Risk scores for hepatocellular carcinoma in chronic hepatitis B

Potential conflict of interest: Nothing to report. See Article on Page 1934 [Correction added on May 8, 2015, after first online publication: reference 8 was added; missing in original publication.] Western hepatocellular carcinoma (HCC) management guidelines suggest that patients at risk for HCC should undergo surveillance. The guidelines provide broad criteria for determining who is at risk for HCC, including patients with cirrhosis, regardless of etiology, and some with hepatitis B above certain age cutoffs. These groups were defined by cost‐effectiveness analyses that determined incidence rates of HCC above which surveillance was deemed cost‐effective. However, common experience shows that only a minority of patients in the risk groups ever develop HCC. There has been considerable research into identifying these at‐risk individuals more precisely so that those not at such a high risk could be excluded from surveillance programs. There are now risk scores that have been developed, some targeted at patients with hepatitis B, others at those with hepatitis C, and yet others included broader populations. Table 1 lists some of the risk scores that have been reported thus far. Table 1 - Risk Scores That Have Been Reported Thus Far Risk Score Variables Population Externally Validated? HBV specific Yang et al. (REACH‐B) Age, ALT, HBeAg status, gender, HBV‐DNA concentration Hepatitis B Yes (only in Asia) Wong et al. (CU‐HCC) Age, albumin, bilirubin, HBV‐DNA concentration, cirrhosis (yes or no) No Yuen et al. (GAG‐HCC) Age, gender, HBV‐DNA concentration cirrhosis, core promoter mutation No Broader patient population El Serag et al. ALT, AFP, age platelets Hepatitis C No Chang et al. Age, gender, platelets, AFP, fibrosis, Diabetes (yes or no) Hepatitis C after SVR No Michikawa et al. Age, gender, alcohol consumption, BMI, diabetes (yes or no), coffee consumption, hepatitis B, hepatitis C General population No Flemming et al. (ADRESS‐HCC) Age, Diabetes, Race, Etiology of liver disease, Sex, Severity (CTP score) Liver transplant waitlist Yes Abbreviations: AFP, alpha‐fetoprotein; BMI, body mass index; CTP, Child‐Turcotte‐Pugh; SVR, sustained virological response. Among the models for hepatitis B virus (HBV) patients, the most robust model to date may be the REACH‐B score.1 This was derived from participants who were not known to have cirrhosis in the REVEAL study.2 The score has been externally validated in other Asian populations.1 However, the score has not been evaluated in other populations where the viral genotype, host genetics, or environmental exposure profile may be dissimilar to the REACH‐B population. The other models for HBV patients take into account cirrhosis and/or degree of hepatic decompensation; however, they have not been externally validated.3 More recently, other scores have been developed that apply to hepatitis C and other populations.5 Some of the variables in some, but not all, models include presence and severity of chronic underlying liver disease (e.g., assessed by Child‐Pugh score), age, gender, race, and diabetes. These considerations raise a question of whether a universal risk score would be practically applied in the general population with vastly heterogeneous levels of HCC risk. In this issue of Hepatology, an article by Hung et al.8 presents another model for assessing HCC risk. They analyzed a large number (n = 12,377) of Taiwanese subjects from three different observational cohorts, heavily enriched with subjects with HBV infection. They proposed four models, aiming to develop a universal tool to stratify a general population for mass HCC screening. The first only included age, sex, and alanine aminotransferase (ALT), whereas the second model added history of chronic liver disease (CLD), family history of HCC, and smoking. The third model incorporated all these variables plus hepatitis B surface antigen (HBsAg) data, and the fourth model included both HBsAg and antibodies to the hepatitis C virus (anti‐HCV) data. Models 3 and 4 performed better than those with less information (models 1 and 2). They also use a decision curve analysis (DCA) to determine the level of risk that is associated with benefit to surveillance. Other risk scores developed to date assess predictive accuracy using odds ratios and receiver operating curves without explicitly considering the level of benefit that is sufficient to make the intervention worthwhile. In the work by Hung et al.,12 an important output is the net benefit, a weighted difference between true‐ and false‐positive classifications. When this was plotted against the threshold probability of 2% for developing HCC over 10 years, their model was superior to the current age‐based criteria for subjects without cirrhosis over an age range of 20‐60 years. This study raises the question of whether it is plausible to apply a scheme to measure HCC risk in the general population. The majority of at‐risk subjects can be characterized by their underlying liver disease. In the data by Hung et al. (Table 3), the hazard ratio (HR) associated with being positive for HBsAg or anti‐HCV (HR = 23.3) was an order of magnitude greater than other significant variables, such as male sex (HR = 3.1) or family history of HCC (2.1). The approach seemingly advocated by Hung et al. is to apply their score to the general population (e.g., patients observed at primary care clinic) and enter subjects with certain scores into surveillance. In the United States and other Western countries, the approach is to identify patients with CLD who are at risk of developing HCC, rather than approach the general population. An important difference in feasibility between the two approaches may be the incidence of HCC in the general population and the public awareness of HCC. In Taiwan, where HCC is common, it may be useful for primary care physicians to have a simple scoring system to identify patients for HCC surveillance. In areas with lower HCC incidence, it is much less likely that even a simple scoring system will be utilized in primary care, especially if the decision is based on a remote outcome, such as development over a 10‐year horizon. Ultimately, this is a societal decision and involves issues of cost‐efficacy and health care policy. The threshold of accepted benefit may also be different in regions with difference burden of HCC. Nonetheless, there is still a need for a better decision tool for patients cared for in specialty practices, such as hepatology and gastroenterology. The American Association for the Study of Liver Diseases (AASLD) guideline recommends surveillance for patients with cirrhosis (estimated annual incidence >1.5%). HBV or hepatitis C virus (HCV) patients with cirrhosis clearly belong in this group. However, risk estimation in patients with other etiologies of cirrhosis, such as autoimmune/cholestatic disease or nonalcoholic steatohepatitis, is not well established. Some of the models shown in Table 1, such as those by Flemming and Michikawa, address this point; however, more robust and practical measures may be helpful. Another group in which surveillance is recommended is subjects without cirrhosis with HBV infection. Here, the results by Hung et al. may be useful in identifying patients who should undergo surveillance—those with an estimated risk greater than 0.2% per year. This would include certain patients of ages between 20 and 39 who do not meet AASLD criteria (Figs. 4 and 5). The weakness of their score is that they do not fully utilize the clinical information that would be available to the specialist, such as HBV‐DNA concentration or hepatitis B envelope antigen (HBeAg) status, that other models, such as REACH‐B score, take advantage of. However, in contrast to the REACH‐B model, the explicit consideration of the benefit of surveillance by the DCA approximates the value decision entailed in the AASLD guideline. Nonetheless, a decision still has to be made as to what amount of benefit makes surveillance worthwhile. Finally, all of the models, including the one by Hung et al., were developed in cohorts of patients with viral hepatitis not treated with antiviral agents. Thus, interesting though these kinds of studies are, they have little applicability for those with viral hepatitis undergoing or after treatment. No scoring system is going to have 100% predictability. All the available risk scores perform well, although some were better than others. At present, at least for those with hepatitis B or cirrhosis, the risk scores are more useful for excluding patients from surveillance, rather than including them, given that the current guidelines are broad and more inclusive. Thus, it may not make much difference which score is used, but some care is needed to ensure that the score used most closely matches the population from which the patient comes. For example, in North America for patients with advanced liver disease, the score described by Flemming et al. is most likely to be the most accurate, whereas for hepatitis B in Asians one of the Asian‐derived scores are more likely to be appropriate. However, given the level of uncertainty about the applicability of these scores, at least in Europe and North America, physicians may just choose to be more inclusive and use the AASLD/European Association for the Study of the Liver guidelines.

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