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Meta-Analysis
. 2024 Aug 13;8(8):CD011929.
doi: 10.1002/14651858.CD011929.pub2.

Liver fibrosis stage based on the four factors (FIB-4) score or Forns index in adults with chronic hepatitis C

Marc Huttman  1 , Tommaso Lorenzo Parigi  1 , Mirko Zoncapè  1 , Antonio Liguori  1 , Maria Kalafateli  1 , Anna H Noel-Storr  2 , Giovanni Casazza  3   4 , Emmanuel Tsochatzis  1
Affiliations
Meta-Analysis

Liver fibrosis stage based on the four factors (FIB-4) score or Forns index in adults with chronic hepatitis C

Marc Huttman et al. Cochrane Database Syst Rev. .

Abstract

Background: The presence and severity of liver fibrosis are important prognostic variables when evaluating people with chronic hepatitis C (CHC). Although liver biopsy remains the reference standard, non-invasive serological markers, such as the four factors (FIB-4) score and the Forns index, can also be used to stage liver fibrosis.

Objectives: To determine the diagnostic accuracy of the FIB-4 score and Forns index in staging liver fibrosis in people with chronic hepatitis C (CHC) virus, using liver biopsy as the reference standard (primary objective). To compare the diagnostic accuracy of these tests for staging liver fibrosis in people with CHC and explore potential sources of heterogeneity (secondary objectives).

Search methods: We used standard Cochrane search methods for diagnostic accuracy studies (search date: 13 April 2022).

Selection criteria: We included diagnostic cross-sectional or case-control studies that evaluated the performance of the FIB-4 score, the Forns index, or both, against liver biopsy, in the assessment of liver fibrosis in participants with CHC. We imposed no language restrictions. We excluded studies in which: participants had causes of liver disease besides CHC; participants had successfully been treated for CHC; or the interval between the index test and liver biopsy exceeded six months.

Data collection and analysis: Two review authors independently extracted data. We performed meta-analyses using the bivariate model and calculated summary estimates. We evaluated the performance of both tests for three target conditions: significant fibrosis or worse (METAVIR stage ≥ F2); severe fibrosis or worse (METAVIR stage ≥ F3); and cirrhosis (METAVIR stage F4). We restricted the meta-analysis to studies reporting cut-offs in a specified range (+/-0.15 for FIB-4; +/-0.3 for Forns index) around the original validated cut-offs (1.45 and 3.25 for FIB-4; 4.2 and 6.9 for Forns index). We calculated the percentage of people who would receive an indeterminate result (i.e. above the rule-out threshold but below the rule-in threshold) for each index test/cut-off/target condition combination.

Main results: We included 84 studies (with a total of 107,583 participants) from 28 countries, published between 2002 and 2021, in the qualitative synthesis. Of the 84 studies, 82 (98%) were cross-sectional diagnostic accuracy studies with cohort-based sampling, and the remaining two (2%) were case-control studies. All studies were conducted in referral centres. Our main meta-analysis included 62 studies (100,605 participants). Overall, two studies (2%) had low risk of bias, 23 studies (27%) had unclear risk of bias, and 59 studies (73%) had high risk of bias. We judged 13 studies (15%) to have applicability concerns regarding participant selection. FIB-4 score The FIB-4 score's low cut-off (1.45) is designed to rule out people with at least severe fibrosis (≥ F3). Thirty-nine study cohorts (86,907 participants) yielded a summary sensitivity of 81.1% (95% confidence interval (CI) 75.6% to 85.6%), specificity of 62.3% (95% CI 57.4% to 66.9%), and negative likelihood ratio (LR-) of 0.30 (95% CI 0.24 to 0.38). The FIB-4 score's high cut-off (3.25) is designed to rule in people with at least severe fibrosis (≥ F3). Twenty-four study cohorts (81,350 participants) yielded a summary sensitivity of 41.4% (95% CI 33.0% to 50.4%), specificity of 92.6% (95% CI 89.5% to 94.9%), and positive likelihood ratio (LR+) of 5.6 (95% CI 4.4 to 7.1). Using the FIB-4 score to assess severe fibrosis and applying both cut-offs together, 30.9% of people would obtain an indeterminate result, requiring further investigations. We report the summary accuracy estimates for the FIB-4 score when used for assessing significant fibrosis (≥ F2) and cirrhosis (F4) in the main review text. Forns index The Forns index's low cut-off (4.2) is designed to rule out people with at least significant fibrosis (≥ F2). Seventeen study cohorts (4354 participants) yielded a summary sensitivity of 84.7% (95% CI 77.9% to 89.7%), specificity of 47.9% (95% CI 38.6% to 57.3%), and LR- of 0.32 (95% CI 0.25 to 0.41). The Forns index's high cut-off (6.9) is designed to rule in people with at least significant fibrosis (≥ F2). Twelve study cohorts (3245 participants) yielded a summary sensitivity of 34.1% (95% CI 26.4% to 42.8%), specificity of 97.3% (95% CI 92.9% to 99.0%), and LR+ of 12.5 (95% CI 5.7 to 27.2). Using the Forns index to assess significant fibrosis and applying both cut-offs together, 44.8% of people would obtain an indeterminate result, requiring further investigations. We report the summary accuracy estimates for the Forns index when used for assessing severe fibrosis (≥ F3) and cirrhosis (F4) in the main text. Comparing FIB-4 to Forns index There were insufficient studies to meta-analyse the performance of the Forns index for diagnosing severe fibrosis and cirrhosis. Therefore, comparisons of the two tests' performance were not possible for these target conditions. For diagnosing significant fibrosis and worse, there were no significant differences in their performance when using the high cut-off. The Forns index performed slightly better than FIB-4 when using the low/rule-out cut-off (relative sensitivity 1.12, 95% CI 1.00 to 1.25; P = 0.0573; relative specificity 0.69, 95% CI 0.57 to 0.84; P = 0.002).

Authors' conclusions: Both the FIB-4 score and the Forns index may be considered for the initial assessment of people with CHC. The FIB-4 score's low cut-off (1.45) can be used to rule out people with at least severe fibrosis (≥ F3) and cirrhosis (F4). The Forns index's high cut-off (6.9) can be used to diagnose people with at least significant fibrosis (≥ F2). We judged most of the included studies to be at unclear or high risk of bias. The overall quality of the body of evidence was low or very low, and more high-quality studies are needed. Our review only captured data from referral centres. Therefore, when generalising our results to a primary care population, the probability of false positives will likely be higher and false negatives will likely be lower. More research is needed in sub-Saharan Africa, since these tests may be of value in such resource-poor settings.

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Conflict of interest statement

No conflicts of interest to declare

Figures

1
1
Risk of bias and applicability concerns graph: review authors' judgements about each domain presented as percentages across included studies
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Risk of bias and applicability concerns summary: review authors' judgements about each domain for each included study testing the FIB‐4 score.
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Risk of bias and applicability concerns summary: review authors' judgements about each domain for each included study using the Forns index.
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PRISMA flow diagram detailing database searches, number of abstracts screened, and number of full texts reviewed. Date of search: 13 April 2022
5
5
Forest plot of FIB‐4 score for F2 – studies with cut‐off around 1.45
6
6
Summary ROC plot of FIB‐4 for F2 – studies with cut‐off around 1.45 The circles represent individual studies.
The solid circle represents the summary estimate of sensitivity and specificity.
The dotted line represents the 95% confidence regions. 
The dashed line represents the 95% prediction regions.
7
7
Forest plot of FIB4 for F2 ‐ Studies with cut‐off around 3.25.
8
8
Summary ROC plot of FIB4 for F2 ‐ Studies with cut‐off around 3.25. The circles represent individual studies.
The solid circle represents the summary estimate of sensitivity and specificity.
The dotted line represents the 95% confidence regions. 
The dashed line represents the 95% prediction regions.
9
9
Forest plot of FIB4 for F3 ‐ Studies with cut‐off around 1.45.
10
10
Summary ROC plot of FIB4 for F3 ‐ Studies with cut‐off around 1.45. The circles represent individual studies.
The solid circle represents the summary estimate of sensitivity and specificity.
The dotted line represents the 95% confidence regions. 
The dashed line represents the 95% prediction regions
11
11
Forest plot of FIB4 for F3 ‐ Studies with cut‐off around 3.25.
12
12
Summary ROC plot of FIB4 for F3 ‐ Studies with cut‐off around 3.25. The circles represent individual studies.
The solid circle represents the summary estimate of sensitivity and specificity.
The dotted line represents the 95% confidence regions. 
The dashed line represents the 95% prediction regions
13
13
Forest plot of FIB4 for F4 ‐ Studies with cut‐off around 1.45.
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Forest plot of FIB4 for F4 ‐ Studies with cut‐off around 3.25.
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Forest plot of Forns for F2 ‐ Studies with cut‐off around 4.2.
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Summary ROC plot of Forns for F2 ‐ Studies with cut‐off around 4.2. The circles represent individual studies.
The solid circle represents the summary estimate of sensitivity and specificity.
The dotted line represents the 95% confidence regions. 
The dashed line represents the 95% prediction regions.
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Forest plot of Forns for F2 ‐ Studies with cut‐off around 6.9.
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Summary ROC plot of Forns for F2 ‐ Studies with cut‐off around 6.9. The circles represent individual studies.
The solid circle represents the summary estimate of sensitivity and specificity.
The dotted line represents the 95% confidence regions. 
The dashed line represents the 95% prediction regions.
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Forest plot of Forns for F3 ‐ Studies with cut‐off around 6.9.
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FIB4 1.45 ‐ F2
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2. Test
FIB4 3.25 ‐ F2
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3. Test
FIB4 1.45 ‐ F3
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FIB4 3.25 ‐ F3
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5. Test
FIB4 1.45 ‐ F4
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6. Test
FIB4 3.25 ‐ F4
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7. Test
Forns 4.2 ‐ F2
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Forns 6.9 ‐ F2
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Forns 4.2 ‐ F3
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Forns 6.9 ‐ F3
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Forns 4.2 ‐ F4
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Forns 6.9 ‐ F4
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References

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    1. Martinez SM, Fernández‐Varo G, González P, Sampson E, Bruguera M, Navasa M, et al. Assessment of liver fibrosis before and after antiviral therapy by different serum marker panels in patients with chronic hepatitis C. Alimentary Pharmacology & Therapeutics 2011;33(1):138-48. [DOI: 10.1111/j.1365-2036.2010.04500.x] - DOI - PubMed
Matsuura 2018 {published data only}
    1. Matsuura K, Aizawa N, Enomoto H, Nishiguchi S, Toyoda H, Kumada T, et al. Circulating let-7 Levels in Serum Correlate With the Severity of Hepatic Fibrosis in Chronic Hepatitis C. Open Forum Infectious Diseases 2018;5(11):268. [DOI: 10.1093/ofid/ofy268] - DOI - PMC - PubMed
Mossong 2011 {published data only}
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Omran 2018 {published data only}
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Ozel 2015 {published data only}
    1. Ozel BD, Poyrazoğlu OK, Karaman A, Karaman H, Altinkaya E, Sevinç E, et al. The PAPAS index: a novel index for the prediction of hepatitis C-related fibrosis. European Journal of Gastroenterology & Hepatology 2015;27(8):895-900. [DOI: 10.1097/MEG.0000000000000379] - DOI - PubMed
Paranaguá‐Vezozzo 2017 {published data only}
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Portilla 2009 {published data only}
    1. Portilla J, López-Burgos A, Saiz-De-La-Hoya-Zamácola P, Sánchez-Payá J, Bedía-Collantes M, Faraco-Atienzar I, et al. Utility of 2 predictive biochemical models of hepatic fibrosis grade in the prison population with hepatitis C [Utilidad de 2 modelos bioquímicos predictivos del grado de fibrosis hepática en la población penitenciaria con hepatitis C]. Gastroenterología y Hepatología 2009;32(6):387-94. [DOI: 10.1016/j.gastrohep.2009.01.176] - DOI - PubMed
Qian 2019 {published data only}
    1. Qian X, Zheng S, Wang L, Yao M, Guan G, Wen X, et al. Exploring the diagnostic potential of serum golgi protein 73 for hepatic necroinflammation and fibrosis in chronic HCV infection with different stages of liver injuries. Disease Markers 2019;2019:3862024. [DOI: 10.1155/2019/3862024] - DOI - PMC - PubMed
Ramzy 2021 {published data only}
    1. Ramzy I, Fouad R, Salama R, Abdellatif Z, Elsharkawy A, Zayed N, et al. Evaluation of red cell distribution width to platelet ratio as a novel non-invasive index for predicting hepatic fibrosis in patients with chronic hepatitis C. Arab Journal of Gastroenterology 2021;22(1):6-11. [DOI: 10.1016/j.ajg.2020.12.003] - DOI - PubMed
Schmid 2015 {published data only}
    1. Schmid P, Bregenzer A, Huber M, Rauch A, Jochum W, Müllhaupt B, et al, Swiss HIV Cohort Study. Progression of liver fibrosis in HIV/HCV co-infection: a comparison between non-invasive assessment methods and liver biopsy. PLOS One 2015;10(9):e0138838. [DOI: 10.1371/journal.pone.0138838] - DOI - PMC - PubMed
Schmoyer 2020 {published data only}
    1. Schmoyer CJ, Kumar D, Gupta G, Sterling RK. Diagnostic accuracy of noninvasive tests to detect advanced hepatic fibrosis in patients with hepatitis C and end-stage renal disease. Clinical Gastroenterology & Hepatology 2020;18(10):2332-9. [DOI: 10.1016/j.cgh.2020.02.019] - DOI - PubMed
Sebastiani 2008a {published data only}
    1. Sebastiani G, Vario A, Guido M, Alberti A. Performance of noninvasive markers for liver fibrosis is reduced in chronic hepatitis C with normal transaminases. Journal of Viral Hepatitis 2008;15(3):212-8. [DOI: 10.1111/j.1365-2893.2007.00932.x] - DOI - PubMed
Sebastiani 2008b {published data only}
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Sebastiani 2012 {published data only}
    1. Sebastiani G, Halfon P, Castera L, Mangia A, Di Marco V, Pirisi M, et al. Comparison of three algorithms of non‐invasive markers of fibrosis in chronic hepatitis C. Alimentary Pharmacology & Therapeutics 2012;35(1):92-104. [DOI: 10.1111/j.1365-2036.2011.04897.x] - DOI - PubMed
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Sène 2006 {published data only}
    1. Sène D, Limal N, Messous D, Ghillani-Dalbin P, Charlotte F, Thiollière JM, et al. Biological markers of liver fibrosis and activity as non-invasive alternatives to liver biopsy in patients with chronic hepatitis C and associated mixed cryoglobulinemia vasculitis. Clinical Biochemistry 2006;39(7):715-21. [DOI: 10.1016/j.clinbiochem.2006.04.019] - DOI - PubMed
Shaikh 2009 {published data only}
    1. Shaikh S, Memon MS, Ghani H, Baloch GH, Jaffery M, Shaikh K. Validation of three non-invasive markers in assessing the severity of liver fibrosis in chronic hepatitis C. Journal of the College of Physicians and Surgeons Pakistan 2009;19(8):478-82. - PubMed
Shiha 2017 {published data only}
    1. Shiha G, Seif S, Eldesoky A, Elbasiony M, Soliman R, Metwally A, et al. A simple bedside blood test (Fibrofast; FIB-5) is superior to FIB-4 index for the differentiation between non-significant and significant fibrosis in patients with chronic hepatitis C. Hepatology International 2017;11(3):286-91. [DOI: 10.1007/s12072-017-9796-z] - DOI - PubMed
Shiha 2022a {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: a novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Shiha 2022b {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Shiha 2022c {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Shiha 2022d {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Shiha 2022e {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Shiha 2022f {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Shiha 2022g {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Shiha 2022h {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Shiha 2022i {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Shiha 2022j {published and unpublished data}
    1. Shiha G, Soliman R, Mikhail NN, Alswat K, Abdo A, Sanai F, et al. Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C. Hepatology Research 2022;52:165-75. [DOI: 10.1111/hepr.13729] - DOI - PubMed
Silva Junior 2014 {published data only}
    1. Silva Junior RG, Schmillevitch J, Nascimento MD, Miranda ML, Brant PE, Schulz PO, et al. Acoustic radiation force impulse elastography and serum fibrosis markers in chronic hepatitis C. Scandinavian Journal of Gastroenterology 2014;49(8):986-92. [DOI: 10.3109/00365521.2014.909528] - DOI - PubMed
Şirli 2010 {published data only}
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Stauber 2015 {published data only}
    1. Stauber RE, Maieron A, Posch A, Pock H, Streit A, Lackner C. FIB-4, a simple fibrosis test, accurately predicts METAVIR F3/F4 stage in chronic hepatitis C. United European Gastroenterology Journal 2015;3(5S):A519.
Sterling 2006 {published and unpublished data}
    1. Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 2006;43(6):1317-25. [DOI: 10.1002/hep.21178] - DOI - PubMed
Stibbe 2011 {published data only}
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Tachi 2015 {published data only}
    1. Tachi Y, Hirai T, Toyoda H, Tada T, Hayashi K, Honda T, et al. Predictive ability of laboratory indices for liver fibrosis in patients with chronic hepatitis C after the eradication of hepatitis C virus. PLoS One 2015;10(7):e0133515. [DOI: 10.1371/ journal.pone.0133515] - PMC - PubMed
Tanwar 2017 {published data only}
    1. Tanwar S, Trembling PM, Hogan BJ, Parkes J, Harris S, Grant P, et al. Biomarkers of hepatic fibrosis in chronic hepatitis C. Journal of Clinical Gastroenterology 2017;51(3):268-77. [DOI: 10.1097/MCG.0000000000000581] - DOI - PubMed
Toson 2017 {published data only}
    1. Toson ES, Shiha GE, El-Mezayen HA, Samir W, El-Khininy MM. Noninvasive estimation of liver fibrosis in biopsy-proven hepatitis C virus-infected patients: angiogenic fibrogenic link. European Journal of Gastroenterology & Hepatology 2017;29(2):199-207. [DOI: 10.1097/MEG.0000000000000775] - DOI - PubMed
Trang 2008 {published data only}
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Trifan 2009 {published data only}
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Tsukano 2017 {published data only}
    1. Tsukano N, Miyase S, Saeki T, Mizobe K, Iwashita H, Arima N, et al. Usefulness of virtual touch quantification for staging liver fibrosis in patients with hepatitis C, and factors affecting liver stiffness measurement failure compared with liver biopsy. Hepatology Research 2018;48(5):373-82. [DOI: 10.1111/hepr.13041] - DOI - PubMed
Tural 2009 {published data only}
    1. Tural C, Tor J, Sanvisens A, Pérez–Alvarez N, Martínez E, Ojanguren I, et al. Accuracy of simple biochemical tests in identifying liver fibrosis in patients co-infected with human immunodeficiency virus and hepatitis C virus. Clinical Gastroenterology and Hepatology 2009;7(3):339-45. [DOI: 10.1016/j.cgh.2008.11.019] - DOI - PubMed
Udompap 2020 {published data only}
    1. Udompap P, Sukonrut K, Suvannarerg V, Pongpaibul A, Charatcharoenwitthaya P. Prospective comparison of transient elastography, point shear wave elastography, APRI and FIB-4 for staging liver fibrosis in chronic viral hepatitis. Journal of Viral Hepatitis 2020;27(4):437-48. [DOI: 10.1111/jvh.13246] - DOI - PubMed
Usluer 2012 {published data only}
    1. Usluer GA, Erben NU, Aykin N, Dagli O, Aydogdu O, Barut S, et al. Comparison of non-invasive fibrosis markers and classical liver biopsy in chronic hepatitis C. European Journal of Clinical Microbiology & Infectious Diseases 2012;31(8):1873-8. [DOI: 10.1007/s10096-011-1513-6] - DOI - PubMed
Vallet‐Pichard 2007 {published data only}
    1. 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(1):32-6. [DOI: 10.1002/hep.21669] - DOI - PubMed
Wang 2015 {published data only}
    1. Wang CC, Liu CH, Lin CL, Wang PC, Tseng TC, Lin HH, et al. Fibrosis index based on four factors better predicts advanced fibrosis or cirrhosis than aspartate aminotransferase/platelet ratio index in chronic hepatitis C patients. Journal of the Formosan Medical Association 2015;114(10):923-8. [DOI: 10.1016/j.jfma.2015.07.004] - DOI - PubMed
Wang 2017 {published data only}
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Yen 2018 {published data only}
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Yilmaz 2021 {published data only}
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