Advanced Fibrosis Detection and a Predictive Diagnostic Model for Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD) in Primary Care

Abstract Advanced Fibrosis Detection and a Predictive Diagnostic Model for Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD) in Primary Care. Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly nonalcoholic fatty liver disease [NAFLD]) affects an estimated 1 in 3 persons in the U.S., a prevalence expected to increase over the next decade. MASLD’s rising prevalence and its association with diabetes and obesity make it a chronic disease well-suited for initial management by primary care providers (PCPs). PCPs can impact MASLD care by detecting advanced fibrosis, which is the best predictor of cirrhosis, hepatocellular carcinoma, and liver-related mortality in affected patients. Recently issued guidelines from the American Association for the Study of Liver Diseases recommend the sequential use of non-invasive liver tests, the Fibrosis-4 Index (FIB-4) followed by confirmatory liver stiffness measurement (LSM) with vibration-controlled elastography (VCTE), to detect advanced fibrosis in patients with MASLD. FIB-4 is attractive in primary care due to the low-cost and broad availability of its inputs, but PCPs have little experience with FIB-4 calculation, limited comfort with its interpretation, and infrequent access to confirmatory liver stiffness testing. The guidelines provide a clinical threshold for hepatology engagement, recommending referral for patients with advanced fibrosis, while those with low-risk MASLD remain in primary care. Incorporating advanced fibrosis detection into the already overwhelming workload of PCPs requires thoughtful application of electronic health record (EHR) technologies to avoid contributing to PCP alert fatigue and burnout. Also, despite the high prevalence, MASLD is underdiagnosed in primary care due to a host of factors that include the lack of recommendations for universal screening and limitations in the performance of traditional MASLD signals for case finding, including type 2 diabetes mellitus, obesity, and abnormal liver chemistries. Iterative efforts to address the MASLD diagnostic care gap need to assist PCPs in identifying which patients need liver imaging and ensure that the signals for diagnostic testing capture MASLD with advance fibrosis, disease PCPs cannot miss. In this work, we aim to test the adoption, penetration, fidelity, sustainability, and performance of a novel, non-interruptive EHR alert for MASLD fibrosis risk assessment in a primary care network by performing a stepped wedge, cluster randomized trial in patients with known MASLD (Aim 1). We will also develop, test, and internally validate a predictive diagnostic model for identifying MASLD and MASLD with advanced fibrosis in a prospective cohort of patients with no known chronic liver disease (Aim 2). This proposal aligns with NIDDK’s scientific goal to disseminate, implement, and evaluate evidence-based care strategies in community care settings where the burden of MASLD hides in plain sight.