key: cord-0803647-jgraq7kq
authors: Sinharay, Ricky; Grant, Andrew J.; Rivett, Lucy; Blackwell, Rebecca; Mells, George; Gelson, William
title: Assessing efficacy of hepatocellular carcinoma prediction scores to prioritise hepatitis B surveillance in the COVID‐19 era
date: 2021-02-24
journal: GastroHep
DOI: 10.1002/ygh2.443
sha: da3c60149b1db98ff3c754c176fd7b8bf50eff8a
doc_id: 803647
cord_uid: jgraq7kq

OBJECTIVE: An estimated 250 million people worldwide are chronically infected with hepatitis B virus (HBV), the leading cause of hepatocellular carcinoma (HCC) globally. The novel Sars‐cov2 virus continues to spread at an alarming rate, and with guidance at the onset of the pandemic recommending the deferral of HCC surveillance, the implications on liver cancer care are now emerging and highlight the urgent need for reorganisation of services. METHODS: We analysed how five HCC risk prediction scores could aid stratification of patients with chronic HBV. We calculated scores using parameters measured from 3 years prior (where available, n = 17) and at the time of HCC diagnosis in all adult patients with chronic HBV diagnosed with HCC (n = 46), and controls (n = 100). We compared the number of patients requiring cancer surveillance according to each score and regional surveillance guidance. RESULTS: The aMAP score had the highest discriminatory performance in HCC risk prediction at 3 years (area under receiver‐operating characteristic curve (auROC) of 0.824), followed by the mREACH B score (auROC of 0.719), and mPAGE B score (auROC of 0.742). However, only the mREACH B score had a negative predictive value (NPV) >99%. Applying the mREACH B score to our HBV cohort identified 11 patients requiring HCC surveillance, compared with 62 under current guidelines. CONCLUSION: The use of HCC risk prediction scores could streamline the surveillance of patients with chronic HBV at a time of extremely limited resources. Overall, the mREACH B score had both a strong discriminatory performance and a high NPV, thus safely identifying low risk patients not requiring surveillance.

An estimated 250 million people worldwide are chronically infected with hepatitis B virus (HBV), the leading cause of hepatocellular carcinoma (HCC) globally. 1 HCC is now the third commonest cause of cancerrelated deaths and UK-wide incidence rates are projected to rise by 38%

by the year 2035. 2 Nine months since the first reports of the novel Sars-cov2 virus in China, 3 and 6 months after the imposition of the UK-wide lockdown, the virus has had an unprecedented impact on healthcare services. The pandemic has seen dramatic reductions in early cancer referrals, delayed treatment and the suspension of cancer surveillance programmes. 4 Guidance at the onset of the pandemic recommended the deferral of HCC surveillance to protect the most vulnerable patients, however, the implications on liver cancer care are now emerging and the need for reorganisation of services is becoming urgent. 5 HBV is a partially double stranded DNA virus that exclusively infects hepatocytes. Increased cancer risk is the result of HBV DNA integration into the nuclei of infected cells, which leads to the promotion of hepatocyte clonal expansion. Importantly this process has been found to occur in all phases of chronic infection in those exposed to the virus perinatally or in childhood, regardless of severity of inflammation or liver fibrosis. 6 As HBV uses covalently closed circular DNA (cccDNA) in the nuclei of hepatocytes as a template for replication, it is unlikely that current or future therapies will eradicate all traces of the virus, and thus the risk for HCC occurrence remains in all patients with chronic HBV.

This risk is greatly increased in those with liver cirrhosis and indeed guidelines concur that this population should undergo HCC surveillance, by means of ultrasound scans and alpha fetoprotein (AFP) measurements, every 6 months. Guidelines are discordant, however, on the utility of viral and patient factors. Viral factors independently associated with increased HCC risk include HBV genotype 7 and hepatitis B E antigen. 8 Patient factors include male gender, age, alcohol excess, aflatoxin exposure, viral co-infection and metabolic syndrome. 9 In the last decade, several HCC risk prediction scores have been developed, taking patient and viral factors into account, while more recent scores place more emphasis on liver fibrosis measurements. In the present study, we analysed how five HCC risk prediction scores could aid stratification of patients with HBV attending the East of England Hepatitis B Regional Service at Addenbrookes Hospital, Cambridge University Hospitals NHS Trust UK, to safely identify those at high risk of HCC who require surveillance and those at low risk, in whom surveillance might be unnecessary. Furthermore, we investigated how these scores compare to regional surveillance guidelines, to rationalise services that have been strained during this pandemic.

We retrospectively assessed the diagnostic performance of five validated HCC risk scores, namely: Guide with Age, Gender, HBV DNA, core promotor mutations and cirrhosis score (GAG HCC score), 10 Risk estimation for hepatocellular carcinoma in Chronic Hepatitis B (REACH B score), 11 Modified REACH B score (HBV DNA substituted for liver fibrosis measurements), 12 modified platelets, age, gender (mPAGE B score) 13 and the age-Male-ALBI-Platelets (aMAP) score. 14 

The five HCC risk scores were calculated for all adult patients with chronic HBV diagnosed with HCC from 2007 to 2019 at Addenbrookes Hospital, Cambridge University Hospitals NHS Trust.

One hundred consecutive patients with a diagnosis of chronic HBV but without HCC attending our outpatient clinic between March and April 2020 were selected as the control group. Two scores were calculated for each patient: one from parameters contemporaneous to the HCC diagnosis for the cancer group or the latest clinic review for the control group, and one score from parameters 3 years prior.

Chronic HBV was diagnosed in those who tested positive for hepatitis B surface antigen for ≥6 months. The diagnosis of cirrhosis was based on serum markers and radiological findings, transient liver elastography (FibroScan) or liver biopsy. The diagnosis of HCC was based on radiological findings of multiphasic computed tomography and dynamic contrast enhanced MRI. In cases where imaging was not diagnostic for HCC, liver biopsy was performed.

HCC risk scores were calculated using previously published formulas. [10] [11] [12] [13] [14] Current local guidelines advise the surveillance of patients with chronic HBV if they are men over the age of 40 years, women over the age of 50 years, have cirrhosis or a family history of HCC.

Analysis was performed using Prism GraphPad v.8.0 and R version 3.5.1. Normality was determined using the D'Agostino-Pearson test.

Data were presented as mean ± standard deviation (±SD) if normally distributed, or median (IQR) for non-normal distribution. Two groups were compared by an unpaired t test if normally distributed, or by the Mann-Whitney U test if non-normally distributed. For multivariable analyses, logistic regression was used for binary outcomes.

Receiver-operating characteristic curves were used to determine the ability of the scores to discriminate between patients with HCC from controls. Negative predictive values (NPVs) were calculated using optimal HCC risk score thresholds from validation studies to identify those at low risk of HCC. P values < 0.05 were considered statistically significant.

In total 46 patients with chronic HBV were diagnosed with HCC from 2007 to 2019 (Table 1 ). The control group comprised of 100 patients with chronic HBV without HCC. The mean age in the HCC group was 58.16 ± 12.60 years. The majority (39/46, 84.78%) of patients were male, and 24 were of Asian ethnicity (52.17%). The mean age of the control group was 46.54 ± 11.39 years, with 59 being male (59%) and 57 of Asian ethnicity (57%). There were significant differences in age, gender, liver biochemistry and viral markers TA B L E 1 Baseline characteristics of study participants using parameters at the time of HCC diagnosis and 3 years prior We also selected 17 patients from the control HBV group, that were matched for age, gender, BMI and ethnicity to act as a control subgroup (Table 1) .

A multivariable logistic regression model was applied to the whole study population to determine the variables associated with the de- fibrosis was used as an ordinal variable in our model, the risk of HCC was highest for cirrhosis than for other categories of fibrosis categories below (Table 2 ).

There was a clear difference in the median scores between those with HCC and controls in all five scores ( Figure S1A 

Using parameters 3 years prior to HCC diagnosis to calculate each patient's HCC risk score revealed that only three scores (aMAP, mPAGE B and mREACH B) were able to significantly differentiate patients with HCC from those without ( Figure 1H 

We used each score's optimal threshold derived from their respective validation studies (Table S1) , to identify which patients in the HBV group required surveillance and compared how these differ to the 62 patients identified by current surveillance guidelines. Applying the scores to our control group identified significantly lower numbers of patients meeting criteria for surveillance when using the GAG HCC score (n = 16, P < 0.0001), the REACH B score (n = 26, P < 0.0001) and the mREACH B (n = 11, P < 0.0001). Use of the other two scores resulted in no-significant difference in those meeting criteria for surveillance compared to current guidelines (n = 59 for the mPAGE B, P > 0.99 and n = 72 for the aMAP score, P = 0.754) ( Figure 2A ). As mREACH B was the score with both high predictive performance and excellent NPV, we also calculated how many patients would require HCC surveillance using different cut-offs of this score. Unsurprisingly lower cut-offs resulted in more patients requiring surveillance, however, this did not affect the negative diagnostic performance of the score ( Figure 2B ).

We demonstrate that the mREACH B, aMAP and mPAGE B scores We reviewed the ease of use of each score and whilst all were designed as bedside clinical tools, some were easier to calculate than others. The GAG HCC, REACH B, mPAGE B and aMAP scores use age and biochemical parameters that are easy to collect, however, the aMAP score uses a complex formula not easily calculated in the clinical setting. Furthermore, the mREACH B score substitutes HBV DNA levels (incorporated in the REACH B score) for fibrosis measurements, potentially raising issues with access, cost and operator variability in measurements.

The introduction of widespread anti-viral treatment has shaped the natural history of the virus. Scores developed in the era preceding anti-viral use, such as GAG HCC and REACH B, placed greater emphasis on viral factors, whereas more recent scores mPAGE, mREACH and aMAP are centred around the presence of fibrosis. Indeed, these scores demonstrated superior discriminative performance in our study population. Furthermore, in our cohort, which includes both treated and untreated patients, liver fibrosis and AFP were associated with an increased risk of developing HCC, while HBV DNA and other recognised risk factors for HCC such as BMI, were not found to be significant. This is in keeping with recent studies demonstrating that HBV DNA levels are not associated with increased incidence of HCC, especially in patients on anti-viral therapy. 15 Recent validation studies also suggest that HCC risk scores incorporating HBV DNA levels may have reduced predictive capacity. 16 Whilst the performance of mREACH B, mPAGE B and aMAP scores remained similar when using parameters contemporaneous to the diagnosis and 3 years prior to this, the performance of the GAG HCC and REACH B score declined when using parameters 3 years prior to the cancer diagnosis. This might be due to the inclu- showed narrower performance ranges. All five HCC risk scores were internally and externally validated in Asian and Caucasian cohorts, however, none of them were specifically tested in Afro-Caribbean cohorts who have a higher risk of developing HCC, thus affecting the validity of the use of these scores in certain areas of the UK. 26 The predictive performance in patients with liver cirrhosis is suboptimal in all the scores, as patients with liver cirrhosis were often excluded in many derivation and validation studies. 11 An effective HCC risk score requires distinct optimal cut-off scores based on cumulative risk of developing HCC while also retaining a high NPV in order to safely streamline cancer surveillance services. A GAG HCC score <82 had an NPV of 100% for 10-year risk prediction in its derivation study, however, an NPV < 99% was found in six out of seven validation studies. 13, [17] [18] [19] [20] 22, 23 Two out of nine validation studies of the REACH B score showed a NPV > 99% using a cut-off of 8. 13, [17] [18] [19] [20] [21] [22] [23] [24] The derivation study for the mREACH B score did not include thresholds, but a subsequent validation study identified an optimal cut-off score of <10 using the Youden Index. 20 An NPV > 99% was found in 1 of 3 subsequent validation studies. 20, 21, 25 Interestingly though, cumulative incidence rates of HCC in those with scores <7 were significantly lower than those with higher scores. 20 Patients with an mPAGE B score ≤8 had a 0.7% 5-year HCC risk with NPVs > 99%. 23, 24 An aMAP score <50 had a 5-year cumulative risk of HCC of 0.8% and a NPV > 99%. 14 Strengths of the study include the evaluation of the performance of each score in our specific HBV population. Indeed, regression analysis revealed that parameters associated with the development of HCC in our cohort were concordant with the best performing risk scores. Furthermore, we critically appraised the literature regarding the validation of each score, which can aid the selection of the most appropriate score in different population settings. As a result of COVID-19 there are significant delays in ultrasound scans and patients are unable or reluctant to attend appointments. Utilising these scores would give clinicians some reassurance during these delays. A second wave of COVID-19 infections is currently observed in Europe and it is our hope that this body of work will help streamline services in the near future by reducing the number of unnecessary tests.

Limitations of the study include a small retrospective study design. A large prospective study comparing the best performing scores to current practice would be of value to validate our conclusions. Using contemporaneous parameters to diagnosis of HCC to assess the performance of each score we used the scores outside their original study indications. For this reason, we utilised the scores' 3-year diagnostic performance when deciding which ones can be adopted henceforth.

In summary the mREACH B, aMAP and mPAGE B scores appear to have the strongest discriminatory performance in identifying chronic HBV patients at high risk of HCC, whilst the mREACH B score has a high NPV to identify those at low risk. Incorporating the mREACH B score in the management of patients with HBV, reduced the number requiring surveillance by more than five times.

Consequently, the use of HCC risk prediction scores could have a considerable impact in the management of this patient population at a time of extremely limited resources. 

The authors confirm that the ethical policies of the journal, as noted on the journal's author guidelines page, have been adhered to. As we are reporting on routinely collected nonidentifiable clinical audit data, no approval from a research ethics committee was additionally required under the UK Policy Framework for Health and Social Care.

The authors have no conflict of interest to declare. The research was conducted in the absence of any personal, professional, commercial or financial relationships that could be construed as a potential conflict of interest, and this applies to all authors.

RS, GM and WG designed the study, RS and AJG collected and analysed the data, RS and AJG performed the statistical analysis, RS prepared the manuscript and designed the figures and tables, RS, AJG, LR, RB, GM and WG reviewed and edited the final manuscript, WG was the project supervisor, and is the guarantor of this project. All authors have made a substantial, direct and intellectual contribution to the work and approved the manuscript prior to its submission.

The peer review history for this article is available at https://publo ns.com/publo n/10.1002/ygh2.443.

An anonymised dataset and data analysis code is available upon application to the corresponding author.

https://orcid.org/0000-0002-1587-9412

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