Abstract
Simple SummaryWe externally validated the recently suggested FSAC prediction model for hepatocellular carcinoma (HCC) in treatment-naïve Asian chronic hepatitis B patients starting potent antiviral therapy (AVT). The model reflects age, sex, presence of cirrhosis, and on-therapy changes in non-invasive fibrosis markers (NFMs) after 12 months of antiviral therapy, such as APRI and FIB-4. Our results highlighted better predictive performance for the FSAC model for HCC (Harrell’s c-index: 0.770) than the PAGE-B, modified PAGE-B, modified REACH-B, LSM-HCC, and CAMD models, which only use baseline parameters. A simplified version of FSAC score (i.e., FSAC (2)), including only NFMs at 12 months, also showed a high c-index value (0.763). Our retrospective study suggests that the accurate measurement of intra-hepatic fibrotic burden during adequate AVT is necessary for predicting HCC development.Antiviral therapy (AVT) induces the regression of non-invasive fibrosis markers (NFMs) and reduces hepatocellular carcinoma (HCC) risk among chronic hepatitis B (CHB) patients. We externally validated the predictive performance of the FSAC prediction model for HCC using on-therapy NFM responses. Our multicenter study consecutively recruited treatment-naïve CHB patients (n = 3026; median age, 50.0 years; male predominant (61.3%); cirrhosis in 1391 (46.0%) patients) receiving potent AVTs for >18 months between 2007 and 2018. During follow-up (median 64.0 months), HCC developed in 303 (10.0%) patients. Patients with low FIB-4 or APRI levels at 12 months showed significantly lower HCC risk than those with high NFM levels at 12 months (all p < 0.05). Cumulative 3-, 5-, and 8-year HCC probabilities were 0.0%, 0.3% and 1.2% in the low-risk group (FSAC ≤ 2); 2.1%, 5.2%, and 11.1% in the intermediate-risk group (FSAC 3−8); and 5.2%, 15.5%, and 29.8% in the high-risk group (FSAC ≥ 9) (both p < 0.001 between each adjacent pair). Harrell’s c-index value for FSAC score (0.770) was higher than those for PAGE-B (0.725), modified PAGE-B (0.738), modified REACH-B (0.737), LSM-HCC (0.734), and CAMD (0.742). Our study showed that the FSAC model, which incorporates on-therapy changes in NFMs, had better predictive performance than other models using only baseline parameters.
Highlights
Chronic hepatitis B virus (HBV) infection is a major public health problem affecting approximately more than 250 million people worldwide; it remains a leading cause of hepatocellular carcinoma (HCC), especially in endemic areas such as Korea [1,2,3,4,5]
We aimed to externally validate the predictive performance of the newly developed FSAC model in comparison with other risk prediction models assessed at one time-point in an independent HBV cohort treated with ETV, tenofovir disoproxil fumarate (TDF), or tenofovir alafenamide (TAF)
Antiviral therapy (AVT) was initiated for patients with chronic hepatitis B (CHB) or cirrhosis according to the practice guidelines of the Korean Association for the Study of the Liver and the reimbursement guidelines of the National Health Insurance Service of Korea [26]
Summary
Chronic hepatitis B virus (HBV) infection is a major public health problem affecting approximately more than 250 million people worldwide; it remains a leading cause of hepatocellular carcinoma (HCC), especially in endemic areas such as Korea [1,2,3,4,5]. The risk of developing HCC has substantially decreased in the past several decades, stemming primarily from the use of potent oral nucleos(t)ide analogues with high genetic barriers, that is, entecavir (ETV), tenofovir disoproxil fumarate (TDF), and tenofovir alafenamide (TAF), which can effectively suppress viral replication and reduce processes of necro-inflammation and/or fibrosis [4,5,6]. Notwithstanding, since such highly active antiviral therapy cannot eradicate intra-hepatic HBV itself and the molecular mechanisms of hepato-carcinogenesis are complex [7,8,9], the regular surveillance of patients with chronic hepatitis B (CHB) is recommended to detect early stage HCC, for which treatment with a curative aim might be possible [10,11,12]. We aimed to externally validate the predictive performance of the newly developed FSAC model in comparison with other risk prediction models assessed at one time-point in an independent HBV cohort treated with ETV, TDF, or TAF
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