Budget Impact Analysis of Introducing the Elecsys® PRO-C3 Assay as Part of the ADAPT Algorithm for Assessing the Severity of Hepatic Fibrosis in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) in the UK
Author(s)
Osvaldo Ulises Garay, MSc1, Bruno Golding, PhD1, Alessandro Pedrioli, PhD1, Ema Dauksaite, MSc2, Mark Pennington, PhD2, Roland Fritz, PhD1.
1Roche Diagnostics International, Rotkreuz, Switzerland, 2Source Health Economics, London, United Kingdom.
1Roche Diagnostics International, Rotkreuz, Switzerland, 2Source Health Economics, London, United Kingdom.
OBJECTIVES: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 30-38% of the general population and is highly underdiagnosed. Metabolic dysfunction-associated steatohepatitis (MASH), found in 25% of patients with MASLD, may cause hepatocellular carcinoma and liver cirrhosis. Fibrosis-4 (FIB-4), Enhanced Liver Fibrosis (ELF), and transient elastography (Fibroscan®) are currently used to diagnose MASLD in the United Kingdom (UK). Elecsys® PRO-C3 as part of the ADAPT algorithm is a CE marked test intended to be used to assess hepatic fibrosis severity in patients showing signs of MASLD. The ADAPT algorithm incorporates age, diabetic status, PRO-C3 (a type III collagen formation marker), and platelet count. This analysis evaluates the budget impact of introducing ADAPT to assess hepatic fibrosis severity in the UK over a 5-year time horizon.
METHODS: We compared costs associated with a diagnostic pathway using ADAPT (FIB-4→ADAPT→Fibroscan®) with current care. We assumed current care consists of 50% of patients receiving FIB-4→ELF→Fibroscan® and 50% of patients receiving FIB-4→Fibroscan®. Progression through test sequences was assumed following a positive or inconclusive test. The model included initial and subsequent testing for hepatic fibrosis in primary and secondary care.
RESULTS: Assuming an increase in diagnosis of MASLD/MASH from 18% of patients in 2025 to 22% in 2029, projected tests were 254,447 in 2025, increasing to 286,410 by 2029. The total cost of testing over five years was £294 million under current care. Incorporation of ADAPT into testing saved £81 to £41 per patient first tested in 2025 to 2029; total savings over five years were £83 million. In scenario analysis assuming 20% of testing pathways under current care include ELF, introduction of ADAPT saved £127 million.
CONCLUSIONS: MASLD testing is likely to rise over the next five years as availability of treatments increase. The introduction of ADAPT will substantially reduce the impact of testing on healthcare budgets.
METHODS: We compared costs associated with a diagnostic pathway using ADAPT (FIB-4→ADAPT→Fibroscan®) with current care. We assumed current care consists of 50% of patients receiving FIB-4→ELF→Fibroscan® and 50% of patients receiving FIB-4→Fibroscan®. Progression through test sequences was assumed following a positive or inconclusive test. The model included initial and subsequent testing for hepatic fibrosis in primary and secondary care.
RESULTS: Assuming an increase in diagnosis of MASLD/MASH from 18% of patients in 2025 to 22% in 2029, projected tests were 254,447 in 2025, increasing to 286,410 by 2029. The total cost of testing over five years was £294 million under current care. Incorporation of ADAPT into testing saved £81 to £41 per patient first tested in 2025 to 2029; total savings over five years were £83 million. In scenario analysis assuming 20% of testing pathways under current care include ELF, introduction of ADAPT saved £127 million.
CONCLUSIONS: MASLD testing is likely to rise over the next five years as availability of treatments increase. The introduction of ADAPT will substantially reduce the impact of testing on healthcare budgets.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
EE90
Topic
Economic Evaluation, Medical Technologies
Topic Subcategory
Budget Impact Analysis
Disease
Diabetes/Endocrine/Metabolic Disorders (including obesity)