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Several risk prediction algorithms have been developed to guide antiviral therapy initiation among patients with chronic hepatitis B (CHB). This study assessed the cost-effectiveness and budget impact of three risk prediction algorithms among patients with CHB in Thailand. A decision tree with a Markov model was constructed. Three risk prediction algorithms were compared with current practices including HePAA, TREAT-B and REACH-B. PubMed was searched from its inception to December 2022 to identify inputs. Tenofovir alafenamide and best supportive care were selected for antiviral-eligible patients, and incremental cost-effectiveness ratios per quality-adjusted life year (QALY) were calculated. Our base case analysis showed that HePAA and REACH-B could provide better QALY (0.098 for HePAA and 0.921 for REACH-B) with decreased total healthcare costs (−10909 THB for HePAA and −8,637 THB for REACH-B). TREAT-B provided worse QALY (−0.144) with increased total healthcare costs (10,435 THB). The budget impacts for HePAA and REACH-B were 387 million THB and 3,653 million THB, respectively. HePAA and REACH-B algorithms are cost-effective in guiding antiviral therapy initiation. REACH-B is the most cost-effective option, but has a high budget impact. Policymakers should consider both cost-effectiveness and budget impact findings when deciding which algorithm should be implemented.

Additional file 1: Supplementary 1. - Sample size calculation.

Additional file 1: Supplementary figure 1. Sample size calculation using G Power analysis. Sample size was calculated using serum amylase levels in the control and low-dose curcumin groups in a study by Siriviriyakul et al.13. With the alpha of 0.05 and the power of 90%, the sample size in each group was 6.