Prediction models of long-term Cirrhosis and hepatocellular carcinoma risk in chronic hepatitis B patients: Risk scores integrating host and virus profiles

Authors


  • Potential conflict of interest: Dr. Batrla-Utermann owns stock in Roche. Dr. Iloeje owns stock in Bristol-Myers Squibb. None of the funding organizations played a role in the design and conduct of this study; collection, management, analysis, and interpretation of the data; or preparation, review, and approval of the article.

  • Supported by grants from the Department of Health, Executive Yuan, Taipei, Taiwan; Bristol-Myers Squibb Co., USA; Academia Sinica, Taipei, Taiwan; Roche Diagnostics Ltd., Switzerland; and the National Health Research Institutes (NHRI-EX98-9806PI), Chunan, Taiwan. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the article.

Abstract

Integrating host and HBV characteristics, this study aimed to develop models for predicting long-term cirrhosis and hepatocellular carcinoma (HCC) risk in chronic hepatitis B virus (HBV) patients. This analysis included hepatitis B surface antigen (HBsAg)-seropositive and anti-HCV-seronegative participants from the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer in HBV (R.E.V.E.A.L.-HBV) cohort. Newly developed cirrhosis and HCC were ascertained through regular follow-up ultrasonography, computerized linkage with national health databases, and medical chart reviews. Two-thirds of the participants were allocated for risk model derivation and another one-third for model validation. The risk prediction model included age, gender, HBV e antigen (HBeAg) serostatus, serum levels of HBV DNA, and alanine aminotransferase (ALT), quantitative serum HBsAg levels, and HBV genotypes. Additionally, the family history was included in the prediction model for HCC. Cox's proportional hazards regression coefficients for cirrhosis and HCC predictors were converted into risk scores. The areas under receiver operating curve (AUROCs) were used to evaluate the performance of risk models. Elder age, male, HBeAg, genotype C, and increasing levels of ALT, HBV DNA, and HBsAg were all significantly associated with an increased risk of cirrhosis and HCC. The risk scores estimated from the derivation set could accurately categorize participants with low, medium, and high cirrhosis and HCC risk in the validation set (P < 0.001). The AUROCs for predicting 3-year, 5-year, and 10-year cirrhosis risk ranged 0.83-0.86 and 0.79-0.82 for the derivation and validation sets, respectively. The AUROC for predicting 5-year, 10-year, 15-year risk of HCC ranged 0.86-0.89 and 0.84-0.87 in the derivation and validation sets, respectively. Conclusion: The risk prediction models of cirrhosis and HCC by integrating host and HBV profiles have excellent prediction accuracy and discriminatory ability. They may be used for clinical management of chronic hepatitis B patients. (Hepatology 2013;58:546-554)

Abbreviations
ALT

alanine aminotransferase

AUROC

area under receiver operating curve

HBV

hepatitis B virus

HBeAg

HBV e antigen

HBsAg

hepatitis B surface antigen

HCC

hepatocellular carcinoma

R.E.V.E.A.L.-HBV

Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer in HBV.

Chronic hepatitis B is a significant global health threat with more than 350 million affected people.[1] The Asian-Pacific region is a hyperendemic area of chronic hepatitis, and a major cause of endstage liver diseases including cirrhosis and hepatocellular carcinoma (HCC).[2] Globally, at least one-third of liver cirrhosis was attributable to chronic hepatitis B,[3] and a significant proportion of chronic hepatitis B virus (HBV) infections eventually progress to HCC.[4, 5]

The presence of hepatitis B surface antigen (HBsAg) in serum for 6 or more months remains a useful biomarker for patients with chronic HBV infection. HBsAg is a component of the HBV external envelope. In addition to intact infectious viral particles, the blood of chronic hepatitis B patients also contains noninfectious filamentous and spherical particles, consisting only of an outer coat containing HBsAg.[6] Recently, the quantitative serum HBsAg level has been suggested as an indicator of response to antiviral treatment.[7] The serum HBsAg level is dynamic, and its correlation with the serum HBV DNA level seems to change in different phases of the natural history of chronic hepatitis B.[8, 9] A recent hospital-based study showed that quantitative serum HBsAg levels was one of the determinants of HCC development and liver disease progression among patients with low serum HBV DNA levels.[10, 11] The long-term predictability of quantitative serum HBsAg levels for cirrhosis and HCC for asymptomatic HBV carriers remains to be investigated.

The HBV e antigen (HBeAg) and serum levels of alanine aminotransferase (ALT) and HBV DNA and HBV genotype have been well documented as risk predictors of cirrhosis and HCC in chronic hepatitis B patients.[12-19] An easy-to-use risk prediction tool for chronic disease progression is useful for clinical consultation and management. Recently, various risk scores to predict the risk of HCC for chronic HBV carriers have been reported.[20-22] Nomograms derived from risk functions have recently been published for a quick check of HBV-related HCC risk.[21] However, the prediction model of cirrhosis risk in chronic hepatitis B patients has never been developed. As cirrhosis may result in liver failure and HCC, the risk models for cirrhosis by integrating host and HBV profiles may help the appropriate intervention of cirrhosis and prevention or early detection of HCC. In addition, it will be interesting to integrate the new biomarker, quantitative serum HBsAg levels, into the risk prediction models for cirrhosis and HCC.

The community-based Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer in HBV (R.E.V.E.A.L.-HBV) study enrolled a chronic hepatitis B patient cohort without antiviral treatment. In this analysis, we aimed to 1) elucidate the associations of quantitative HBsAg levels and the risk for cirrhosis and HCC, and 2) develop prediction models for long-term cirrhosis and HCC risk through integrating host and HBV profiles collected in the R.E.V.E.A.L.-HBV study.[16, 18]

Materials and Methods

Study Cohort

The enrollment of the prospective R.E.V.E.A.L.-HBV study cohort has been described.[16, 18] A total of 23,820 male and female residents age 30-65 years old were enrolled from seven townships in Taiwan in 1991-1992. They agreed to participate with written informed consent for questionnaire interview, regular health examination and blood collection, and data linkage of computerized health status profiles. The demographic data for residents who did not participate in this study were quite similar to those of residents who agreed to participate. The study protocol was approved by the Institutional Review Board of National Taiwan University College of Public Health.

Data Collection and Laboratory Testing

All participants were interviewed using a structured questionnaire by well-trained public health nurses. Inquiry information included sociodemographic characteristics, dietary habits, habits of cigarette smoking and alcohol consumption, and personal and family history of major diseases. At enrollment a 10-mL blood sample was collected from each participant using disposable needles and vacuum syringes. Serum samples were separated by centrifugation and stored at −70°C until subsequent serological and biochemical testing. Laboratory tests of HBsAg, HBeAg, anti-HCV, ALT, and HBV DNA were performed using commercial kits, and the HBV genotype was determined by melting curve analysis as described.[16, 18] The quantification of serum HBsAg levels was determined by Elecsys HBsAg II Quant assay (Roche Diagnostics, Mannheim, Germany), which has a lower limit of detection of 0.05 IU/mL.

Participant Selection and Random Allocation

Among 4,155 participants seropositive for HBsAg at study entry, 3,579 had adequate serum samples for tests of serum HBV DNA and HBsAg levels. A total of 168 participants were seropositive for anti-HCV; 67 with liver cirrhosis history at enrollment, two affected with HCC within a half year, and two died within 6 months after study entry were excluded. There were 3,342 participants included for the analyses of liver cirrhosis (excluding anti-HCV seropositives, prevalent cirrhosis cases, and those followed less than 6 months). For the analyses of HCC, a total of 3,340 participants were included (excluding the two prevalent HCC cases additionally). The participants were randomly allocated into model derivation and validation sets in 2:1 ratio to develop risk prediction models for cirrhosis and HCC. In other words, host and HBV profiles of participants in the derivation set were utilized to generate prediction models, and the profiles of other participants in the validation set were utilized to assess the predictive accuracy.

Ascertainment of Cirrhosis and HCC

Study participants were examined by high-resolution real-time abdominal ultrasonography at study entry and follow-up examinations, which were performed by certified gastroenterologists and interpreted according to a standardized protocol set by a specialist panel. Cirrhosis was determined based on a quantitative scoring system that was derived from the appearance of liver surface (normal, irregular, undulated), liver parenchymal texture (normal, heterogeneous, coarse), intrahepatic blood vessel size (normal, obscure, narrowing), and splenic size (normal, enlarged). To complete the ascertainment of cirrhosis, the computerized data linkage with the National Health Insurance profiles (to June 30, 2004) in Taiwan was also performed. The medical records of identified cirrhosis cases were further reviewed by gastroenterologists using a standard case abstraction form.[13]

Newly developed HCC cases were ascertained by follow-up health examinations including ultrasonography and α-fetoprotein testing, or by computerized data linkage with the National Cancer Registry in Taiwan. Data linkage with the National Death Certification System was also performed to ensure complete ascertainment of HCC (to December 31, 2008). The participants received ultrasonographic examinations performed by board-certified gastroenterologists during follow-up. Once HCC was suspected sonographically, the patients were referred for confirmation based on the criteria of 1) histopathology; 2) two imaging techniques (abdominal ultrasonography, angiogram, or computed tomography); or 3) one imaging technique plus a serum α-fetoprotein level of 400 ng/mL or greater.[23] To ensure complete ascertainment, computerized linkage with National Death Certification profiles was also used to identify deaths from HCC.

Statistical Analysis

The person-years of follow-up were calculated from the enrollment date to the diagnosis date of cirrhosis or HCC, date of death, or the last date of computerized data linkage with the national health profiles (June 30, 2004 for cirrhosis and December 31, 2008 for HCC), whichever came first. The incidence of cirrhosis or HCC was derived by dividing the number of incident cirrhosis or HCC cases by the person-years of follow-up. Cox's proportional hazards models were used to estimate the crude and multivariate-adjusted hazard ratios (HR) with 95% confidence intervals (CI) for risk predictors of cirrhosis. Statistical significance levels were determined by two-sided P = 0.05.

To develop risk prediction models for endstage liver disease outcomes, the regression coefficients of predictors were converted into integer risk scores by rounding the quotient of dividing the regression coefficients by the regression coefficient for 5-year increase in age, allowing the integer risk score for 5-year increase in age as one.[24] The predicted risks for cirrhosis or HCC were estimated by the sum of risk scores by the equation: inline image, where P0 was the baseline disease-free probability, βi was the regression coefficient for the ith variables (Xi), and the Mi denoted the mean level of Xi.[21, 24] To evaluate the predictive accuracy of the risk prediction models, the AUROCs were calculated. To evaluate the discriminatory ability of the risk models, the observed cumulative cirrhosis and HCC risk of three groups with low, medium, and high sum risk scores in the validation set were compared. To assure each group had sufficient cirrhosis and HCC cases, the 25th and 75th percentiles of sum risk scores of patients affected with newly developed liver cirrhosis were used as the cutoff values. All of the statistical analyses were performed with SAS v. 9.1 (SAS Institute, Cary, NC).

Results

 After 39,016 person-years of follow-up, there were 327 newly developed cirrhosis cases ascertained, giving an incidence rate of 838.1 per 100,000 person-years. The incidence rates of cirrhosis by risk predictor at study entry are shown in Table 1. Older age, male gender, habits of cigarette smoking, HBeAg seropositivity, elevated serum levels of ALT, HBV DNA and HBsAg, and HBV genotype C were associated with an increased liver cirrhosis risk. All these risk predictors except cigarette smoking remained statistically significant after multivariate adjustment. There were 164 incident HCC cases after 53,551 person-years of follow-up, giving an incidence of 306.3 per 100,000 person-years. In addition to the predictors of cirrhosis mentioned above, the family history of HCC was an important predictor of HCC. The baseline predictors for HCC are shown in Table 2.

Table 1. Number of Participants, Numbers and Incidence Rates of Liver Cirrhosis, and Crude Hazard Ratios by Baseline Liver Cirrhosis Predictors
Baseline Liver Cirrhosis PredictorsNumber of ParticipantsCirrhosis CasesPerson-Years of Follow-upIncidence Rate (Per 100,000 Person-Years)Crude Hazard Ratio (95% Confidence Interval)P Value
  1. a

    Three missing data.

  2. b

    Seven missing data.

  3. c

    Restricted to participants with detectable HBV DNA.

Sex      
Female13107115823448.71.00 
Male2032256231931103.82.51 (1.93-3.26)<0.001
Age, year      
30-3911478213826593.11.00 
40-499358811089793.61.35 (1.00-1.82)0.05
50-651260157141001113.51.91 (1.46-2.50)<0.001
Cigarette smokinga      
No224920126630754.81.00 
Yes1090126123461020.61.37 (1.10-1.71)0.006
Alcohol consumptionb      
No295428134631811.41.00 
Yes3814643101067.21.33 (0.97-1.82)0.07
Hepatitis B e antigen      
Seronegative283820833562619.81.00 
Seropositive50411954542181.83.60 (2.87-4.51)<0.001
Levels of ALT (IU/L)      
< 15215415225623593.21.00 
15-44999131114501144.11.97 (1.56-2.48)<0.001
≥ 451894419422265.33.96 (2.83-5.55)<0.001
Level of HBsAg (IU/mL)      
<1008823710550350.71.00 
102−9999577611232676.61.96 (1.32-2.90)<0.001
≥1031503214172341241.73.60 (2.54-5.10)<0.001
Level of HBV DNA (copies/mL)      
<10419008622803377.21.00 
104−106893105103231017.12.74 (2.06-3.64)<0.001
≥10654913658902309.16.33 (4.83-8.29)<0.001
HBV genotypec      
Genotype B or B+C148013017370748.41.00 
Genotype C71813280381642.22.21 (1.74-2.82)<0.001
Table 2. Number of Participants, Numbers and Incidence Rates of Hepatocellular Carcinoma, and Crude Hazard Ratios by Baseline Hepatocellular Carcinoma Predictors
Baseline Hepatocellular Carcinoma PredictorsNumber of ParticipantsHCC CasesPerson-Years of Follow-upIncidence Rate (Per 100,000 Person-Years)Crude Hazard Ratio (95% Confidence Interval)PValue
  1. a

    Three missing data.

  2. b

    Seven missing data.

  3. c

    Restricted to participants with detectable HBV DNA.

Sex      
Female13103421535157.91.00 
Male203013032016406.12.60 (1.78-3.79)<0.001
Age, year      
30-3911472119099110.01.00 
40-499354515286294.42.70 (1.61-4.52)<0.001
50-6512589819166511.34.79 (2.99-7.68)<0.001
Cigarette smokinga      
No22489236620251.21.00 
Yes10897116880420.61.70 (1.25-2.32)<0.001
Alcohol consumptionb      
No295313147635275.01.00 
Yes380325819549.92.02 (1.37-2.97)<0.001
Family history of hepatocellular carcinoma      
No318514351153279.61.00 
Yes155212398875.83.17 (2.00-5.01)<0.001
Hepatitis B e antigen      
Seronegative28369145736199.01.00 
Seropositive504737814934.24.77 (3.50-6.49)<0.001
Levels of ALT (IU/L)      
< 1520535833274174.31.00 
15-4410987517506428.42.45 (1.74-3.46)<0.001
≥ 451893127701119.36.54 (4.23-10.13)<0.001
Level of HBsAg (IU/mL)      
<100881121414284.91.00 
102−9999564115263268.63.20 (1.68-6.09)<0.001
≥103150311124145459.75.44 (3.00-9.87)<0.001
Level of HBV DNA (copies/mL)      
<10418993230853103.71.00 
104−1068934814308335.53.27 (2.09-5.12)<0.001
≥1065488483891001.39.92 (6.60-14.91)<0.001
HBV genotypec      
Genotype B or B+C14796423747269.51.00 
Genotype C7187811333688.32.56 (1.83-3.56)<0.001

The cumulative risk for cirrhosis was 4.8%, 8.8%, and 16.2% for participants with serum HBsAg levels <100, 100-999, and ≥1,000 IU/mL, respectively (P < 0.001). On the other hand, the cumulative risk for HCC was 1.4%, 4.5%, and 9.2% for those with serum HBsAg levels <100, 100-999, and ≥1000 IU/mL, respectively (P < 0.001). The multivariate-adjusted HRs of cirrhosis were 1.68 (1.12-2.54) and 2.20 (1.48-3.27) for serum levels of HBsAg 100-999 and ≥1000 IU/mL comparing those with HBsAg levels <100 IU/mL as a reference group (P for trend <0.001). For HCC, the multivariate-adjusted HRs were 2.83 (1.55-5.18) and 4.06 (2.24-7.36), respectively, for HBsAg levels 100-999 and ≥1000 IU/mL using HBsAg levels <100 IU/mL as a comparison group (P for trend <0.001).

We stratified participants seropositive or seronegative for HBeAg in further analyses. Among participants seronegative for HBeAg, serum levels of HBsAg were significantly associated with cirrhosis and HCC in a dose-response manner (P for trend <0.001). The dose-response relationship was only observed in the participants with serum HBV DNA levels <106 copies/mL. In contrast, the serum HBsAg levels were not significantly associated with cirrhosis or HCC among participants who were seropositive for HBeAg (P = 0.80 and P = 0.25). There was no interaction relationship between serum levels of HBsAg and HBV DNA for the risk of cirrhosis and HCC between those seronegative and seropositive for HBeAg (all P > 0.05).

Derivation of Risk Prediction Models

The risk predictors of cirrhosis and HCC at study entry were comparable between participants randomly allocated into model derivation set and validation set (P > 0.05). All risk predictors included in the risk prediction model were statistically significantly associated with cirrhosis and HCC in Cox's proportional hazards regression analyses (P < 0.05). The regression coefficients of predictors in the risk prediction model were converted into integer risk scores as shown in Tables 3 and 4. The sum risk scores ranged from 0-26 in the prediction model for cirrhosis and 0-19 for the prediction model for HCC, respectively. The nomograms for predicted cirrhosis and HCC risk for various sum risk scores are shown in Fig. 1A,B. Participants with larger sum risk scores had greater predicted risks for cirrhosis and HCC. The cirrhosis risk ranged from 0.08%-43.15% for 3-year, 0.13%-60.11% for 5-year, and 0.36%-91.98% for 10-year. For the HCC risk, it ranged from 0.01%-36.19% for 5-year, 0.03%-79.72% for 10-year, and 0.07%-98.16% for 15-year.

Figure 1.

Nomogram for the predicted risk of (A) liver cirrhosis (risk score <11 for low-risk, 11-16 for medium-risk, and ≥17 for high-risk groups) and (B) HCC (risk score <9 for low-risk, 9-12 for medium-risk, and ≥13 for high-risk groups).

Table 3. Regression Coefficients and Integer Risk Scores of Baseline Liver Cirrhosis Predictors Estimated from the Derivation Set
Baseline Liver Cirrhosis PredictorRegression CoefficientRisk ScorePValue
Age (each 5 years increment)0.251<0.001
Sex   
FemaleReference0 
Male0.994<0.001
Levels of ALT (IU/L)   
< 15Reference0 
15-440.2510.10
≥ 450.7130.001
HBeAg/HBV DNA/HBsAg/Genotype   
Negative/<104/<100/any typeReference0 
Negative/<104/100-999/any type0.7630.10
Negative/<104/≥1000/any type0.9740.02
Negative/104−106/<100/any type1.1650.06
Negative/104−106/100-999/any type1.285<0.001
Negative/104−106/≥1000/any type1.717<0.001
Negative/≥106/any level/B or B+ C1.767<0.001
Negative/≥106/any level/C3.2613<0.001
Positive/any level/any level /B or B+C1.767<0.001
Positive/any level/any level /C2.6410<0.001
Table 4. Regression Coefficients and Integer Risk Scores of Baseline Hepatocellular Carcinoma Predictors Estimated From the Derivation Set
Baseline Hepatocellular Carcinoma PredictorRegression CoefficientRisk ScoreP Value
Age (each 5 years increment)0.461<0.001
Sex   
FemaleReference0 
Male0.912<0.001
Levels of ALT (IU/L)   
< 15Reference0 
15-440.3610.10
≥ 450.7620.01
Family history of hepatocellular carcinoma   
NoReference0 
Yes0.9820.001
HBeAg/HBV DNA/HBsAg/Genotype   
Negative/<104/<100/any typeReference0 
Negative/<104/100-999/any type0.8220.13
Negative/<104/≥1000/any type1.0720.04
Negative/104−106/<100/any type1.4230.04
Negative/104−106/100-999/any type1.4530.005
Negative/104−106/≥1000/any type1.784<0.001
Negative/≥106/any level/B or B+ C2.455<0.001
Negative/≥106/any level/C3.097<0.001
Positive/any level/any level /B or B+C2.706<0.001
Positive/any level/any level /C3.377<0.001

Validation of Risk Prediction Model

In the evaluation of predictive accuracy of the risk model, the AUROCs for predicting 3-year, 5-year, and 10-year cirrhosis risk in the derivation set were 0.86, 0.86, and 0.83, indicating the sum risk scores had a satisfactory to high validity for the liver cirrhosis risk prediction. The AUROCs for predicting 3-year, 5-year, and 10-year cirrhosis risk in the validation set were 0.79, 0.80, and 0.82. For the risk prediction model of HCC, the AUROC was 0.89, 0.85, and 0.86 for the 5-year, 10-year, 15-year predicted risk in the derivation set and 0.84, 0.86, and 0.87 for the 5-year, 10-year, 15-year predicted risk in the validation set.

In the evaluation of the discriminatory ability of the risk model in the validation set, participants affected with newly developed cirrhosis and HCC were found to have significantly higher sum risk scores than those who were unaffected (P < 0.001). Participants in the validation set were categorized by their sum risk scores into low-, medium-, and high-risk groups. The observed cumulative cirrhosis and HCC risks of the three groups are compared in Fig. 2A,B. The observed cumulative risk curves for the three predicted risk groups were all significantly different (P < 0.001).

Figure 2.

Cumulative (A) liver cirrhosis and (B) HCC risk by sum risk scores in the validation set.

Discussion

 HBeAg seropositivity, elevated serum ALT and HBV DNA levels, and HBV genotype C have been previously found to be long-term risk predictors of cirrhosis and HCC.[12-19]

In the natural course of HBV infection, the HBsAg loss occurs with an annual rate of 0.5%-2.3%.[25-29] The seroclearance of HBsAg is considered a cure for HBV infection, and patients with HBsAg loss had a favorable clinical outcome.[30] The quantification of serum HBsAg levels was first proposed to monitor the treatment of chronic hepatitis B in 1994.[31] Recently, new quantitative HBsAg assays have been developed with high reproducibility and relatively low cost.[32, 33] Quantitative HBsAg levels could predict the seroclearance of HBsAg in HBeAg seroconverters[29] or HBeAg-seronegative patients with low viral loads.[34]

Combined quantitative HBsAg and HBV DNA levels had accurate predictability to identify inactive carriers (HBeAg seronegatives with persistent HBV DNA ≤2,000 IU/mL).[35] In our study, we retrieved the stored serum samples for testing the new seromarkers, which has been found to be associated with clinical outcomes in chronic hepatitis B patients.[10] Based on clinical guidelines, HBV DNA measurement is essential for the diagnosis, decision to treat, and subsequent monitoring of patients.[36] In this analysis, quantitative serum HBsAg levels were associated with the development of cirrhosis and HCC in a dose-response manner, particularly for those with low HBV DNA (<106 copies/mL) (P < 0.001). This finding suggests that quantification of serum HBsAg levels may provide valuable information for clinical decisions in patients with low viral loads.[10, 11] Moreover, the findings also indicated the importance of lowering serum HBsAg levels in those who already have low serum HBV DNA levels. Clinical trials on therapy to simultaneously lower serum levels of both HBsAg and HBV DNA are recommended. These findings suggest that infectious virions and noninfectious HBsAg particles may have their own unique mechanism of inducing endstage liver diseases.

Integrating the characteristics of host and virus, we developed a prediction model for estimating long-term cirrhosis and HCC risks among chronic hepatitis B patients. The models generated from the derivation set had satisfactory accuracy and discriminatory ability in our internal validation set. Our study suggests the utilization of quantitative serum HBsAg levels in participants with low serum HBV DNA levels, and the application of HBV genotype in participants with high serum HBV DNA levels to refine the estimation of cirrhosis and HCC risk. This implies that different risk factors are involved in different phases of HBV infection (i.e., immune tolerance, immune clearance, or HBeAg-negative phase). Whether the predictability of quantitative HBsAg and HBV genotype varies in different phases of HBV infection needs further evaluation using a larger natural history cohort through collaborative studies. The prediction accuracy of our models should be further evaluated in external cohorts, particularly in clinical patients. A risk prediction model for HCC derived from the R.E.V.E.A.L.-HBV cohort was externally validated by a large multicenter cohort,[37] suggesting the potential of our predictive tools for clinical applications.

Our study showed that the associations of serum HBsAg levels and cirrhosis and HCC among participants with seronegative HBeAg and low HBV DNA. During the natural course of HBV infection, the seromarkers may be dynamic and the changing patterns should be associated with clinical liver outcomes. To incorporate not only the baseline values of a seromarker, but also values during follow-up may increase the predictability of risk models we have developed. However, for clinical consultations it is helpful to provide information to patients based on only a one-shot measurement.

The gold standard for the diagnosis of cirrhosis is liver biopsy. However, it is not suitable for longitudinal monitoring with repeated tests over time. Our study ascertained cirrhosis cases by abdominal ultrasonography, which is more practical for asymptomatic HBV carriers living in the community. Although the risk of cirrhosis in our study might thus be underestimated, the nondifferential misclassification would result in the underestimation of HRs for liver cirrhosis risk predictors. In other words, the risk for cirrhosis may thus be conservatively estimated.

The generalizability of the risk prediction model for younger or older patients should be further evaluated. Most chronic hepatitis B patients in Taiwan were infected by HBV in early childhood, and the incidence and determinants of cirrhosis and HCC may have geographical variation. The application of our prediction model to chronic hepatitis B patients in Western countries, where most carriers are infected in adulthood with HBV genotypes other than B or C, also needs further validation.

In conclusion, we incorporated host and HBV profiles to develop risk prediction models for cirrhosis and HCC, which had excellent prediction accuracy and discriminatory ability. The models may provide valid information for physicians to identify patients who need intensive care and frequent periodic surveillance for liver diseases.

Acknowledgment

We thank all other members of R.E.V.E.A.L.-HBV Study Group: National Taiwan University Hospital: C.Y. Hsieh, H.S. Lee, P.M. Yang, C.H. Chen, J.D. Chen, S.P. Huang, C.F. Jan. National Taiwan University: T.H.H. Chen. National Defense Medical Center: C.A. Sun. Taipei City Psychiatric Center: M.H. Wu. Tzu Chi University: S.Y. Chen. Shin Kong Wu Ho-Su Memorial Hospital: K.E. Chu. Huhsi Health Center, Penghu County: S.C. Ho, T.G. Lu. Provincial Penghu Hospital: W.P. Wu, T.Y. Ou. Sanchi Health Center, Taipei County: C.G. Lin. Provincial Chutung Hospital: K.C. Shih. Provincial Potzu Hospital: W.S. Chung, C. Li. Kaohsu Health Center, Pingtung County: C.C. Chen. Paihsa Health Center, Penghu County: W.C. How.

Author Contributions

Dr. Chien-Jen Chen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: M.-H.L., H.-I.Y., C.-J.C. Acquisition of data: M.-H.L., H.-I.Y., C.-L.J., S.-L.Y., C.-J.C. Analysis and interpretation of data: M.-H.L., H.-I.Y., C.-J.C. Drafting of the article: M.-H.L. Critical revision of the article for important intellectual content: M.-H.L., H.-I.Y., J.L., R.B.-U., C.-L.J., U.H.I., S.-N.L., L.-Y.W., S.-L.Y., C.-J.C. Obtained funding: C.-J.C. Administrative, technical, or material support: M.-H.L., H.-I.Y., J.L., R.B.-U., C.-L.J., U.H.I., S.-N.L., L.-Y.W., S.-L.Y., C.-J.C. Study supervision: C.-J.C.

Ancillary