S2892 Quantifying MASLD Risk Management Gaps in T2DM: A Counterfactual Simulation of Missed GI Referrals Using FIB-4 Stratification

Introduction: Patients with type 2 diabetes mellitus (T2DM) are at elevated risk for advanced hepatic fibrosis due to metabolic dysfunction-associated steatotic liver disease (MASLD). The FIB-4 index is a widely endorsed, noninvasive screening tool, but its implementation in outpatient diabetes care remains limited. Quantifying the potential impact of guideline adherence is essential for optimizing population-level liver care. Our study aimed to estimate the referral gap for high-risk MASLD in T2DM by comparing observed gastroenterology (GI) referral rates to a simulated guideline-adherent model using FIB-4 stratification. Methods: We retrospectively analyzed adult T2DM outpatients seen in 2021. FIB-4 scores were calculated using structured laboratory data. Patients with FIB-4 >1.3 were considered at increased risk for advanced fibrosis. We compared actual GI referral documentation with a simulated ideal model where all high-risk patients were referred. Generalized additive modeling (GAM) was used to assess non-linear associations between age, provider type, and referral likelihood. Results: Among 162 patients with FIB-4 >1.3, only 5 (3.1%) were referred to GI. In the simulated model, 161 patients (99.4%) would have been referred, indicating a 96.9% referral gap. GAM analysis awareness revealed a non-linear decline in referral probability with increasing age, independent of fibrosis risk, suggesting possible under-recognition of MASLD in older adults. Provider type was not independently associated with referral status in multivariable modeling. Conclusion: Despite established guidelines, most T2DM patients at elevated risk for advanced fibrosis were not referred to GI. Age appeared to influence referral behavior, pointing to an opportunity for enhanced and process-based interventions. Structured implementation of FIB-4–based pathways, including EHR prompts or automated referral triggers, may help optimize MASLD risk management in primary diabetes care.