SEARCH

SEARCH BY CITATION

Abstract

  1. Top of page
  2. Abstract
  3. References

Watch a video presentation of this article

Hepatitis B virus (HBV) infection is a global health challenge, with more than 400 million people chronically infected worldwide. Chronic hepatitis B (CHB), if not abated, may progress to several severe clinical outcomes, including cirrhosis, hepatic decompensation, and hepatocellular carcinoma (HCC). HCC is one of the most pernicious complications of CHB infection because of the associated poor quality of life and abbreviated survival. Many patient, viral, and environmental factors are independently associated with an elevated risk of HCC development, particularly in patients with CHB infection. Different study groups have attempted to generate prediction models, using clinical variables for the estimation of HCC risk in CHB patients.1–4 However, these studies have faced criticism due to hospital-based design, inadequate sample size, complicated risk predictors, and, most importantly, lack of rigorous external validation. A recent study established an HCC risk score (REACH-B [risk estimation for hepatocellular carcinoma in chronic hepatitis B]) using sex, age, serum alanine aminotransferase concentration, hepatitis B e antigen (HBeAg) status, and serum HBV DNA level as the predicting parameters (Table 1).5 This study derived a risk model from 3,584 treatment- and cirrhosis-free CHB patients in a community-based Taiwanese cohort (REVEAL-HBV) and validated its use in composite hospital-based cohorts (N = 1,505) from Hong Kong and Korea. The risk score accurately and reliably estimated HCC risk at 3, 5, and 10 years of follow-up (Table 2). This is the first clinical study to provide firm external validation of the use of an HCC risk prediction tool in a cohort of patients with CHB.

Table 1. The REACH-B Score and Associated 3-Year, 5-Year, and 10-Year Risk of Developing HCC
Risk PredictorRisk Score
Sex 
 Female0
 Male2
Age, years 
 30-340
 35-391
 40-442
 45-493
 50-544
 55-595
 60-656
Alanine aminotransferase level, U/L 
 <150
 15-441
 ≥452
HBeAg status 
 Negative0
 Positive2
HBV DNA level, copies/mL 
 <300 (undetectable)0
 300-9,9990
 10,000-99,9993
 100,000-999,9995
 ≥1064
 HCC Risk
Cumulative Risk ScoreYear 3Year 5Year 10
  1. A CHB patient's cumulative risk score could be summed up using the top part of the table. The cumulative risk score could then be applied to the bottom part of the table to find the corresponding projected HCC risk.

  2. Adapted with permission from The Lancet Oncology.5 Copyright 2011, Lancet Publishing Group.

00.0%0.0%0.0%
10.0%0.0%0.1%
20.0%0.0%0.1%
30.0%0.1%0.2%
40.0%0.1%0.3%
50.1%0.2%0.5%
60.1%0.3%0.7%
70.2%0.5%1.2%
80.3%0.8%2.0%
90.5%1.2%3.2%
100.9%2.0%5.2%
111.4%3.3%8.4%
122.3%5.3%13.4%
133.7%8.5%21.0%
146.0%13.6%32.0%
159.6%21.3%46.8%
1615.2%32.4%64.4%
1723.6%47.4%81.6%
Table 2. Performance Indices of Model Validation of REACH-B Score
 For Predicting HCC at Various Time Points of Follow-up
Year 3Year 5Year 10
  1. Abbreviation: AUROC, area under the receiver operating characteristic curve; CI, confidence interval.

Application of risk score, AUROC (95% CI)   
 All patients in the validation set0.811 (0.790-0.831)0.796 (0.775-0.816)0.769 (0.747-0.790)
 Patients without cirrhosis0.902 (0.884-0.918)0.783 (0.759-0.806)0.806 (0.783-0.828)
Predicted HCC risk and observed risk, correlation coefficient   
 All patients in the validation set0.9730.9420.994
 Patients without cirrhosis0.9750.9910.999

The REACH-B risk score has helped create a gauge for HCC risk assessment. It enables evidence-based clinical management decisions to be made based on a continuum of HCC risk. It can also be used to tailor surveillance patterns for patients according to their personalized risk. Furthermore, because antiviral therapy has the potential to improve liver histology in chronic HBV, it might be anticipated that timely identification of patients who are at high risk of progression to HCC, might, when followed by initiation of antiviral therapy, lead to prolonged survival and improved quality of life. The REACH-B score also provides a platform for physician-patient communication in light of the future risk of developing HCC. A patient's willingness to receive antiviral therapy may be enhanced by understanding their own risk. The risk prediction score might complement clinical practice guidelines in providing function on patient risk stratification. From a public health viewpoint, it might aid in health care resource allocation by bridging the gap between personal risk profiles and population health impact resulting from HCC. Although the REACH-B predictive score was externally validated to be a useful tool for HCC risk estimation, further validation is still needed in patients with different ethnicities, geographical areas, age at infection, genetic background, HBV genotype or species, comorbidities, and exposure to environmental factors such as aflatoxin and alcohol.5-7

One fundamental question is: To which group of hepatitis B surface antigen (HBsAg) carriers can this prediction model be applied? We know that CHB covers a wide spectrum of liver pathology, including minimal hepatitis, chronic hepatitis, fibrosis, advanced fibrosis, and cirrhosis. Because carcinogenesis of HCC is a multistage and multifactorial process, the risk predictors of developing HCC for patients with cirrhosis in whom CHB has progressed to a relatively late stage should be very different from those applied to patients without cirrhosis. A universal risk prediction tool for the whole spectrum of patients would not be reasonable. Studies indicate that the annual risk of developing HCC among CHB patients with cirrhosis is extremely high (3%–5%).8 However, there is currently no HCC risk prediction tool for patients with severe fibrosis and cirrhosis. The derivation cohort of the REACH-B risk score did not include patients with CHB and cirrhosis; therefore, its predictability in this particular group of patients is inherently limited. Based on the original REACH-B report,5 applying the risk score to patients at a more advanced stage of liver disease (such as cirrhosis) diminishes the accuracy and values of the prediction; however, the accuracy and value may be better in patients who are in the early stages of disease. Cirrhosis per se is an important predictor for future development of HCC, and CHB patients with existing cirrhosis need close and timely monitoring and initiation of antiviral therapy. Risk assessment in this group of patients or use of cirrhosis as a variable might be pointless, since all such patients are ‘high risk’.

Another question that has been raised is whether applying the risk score to patients during treatment could forecast the potential decline of risk according to improvement of the risk profile. Because the current HCC risk prediction tools were generated from a natural history cohort without antiviral therapy, the inference of a predicted risk in patients receiving antiviral therapy is inappropriate. Furthermore, since current HCC prediction tools are based on one-time baseline measurement; further validation studies are required to evaluate whether this risk score is applicable for changing risk profiles during follow-up, either spontaneously or through antiviral therapy.

Apart from HCC, other clinical outcomes and milestones of CHB for which an accurate prediction tool would be useful included cirrhosis, seroclearance/seroconversion of HBeAg and HBsAg, seroclearance of HBV DNA, and liver related mortality. Quantitative HBsAg titer as well as genetic markers, might hopefully be incorporated into the current HCC risk score.9, 10 The establishment of a risk prediction tool for various clinical outcomes for patients with chronic HBV infection is still in a preliminary phase. The accomplishment and refinement of these prediction tools may supply CHB patients with a comprehensive individualized management measure based on the risk perspectives.

References

  1. Top of page
  2. Abstract
  3. References
  • 1
    Han KH, Ahn SH. How to predict HCC development in patients with chronic B viral liver disease? Intervirology 2005; 48: 23-28.
  • 2
    Wong VW, Chan SL, Mo F, Chan TC, Loong HH, Wong GL, et al. Clinical scoring system to predict hepatocellular carcinoma in chronic hepatitis B carriers. J Clin Oncol 2010; 28: 1660-1665.
  • 3
    Yang HI, Sherman M, Su J, Chen PJ, Liaw YF, Iloeje UH, et al. Nomograms for risk of hepatocellular carcinoma in patients with chronic hepatitis B virus infection. J Clin Oncol 2010; 28: 2437-2444.
  • 4
    Yuen MF, Tanaka Y, Fong DY, Fung J, Wong DK, Yuen JC, et al. Independent risk factors and predictive score for the development of hepatocellular carcinoma in chronic hepatitis B. J Hepatol 2009; 50: 80-88.
  • 5
    Yang HI, Yuen MF, Chan HL, Han KH, Chen PJ, Kim DY, et al. Risk estimation for hepatocellular carcinoma in chronic hepatitis B (REACH-B): development and validation of a predictive score. Lancet Oncol 2011; 12: 568-574.
  • 6
    Keeffe EB. Risk score for development of HCC: ready for use in practice? Lancet Oncol 2011; 12: 517-518.
  • 7
    Sarin SK, Kumar M. Predictive scores for hepatocellular carcinoma development in chronic hepatitis B virus infection: “does one size fit all?” Gastroenterology 2012; 142: 1038-1040.
  • 8
    Llovet JM, Burroughs A, Bruix J. Hepatocellular carcinoma. Lancet 2003; 362: 1907-1917.
  • 9
    Tseng TC, Liu CJ, Yang HC, Su TH, Wang CC, Chen CL, et al. High levels of hepatitis B surface antigen increase risk of hepatocellular carcinoma in patients with low HBV load. Gastroenterology 2012; 142: 1140-1149.
  • 10
    Zhang H, Zhai Y, Hu Z, Wu C, Qian J, Jia W, et al. Genome-wide association study identifies 1p36.22 as a new susceptibility locus for hepatocellular carcinoma in chronic hepatitis B virus carriers. Nat Genet 2010; 42: 755-758.