2 Thus liver failure is increasingly affecting HIV/HCV coinfected patients, as their AIDS free survival is being prolonged.3 For these reasons, HCV infection should be treated in this setting. However, the available treatment for HCV infection is far from optimal. Indeed, HIV/HCV coinfected patients show even worse responses to pegylated interferon plus rivabirin than HCV monoinfected patients.4 In this regard, different strategies have been proposed and evaluated to improve the selection of patients to receive therapy. A rational screening is to perform a liver biopsy and reserve treatment for those with more advanced stages of liver fibrosis. We have shown that this approach would spare up to 40% of coinfected patients from anti‐HCV therapy.5

Liver biopsy is an invasive technique. Although infrequent, there are major complications associated with liver biopsy.6 Mild adverse events are more frequent, such as pain, that occurs in more than 30% of biopsied patients.6 Moreover, the procedure is costly7 and can be limited by sampling error as only 1/50 000 of the organ is sampled. Hence some authors have validated models to predict the severity of liver fibrosis by non‐invasive means. Some rely on routine laboratory tests, easily available in clinical practice.8,9,10,11,12,13,14 There are only two reported models which have focused on non‐invasive diagnosis of liver fibrosis among HIV/HCV coinfected patients.15,16 However, none of the models has been validated by independent authors in this population. In addition, the usefulness of these indexes may be curtailed because some of the predictive markers, such as α2 macroglobulin, haptoglobin, or apolipoprotein A115 and hyaluronic acid16 are not routinely used in clinical practice.

Our aim was to validate five predictive models of liver fibrosis comprising readily available laboratory data, previously constructed and validated in HCV monoinfected patients,8,9,10,11,12,13,14 in HIV/HCV coinfected patients.

Methods

Patients

This retrospective cross sectional study included 398 consecutive patients with HIV/HCV coinfection who were admitted to five hospitals in southern Spain for liver biopsy, from January 1991 to January 2005. Liver biopsies were taken mainly with the aim of establishing the prognosis and indicating therapy for chronic hepatitis C. Eligible patients were those coinfected with HIV and HCV who had undergone liver biopsy, regardless of levels of transaminases. Exclusion criteria included positive hepatitis B surface antigen, other causes of liver disease (autoimmune, tumoral, biliary, or vascular associated liver disease) and prior anti‐HCV therapy. Clinical, biochemical, and haematological data within one month of liver biopsy were collected from databases that abstracted patient records.

For each patient a case report form was completed. It included the main demographics, and clinical, laboratory, and virological data at the time of liver biopsy. Recorded demographics included age, sex, risk category, and history of alcohol intake. Data regarding both HIV and HCV infections were recorded, including plasma HIV‐RNA, CD4+ T cell counts at the time of liver biopsy, and antiretroviral therapy. Regarding HCV infection, data recorded included genotype, viral load, both at the time of liver biopsy, and date of infection. The latter was estimated as the first year of needle exchange in intravenous drug users. The date of HCV infection was considered as unknown for subjects infected through sexual contact or an undefined source.

This study was approved by each local ethics committee. All patients gave written informed consent for liver biopsy.

Predictive indexes of fibrosis

Among the indexes based on routinely available laboratory tests, we selected those with internal8,9 or external validation.10,11,12,13,14 These indexes were elaborated with the aim of discriminating significant fibrosis, F2 to F4 stages, and/or cirrhosis in HIV seronegative patients:

Indexes aimed at discriminating significant fibrosis

Forns and colleagues8 and Wai and colleagues9 validated their results in a separate group of patients. The index by Forns and colleagues8 is calculated by applying the following regression equation:

7.811−3.131 ln (platelet count (109/l)) + 0.781 ln (γ‐glutamyl‐transpeptidase (UI/l)) + 3.467 ln (age (y)) − 0.014 (cholesterol (mg/dl)).

In the estimation of the model by Forns and colleagues,8 the authors excluded drinkers of more than 30 g/day of alcohol and the predominant HCV genotype was 1. The high prevalence of genotype 3 in HIV/HCV coinfected patients3 and its influence on cholesterol levels17 could have affected the accuracy of the index in our study. Because of this, we also analysed a subgroup of patients with alcohol intake <50 g/day and without genotype 3 to validate this model. The index by Wai and colleagues,9 known as the AST platelet ratio index (APRI), is calculated by dividing the aspartate aminotransferase (AST) level (UI/l), expressed as the number of times above the upper limit of normal (ULN), by platelet count (109/l):

AST (/ULN) ×100/platelet count (109/l)

Alcohol drinkers were not excluded from elaboration of the index. The cut off points validated by these authors and positive (PPV) and negative (NPV) predictive values of the indexes are shown in table 11.

Table thumbnail
Table 1 Cut off points, and positive (PPV) and negative (NPV) predictive values of the indexes evaluated, prevalence of significant fibrosis or cirrhosis in the validation studies, and proportion of liver biopsies that could be prevented

Indexes aimed at discriminating cirrhosis

APRI was also aimed at predicting cirrhosis.9 The index by Bonacini and colleagues10 has been recently validated by Saadeh and colleagues13 This index was calculated by assigning arbitrary scores to three laboratory parameters and summing them with a possible value of 0 to 11. The laboratory parameters were scored as follows:

  1. platelet count (109/l): >340 = 0; 280–340 = 1; 220–270 = 2; 160–219 = 3; 100–159 = 4; 40–99 = 5; <40 = 6.
  2. ALT/AST ratio: >1.7 = 0; 1.2–1.7 = 1; 0.6–1.19 = 2; <0.6 = 3.
  3. international normalised ratio: <1.1 = 0; 1.1–1.4 = 1; >1.4 = 2.

Alcohol drinkers were not excluded from validation of this index. AST/ALT ratio and platelet count were assessed as indexes in numerous previous surveys.11,12,14 Alcohol drinkers were not excluded in some of the studies that validated AST/ALT ratio11 or platelet count.12 The cut off points validated by these authors and PPV and NPV of the indexes are shown in table 11.

Laboratory methods

Blood determinations

HCV infection was diagnosed when serum specific antibodies were identified by enzyme immunoassay and a recombinant immunoblot assay before 1996. Since December 1996, diagnosis of HCV infection was made when a positive EIA‐3 was found and serum viral RNA was detected by either qualitative or quantitative polymerase chain reaction. HCV genotype was determined by line probe assay.

Histological evaluation

Specimens were immediately placed in buffered formalin. After 24 hours of fixation they were embedded in paraffin using routine methods. Histological evaluation was made on sections stained with haematoxylin‐eosin and Masson's trichrome. A single pathologist, who was not aware of the clinical data of the patients, evaluated all of the stained sections at each centre. Liver fibrosis was scored following the Knodell histological activity index modified by Scheuer.18 A minimum liver biopsy length of 10 mm was required. Reproducibility of liver fibrosis staging was assessed by blinded re‐evaluation by a single pathologist of 50% of the liver biopsies from each centre randomly selected.

Statistical methods

Continuous variables were expressed as median (Q1–Q3) and categorical variables as numbers (percentage). Continuous variables were compared using the Student's t test or the Mann‐Whitney U test when appropriate. Categorical variables were compared using the χ2 test with Yates' correction or Fisher's test where appropriate.

The predictive accuracy of the indexes was tested by measuring the areas under the receiver operating characteristic curves (AUROC). The cut off points evaluated were those previously validated for each index in HIV uninfected patients. Diagnostic accuracy was calculated by sensitivity, specificity, PPV, and NPV. Significant fibrosis (stages 2–4) or cirrhosis (stage 4) was considered as the disease depending on the index. Performance of the indexes was also assessed using ROC curves in different subpopulations of patients, classified according to the size of their liver biopsies. Agreement between pathologists from different centres and the central pathologist was assessed by the kappa test.

Statistical analysis was carried out using the SPSS 11 statistical software package (SPSS, Chicago, Illinois, USA).

Results

Characteristics of the patients

A total of 357 (90%) of 398 patients had complete data for validation of all of the models. The main characteristics of the study patients by date of liver biopsy are summarised in table 22.

Table thumbnail
Table 2 Characteristics of human immunodeficiency virus (HIV) infected patients with chronic hepatitis C virus (HCV) at the time of liver biopsy according to sample length

Liver biopsy was carried out in 321 (90%) patients after 1997. HCV genotype was 1 in 189 (53%), 3 in 87 (24%), 4 in 46 (13%), and not available in 36 (10%) patients. Median CD4+ cell counts by the time of liver biopsy were 494 (336–653) cells/ml. Median nadir CD4+ cell counts were 255 (136–396) cells/ml. A total of 189 (53%) patients showed undetectable HIV viral load achieved with highly active antiretroviral therapy (HAART) by the date of liver biopsy: 221 (62%) patients received protease inhibitor based HAART before liver biopsy, 57 (16%) were treated with nevirapine based antiretroviral regimens, and 71 (20%) were prescribed efavirenz based HAART. Good agreement was found between each centre's pathologist and the central pathologist in scoring significant fibrosis (kappa scores 0.76–0.80) and cirrhosis (kappa scores 0.87–0.93).

Predictive models of fibrosis applied to HIV infected patients with chronic hepatitis

Models aimed at predicting significant fibrosis

AUROC for the models of Forns and colleagues8 and APRI9 by biopsy length are shown in table 33.. Both models performed better for biopsy size [gt-or-equal, slanted]15 mm. Further increases in biopsy length did not improve AUROC. Because of this, 263 (74%) of 357 patients, in which liver biopsy length was at least 15 mm, were selected to validate these indexes. Characteristics of these patients are shown in table 22.

Table thumbnail
Table 3 Area under the receiver operating characteristic curves (95% confidence interval) of the indexes validated in human immunodeficiency virus/hepatitis C virus coinfected patients by liver biopsy length

For the model of Forns and colleagues,8 applying the lower cut off level (<4.2), 42 (38%) of 110 patients without significant fibrosis were correctly identified (table 44).

Table thumbnail
Table 4 Diagnostic accuracy of the models aimed at predicting significant fibrosis in the study group

The presence of significant fibrosis could not be excluded with certainty, as 33 (44%) of 75 patients with a score <4.2 had significant fibrosis (NPV 56%). Applying the higher cut off level (>6.9), 66 (43%) of 153 patients with significant fibrosis were correctly identified (table 44).). Sixty six (94%) of 70 patients with a score >6.9 showed significant fibrosis. Two of the four falsely classified patients showed F0 and two showed F1 stage at liver biopsy. In the study group, 106 patients reported alcohol intake <50 g/day and harboured genotype non‐3. We also applied the model by Forns and colleagues8 to these patients. AUROC was 0.77 (0.65–0.83). Diagnostic accuracy for this analysis was:

  1. low cut off (<4.2): sensitivity 79%, specificity 48%, PPV 63%, and NPV 67%;
  2. high cut off (>6.9): sensitivity 41%, specificity 98%, PPV 96%, and NPV 60%.

Twenty three (96%) of 24 patients with a score >6.9 showed significant fibrosis.

Using the APRI, for patients with a score <0.5, 36 (33%) of 110 without significant fibrosis would be correctly classified (table 44).). Among the 48 patients with a score <0.5, 12 (25%) showed significant fibrosis (75% NPV). Seven showed F2, four patients showed F3, and one patient F4 stage on liver biopsy. For patients with a score >1.5, 78 (51%) of 153 with significant fibrosis were correctly classified (table 44).). Seventy eight (89%) of 88 patients with a score >1.5 showed significant fibrosis. Seven of 11 misclassified patients showed F1 and three showed F0 stage on liver biopsy.

A total of 175 patients showed a score <1.5 in the APRI. These patients with indeterminate results were screened with the Forns and colleagues8 index, and 21 (12%) showed a Forns score >6.9. Two patients were misclassified. Thus the diagnostic accuracy of the index of Forns and colleagues8 applied to APRI indeterminate results (score <1.5) was: sensitivity 25%, specificity 98%, PPV 91%, and NPV 64%. Combining both indexes, 109 (41%) patients could be spared from liver biopsy.

The diagnostic accuracy of the indexes was not affected by HIV related variables. Patients with and without undetectable HIV RNA at the time of liver biopsy had an AUROC of 0.77 (0.70–0.82) and 0.75 (0.70–0.80) for the Forns model and 0.80 (0.75–0.84) and 0.79 (0.73–0.82) for the APRI, respectively. Patients with CD4+ cell counts [less-than-or-eq, slant]500 and >500 at the time of liver biopsy had an AUROC of 0.77 (0.72–0.84) and 0.76 (0.71–0.82) for the Forns model and 0.79 (0.74–0.83) and 0.79 (0.75–0.84) for the APRI, respectively.

Models aimed at predicting cirrhosis

AUROC values for these models are shown in table 33.. The models performed better for biopsy size [gt-or-equal, slanted]15 mm. Further increases in biopsy length did not improve AUROC. Because of this, patients with a liver biopsy length of at least 15 mm were selected to validate these indexes.

For APRI, 126 (93%) of 135 patients with a score <1 did not have cirrhosis (table 55).). Nine (23%) of 40 patients with cirrhosis were classified falsely. For patients with a score >2, 21 (46%) of 46 had cirrhosis and 25 (11%) of 223 without cirrhosis were identified falsely. PPV for both cut off points was low (table 55).

Table thumbnail
Table 5 Diagnostic accuracy of the AST platelet ratio index (APRI) and Bonacini model in predicting cirrhosis in the study group

For the Bonacini model,10 all 34 patients with a score below the low cut off did not show cirrhosis (table 55).). Twenty (29%) of 68 patients with score above the high cut off had cirrhosis, and 48 (16%) of 297 without cirrhosis were incorrectly identified. PPV for both cut off points was low (table 55).

The AST/ALT ratio was not accurate in predicting the absence or presence of cirrhosis (table 66).). Platelet count, using a cut off of 150 109/l, allowed prediction of the absence of cirrhosis with 92% certainty. The presence of cirrhosis was predicted with 33% certainty (table 66).

Table thumbnail
Table 6 Diagnostic accuracy of the aspartate aminotransferase/alanine aminotransferase (AST/ALT) index and platelet count in predicting cirrhosis in the study group

Discussion

In this study, we attempted to validate predictive models of liver fibrosis previously estimated in HCV monoinfected patients. We selected models based on data easily available, which had been subject to internal or external validation. The diagnostic accuracy of these models was lower in HIV/HCV coinfected patients than in validation studies performed in HCV monoinfected patients. However, simple fibrosis tests may render liver biopsy unnecessary for deciding therapy against HCV in over one third of patients with HIV infection and chronic hepatitis C, as significant liver fibrosis may be predicted in such patients.

Liver biopsy is an invasive technique with associated morbidity and mortality6 and has a significant cost.7 Because of this, others have attempted to find accurate non‐invasive markers of liver fibrosis in chronic hepatitis C. However, only two studies evaluated a models to predict liver fibrosis in HIV infected patients with chronic hepatitis C, but they were based on laboratory parameters not routinely performed which limits their clinical applicability.15,16 In addition, a cumbersome determination, only publicly available very recently, is involved in the calculation of the Fibrotest. This model appears to identify correctly an increased number of HIV/HCV coinfected patients with and without significant hepatic fibrosis, potentially sparing half of the patients from liver biopsy.15 This index has been subject to external validation by independent authors in only one study in patients with chronic hepatitis C without HIV infection.19 Unfortunately, the diagnostic yield of the test was not reproduced by these authors. A novel model, also based on non‐routinely used laboratory parameters, has recently been elaborated in HIV/HCV coinfected patients.16 However, this index was not validated in a separate group of patients by the authors. In addition, the performance of the model did not improve previous simpler indexes.

In the present study, the models aimed at discerning significant from non‐significant fibrosis reliably predicted the presence of substantial fibrosis. Thus the model of Forns and colleagues8 predicted the presence of significant fibrosis with 96% certainty, and only 4% of patients with a score >6.9 showed non‐significant fibrosis. Similarly, the APRI predicted the presence of significant fibrosis with 91% certainty, and misclassified 9% of patients with a score >1.5 who showed F0 to F1 stage fibrosis on liver biopsy. Hence 27–34% of patients would benefit from exclusion from liver biopsy as a tool for deciding anti‐HCV therapy. This represents one third of patients potentially excluded from liver biopsy compared with half of patients prevented from liver biopsy in the original studies.8,9 If patients with indeterminate results with the APRI are screened with the Forns model,8 40% of patients could be spared liver biopsy by combining both models, as treatment for HCV could be indicated in these cases.

The APRI has recently been validated in patients with chronic hepatitis C with HIV infection.16 Among HIV coinfected patients, AUROC was 0.71. Liver biopsy size >10 mm was required by the authors in this study. This poor result is in agreement with our findings as the APRI had an AUROC value of 0.73 for liver biopsies [gt-or-equal, slanted]10 mm in our study. We found that with larger liver biopsies as reference, this and other indexes performed better. In this regard, another recent study validated the APRI in patients with chronic hepatitis C without HIV infection.20 At least six portal tracts were required and mean length of the biopsy core was 19 mm. The AUROC of the APRI was 0.80, which is in agreement with our results for larger liver biopsies. Thus the potential variability of liver biopsy, whose diagnostic performance is critically affected by sample size, probably influenced the diagnostic yield of the indexes of fibrosis found in previous studies.

The APRI, the Bonacini model,10 and platelet count showed high levels of certainty in predicting the absence of cirrhosis. This may be reassuring for patients and physicians but is of little clinical use. Thus patients classified as not having cirrhosis still need a liver biopsy for treatment decisions. In contrast, these models did not confidently predict the presence of cirrhosis, as the PPV was low. These disappointing results are in agreement with a recent survey on patients without HIV infection.20

Liver biopsy was used as a reference for the diagnosis of fibrosis. However, the accuracy of liver biopsy for assessing fibrosis is limited by observer and sampling variability. Several studies have assessed interobserver variability in the evaluation of fibrosis. These surveys concluded that reproducibility in staging fibrosis in chronic hepatitis C is good, whatever the scoring system.21 In the present study, interobserver variability was also low. In contrast, sampling variability is more difficult to control. We evaluated the performance of the indexes evaluated in this study for different liver biopsy sizes. We observed that increasing the liver biopsy size from 10 mm to 15 mm improved the diagnostic yield but further increases did not provide better yields. This is in accordance with a previous survey that analysed discordant results between liver biopsy and markers of fibrosis.22 In this study, patients categorised as staging errors on liver biopsy showed smaller biopsy size. However, larger biopsies, [gt-or-equal, slanted]15 mm or [gt-or-equal, slanted]25 mm, were similarly frequent in patients with and without discordant results.

The patients included in this study may not be fully representative of the HIV/HCV population. Only patients who adhered to clinical visits and antiretroviral therapy were selected for liver biopsy. In addition, patients were usually scheduled to undergo liver biopsy only if they had been abstinent from alcohol and other drugs, and HIV infection was stable and under control. Thus there was a possible bias towards patients with less advanced HIV infection and less concomitant alcohol related liver disease. However, the indexes evaluated in this study would probably have performed worse in patients with these associated problems.

The presence of HIV infection changes the course of chronic hepatitis C. Thus coinfected patients show accelerated evolution of chronic hepatitis C, most probably related to immunosuppression.2 These patients are exposed to antiretroviral drugs that are associated with elevations in transaminases, bilirubin, γ‐glutamyl‐transpeptidase, and cholesterol, all of which can distort the results of some indexes. Moreover, antiretroviral therapy may alter the course of liver fibrosis in HCV infection.23,24 However, analysis of the study population stratified by CD4+ cell counts and undetectable HIV RNA achieved with antiretroviral therapy did not show changes in the performance of the indexes. Indeed, inclusion of HIV related variables in the validation of the Fibrotest in HIV/HCV coinfected patients did not improve the diagnostic yield of the model.16

In conclusion, therapy for HCV may be decided without liver biopsy evaluation of fibrosis in over one third of HIV infected patients with chronic hepatitis C using simple indexes. Absence of cirrhosis, but not its presence, and significant liver fibrosis can be predicted with certainty in most patients. However, these results clearly need improvement. Hence achieving a non‐invasive tool, readily available at the bedside, to predict liver fibrosis in the setting of HIV/HCV coinfection, still requires further investigation.

Conflict of interest: declared (the declaration can be viewed on the Gut website at http://www.gutjnl.com/supplemental).

Supplementary Material

[Competing interest statement]

Acknowledgements

This work was partly supported by grants from the Fondo de Investigaciones Sanitarias (FIS PI021726), by Fundació Barcelona SIDA 2002 (05/2003), and by the Fundación para la Investigación y la Prevención del SIDA en España (FIPSE 36380/03).

We thank Merchante N, Vergara S (Hospital Universitario de Valme, Seville, Spain, Grupo Andaluz para el Estudio de las Enfermedades Infecciosas), Brun FM, Pérez‐Guzmán E, Martínez‐Sierra C, Cabello P (Hospital Universitario Puerta del Mar, Cadiz, Spain, Grupo Andaluz para el Estudio de las Enfermedades Infecciosas).

Abbreviations

HCV - hepatitis C virus

HIV - human immunodeficiency virus

PPV - positive predictive value

NPV - negative predictive value

AUROC - area under the receiver operating curve

AST - aspartate aminotransferase

ALT - alanine aminotransferase

APRI - AST platelet ratio index

ULN - upper limit of normal

HAART - highly active antiretroviral therapy

Footnotes

All of the authors are members of the Grupo Andaluz para el Estudio de las Enfermedades Infecciosas (GAEI).

Conflict of interest: declared (the declaration can be viewed on the Gut website at http://www.gutjnl.com/supplemental).

References

1. Macías J, Pineda J A, Leal M. et al Influence of hepatitis C virus infection on the mortality of antiretroviral‐treated patients with HIV disease. Eur J Clin Microbiol Infect Dis 1998. 17167–170.170 [PubMed]
2. Graham C S, Baden L R, Yu E. et al Influence of human immunodeficiency virus infection on the course of hepatitis C virus infection: a meta‐analisis. Clin Infect Dis 2001. 33562–569.569 [PubMed]
3. Macías J, Melguizo I, Fernández‐Rivera F J. et al Mortality due to liver failure and impact on survival of hepatitis virus infections in HIV‐infected patients on potent antiretroviral therapy. Eur J Clin Microbiol Infect Dis 2002. 21775–781.781 [PubMed]
4. Torriani F J, Rodríguez‐Torres M, Rockstroth J. et al Peginterferon alfa‐2a plus ribavirin for chronic hepatitis C virus infection in HIV‐infected patients. N Engl J Med 2004. 351438–450.450 [PubMed]
5. Merchante N, Macías J, Palacios R B. et al Prevalence of non‐significant liver fibrosis and rate of fibrosis progression in HIV/HCV‐coinfected patients: Still a role for liver biopsy? AIDS 2004. 181746–1748.1748 [PubMed]
6. Canadrel J F, Rufat P, Degos F. Practices of liver biopsy in France: results of a prospective nationwide survey. Hepatology 2000. 32477–481.481 [PubMed]
7. Green R M, Flamm S. AGA technical review on the evaluation of liver chemistry tests. Gastroenterology 2002. 1231367–1384.1384 [PubMed]
8. Forns X, Ampurdanès S, Llovet J M. et al Identification of chronic hepatitis C patients without hepatic fibrosis by a simple predictive model. Hepatology 2002. 36986–992.992 [PubMed]
9. Wai C T, Greeson J K, Fontana R J. et al A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 2003. 38518–526.526 [PubMed]
10. Bonacini M, Hadi G, Govindarajan S. et al Utility of a discriminate score for diagnosing advanced fibrosis or cirrhosis in patients with chronic hepatitis C virus infection. Am J Gastroenterol 1997. 921302–1304.1304 [PubMed]
11. Imperiale T F, Said A T, Cummings O W. et al Need for validation of clinical decisions aids: use of the AST/ALT ration in predicting cirrhosis in chronic hepatitis C. Am J Gastroenterol 2000. 952328–2332.2332 [PubMed]
12. Renou C, Muller P, Jouve E. et al Relevance of moderate isolated thrombopenia as a strong predictive marker of cirrhosis in patients with chronic hepatitis C. Am J Gastroenterol 2001. 961657–1659.1659 [PubMed]
13. Saadeh S, Cammel G, Carey W D. et al The role of liver biopsy in chronic hepatitis C. Hepatology 2001. 33196–200.200 [PubMed]
14. Dienstag J. The role of liver biopsy in chronic hepatitis C. Hepatology 2002. 36S152–S160.S160 [PubMed]
15. Myers R P, Benhamou Y, Imbert‐Bismut F. et al Serum biochemical markers accurately predict liver fibrosis in HIV and hepatitis C virus co‐infected patients. AIDS 2003. 17721–725.725 [PubMed]
16. Kelleher T B, Mehta S H, Bhaskar R. et al Prediction of hepatic fibrosis in HIV/HCV co‐ infected patients using serum fibrosis markers: The SHASTA index. J Hepatol 2005. 4378–84.84 [PubMed]
17. Hofer H, Bankl H C, Wrba F. et al Hepatocellular fat accumulation and low serum cholesterol in patients infected with HCV‐3a. Am J Gastroenterol 2002. 972880–2885.2885 [PubMed]
18. Scheuer P J. Classification of chronic viral hepatitis: a need for reassessment. J Hepatol 1991. 13372–374.374 [PubMed]
19. Rossi E, Adams L, Prins A. et al Validation of the FibroTest biochemical markers score in assessing liver fibrosis in hepatitis C patients. Clin Chem 2003. 49450–454.454 [PubMed]
20. Lackner C, Struber G, Liegl B. et al Comparison and validation of simple non‐invasive tests for prediction of fibrosis in chronic hepatitis C. Hepatology 2005. 411376–1382.1382 [PubMed]
21. Goldin R D, Goldin J G, Burt A D. et al Intra‐observer and inter‐observer variation in the histopathological assessment of chronic viral hepatitis. J Hepatol 1996. 25649–654.654 [PubMed]
22. Poynard T, Munteanu M, Imbert‐Bismut F. et al Prospective analysis of discordant results between biochemical markers and biopsy in patients with chronic hepatitis C. Clin Chem 2004. 501344–1355.1355 [PubMed]
23. Macías J, Castellano V, Merchante N. et al Effect of antiretroviral drugs on liver fibrosis in HIV‐infected patients with chronic hepatitis C: harmful impact of nevirapine. AIDS 2004. 18767–774.774 [PubMed]
24. Benhamou Y, Martino V, Bochet M. et al Factors affecting liver fibrosis in human immunodefiency virus‐ and hepatitis C virus‐coinfected patients: impact of protease inhibitor therapy. Hepatology 2001. 34283–287.287 [PubMed]

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