Introducing Risk Stratification Reduces Patient Burden and Improves Cost-Effectiveness of Hepatocellular Carcinoma Surveillance for People With Metabolic Dysfunction-Associated Steatotic Liver Disease: A Modeling Study

Abstract

Objectives

As the burden of hepatocellular carcinoma (HCC, the most common form of liver cancer) associated with metabolic dysfunction-associated steatotic liver disease (MASLD/metabolic-associated fatty liver disease) increases, there is a renewed focus on early detection via routine surveillance. However, a third of Australians have metabolic dysfunction-associated steatotic liver disease/metabolic-associated fatty liver disease because of high overweight/obesity rates, with varying liver disease and HCC risk. With such a large at-risk population, it is crucial that interventions are well targeted for those at highest risk to remain cost-effective. Affordable biomarker testing can identify those at highest risk, focusing efforts on those most likely to benefit.

Methods

We used the Policy1-Liver model of liver disease and cancer to evaluate the health impacts and cost-effectiveness of routine HCC surveillance. We assessed the benefits of risk stratification using FIB-4 biomarker liver disease score. Outcomes including HCC incidence and mortality, quality-adjusted life-years, costs, and cost-effectiveness were estimated.

Results

Routine surveillance reduced HCC mortality by 19% to 27% compared with no surveillance, but without risk stratification, it was not cost-effective for patients with early-stage liver fibrosis or no steatohepatitis. Incorporating FIB-4 risk stratification for inclusion in surveillance maintained health benefits while reducing costs and patient burden, with a cost-effectiveness under $30 000/quality-adjusted life-year at all cutoffs above 1.10. For example, routine surveillance for all individuals with a baseline FIB-4 above 2.67 had an incremental cost-effectiveness of $15 055/quality-adjusted life-year saved.

Conclusion

Risk stratification can improve the efficiency of routine HCC surveillance by focusing efforts on those at highest risk. As the burden of MASLD continues to grow, targeted intervention can be guided by predictive modeling.