P1576 - Beyond FIB-4: A Multivariable Risk Prediction Model for Advanced Fibrosis in Type 2 Diabetes Mellitus Using Outpatient Data

Safia Mohamed, MD¹, Sabah Sikander, DO², Pranav Ramamurthy, MBBS, MD³, Victoria Caruso, DO¹, Mithil Gowda Suresh, MD⁴, Yesenia Greeff, MD⁵
1University of Massachusetts Chan Medical School - Baystate Health, Springfield, MA; 2Baystate Medical Center, Springfield, MA; 3University of Massachusetts Chan Medical School-Baystate Medical Center, Springfield, MA; 4St. Vincent's Hospital, Chicopee, MA; 5University of Massachusetts Chan Medical School - Baystate Health, Northampton, MA

Introduction: FIB-4 is a valuable noninvasive score for identifying patients at risk for hepatic fibrosis, but it is often underutilized or unavailable at the point of care. We sought to develop a multivariable model using routine outpatient clinical data to predict elevated FIB-4 in patients with Type 2 Diabetes Mellitus (T2DM), offering an adjunctive tool to trigger MASLD care pathways. The aim of the study was to develop and evaluate a predictive model for identifying patients with FIB-4 > 1.3 using demographic and laboratory data.

Methods: A retrospective cohort of T2DM patients seen in outpatient care in 2021 was analyzed. Predictor variables included age, sex, BMI, A1c, race, and provider type. The outcome was a calculated FIB-4 >1.3. A logistic regression model with L2 regularization was trained and validated on a 75/25 split. Model performance was assessed using AUC and confusion matrix.

Results: Among 1,143 patients: The final model demonstrated an AUC of 0.79. Top predictors included race (Nepalese, Somali, Hispanic, Nigerian), with NP/PAs less likely to care for patients with elevated FIB-4. In the test set, the model had: Sensitivity 41.5%, Specificity 94.2%, Accuracy 78%.

Discussion: We present a novel predictive model for advanced fibrosis risk in patients with T2DM using standard outpatient data. With strong discriminatory performance, this model may serve as a surrogate flag to prompt further MASLD workup or specialist referral—especially when full lab-based scores like FIB-4 are unavailable. Integration into EMR systems can help close the gap in MASLD recognition.

Disclosures:
Safia Mohamed indicated no relevant financial relationships.
Sabah Sikander indicated no relevant financial relationships.
Pranav Ramamurthy indicated no relevant financial relationships.
Victoria Caruso indicated no relevant financial relationships.
Mithil Gowda Suresh indicated no relevant financial relationships.
Yesenia Greeff indicated no relevant financial relationships.

Safia Mohamed, MD¹, Sabah Sikander, DO², Pranav Ramamurthy, MBBS, MD³, Victoria Caruso, DO¹, Mithil Gowda Suresh, MD⁴, Yesenia Greeff, MD⁵. P1576 - Beyond FIB-4: A Multivariable Risk Prediction Model for Advanced Fibrosis in Type 2 Diabetes Mellitus Using Outpatient Data, ACG 2025 Annual Scientific Meeting Abstracts. Phoenix, AZ: American College of Gastroenterology.