Cost-Effectiveness of a Biomarker-Based Screening Strategy for Hepatocellular Carcinoma in Patients with Cirrhosis.

Introduction: Given suboptimal performance of ultrasound-based surveillance for early hepatocellular carcinoma (HCC) detection in patients with cirrhosis, there is interest in alternative surveillance strategies, including blood-based biomarkers. We aimed to evaluate the cost-effectiveness of biomarker-based surveillance in patients with cirrhosis.

Methods: We constructed a decision-analytic model to compare ultrasound/alpha-fetoprotein (AFP) and biomarker-based surveillance strategies in 1,000,000 simulated patients with compensated cirrhosis. Model inputs for adherence, benefits, and harms of each strategy were based on literature review, and costs were derived from the Medicare fee schedule. Primary outcomes were quality-adjusted life-years (QALY) and incremental cost-effectiveness ratio (ICER) of the surveillance strategies, with cost-effectiveness assessed at a threshold of USD 150,000 per QALY. We performed sensitivity analyses for HCC incidence, test performance characteristics, surveillance adherence, and biomarker costs.

Results: In the base case, both ultrasound/AFP and biomarker-based surveillance were cost-effective versus no surveillance, with ICERs of USD 105,620, and USD 101,295, per QALY, respectively. Biomarker-based surveillance was also cost-effective versus ultrasound/AFP, with an ICER of USD 14,800 per QALY. Biomarker sensitivity exceeding 80%, cost below USD 210, or adherence exceeding 58% were necessary for biomarker-based screening to be cost-effective versus ultrasound/AFP. In two-way sensitivity analyses, biomarker costs were directly related with test sensitivity and adherence, whereas sensitivity and adherence were inversely related. In a probabilistic sensitivity analysis, biomarker-based screening was the most cost-effective strategy in most (65%) simulations.

Conclusion: Biomarker-based screening appears cost-effective for HCC screening, but results are sensitive to test sensitivity, adherence, and costs.