P5768 - Enhancing Fibrosis Risk Stratification in T2DM Patients Using Machine Learning: A Real-World Outpatient Study

Samia Nadeem, MD¹, Safia Mohamed, MD², Dimitri Melki, MD³, Victoria Caruso, DO², Yesenia Greeff, MD⁴
1University of Massachusetts Chan Medical School-Baystate Medical Center, Broad Brook, CT; 2University of Massachusetts Chan Medical School - Baystate Health, Springfield, MA; 3University of Massachusetts Chan Medical School - Baystate Health, Windsor, CT; 4University of Massachusetts Chan Medical School - Baystate Health, Northampton, MA

Introduction: Noninvasive fibrosis indices such as the FIB-4 score are valuable tools for identifying patients with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) at risk for advanced fibrosis. However, screening uptake in outpatient populations with Type 2 Diabetes Mellitus (T2DM) remains inconsistent. We aimed to develop and compare machine learning (ML) models to predict elevated fibrosis risk and determine the most important clinical predictors in a real-world setting.

Methods: We retrospectively analyzed data from 1,149 adult patients with T2DM seen in outpatient clinics across a multi-provider network in 2021. The primary outcome was elevated fibrosis risk, defined as a FIB-4 score >1.3. Predictor variables included demographics (age, sex, race), BMI, hemoglobin A1c, and provider type. We evaluated the performance of three predictive models—logistic regression, random forest, and XGBoost—using 5-fold cross-validation. Feature importance was derived from the final XGBoost model.

Results: Of the 1,149 patients, 14.1% had an FIB-4 score >1.3. Model comparison showed that XGBoost achieved the highest discriminatory performance (AUC = 0.812), outperforming random forest (AUC = 0.798) and logistic regression (AUC = 0.774). The top predictors of elevated FIB-4 included older age, higher BMI, elevated A1c, and male sex. Provider type and race also contributed substantially to model performance.

Discussion: Machine learning models, particularly XGBoost, demonstrate strong potential for improving fibrosis risk stratification in high-risk T2DM patients. These models may complement FIB-4 scoring by leveraging routinely available clinical data to prioritize patients for MASLD screening and referral. Prospective validation is warranted to guide implementation.

Disclosures:
Samia Nadeem indicated no relevant financial relationships.
Safia Mohamed indicated no relevant financial relationships.
Dimitri Melki indicated no relevant financial relationships.
Victoria Caruso indicated no relevant financial relationships.
Yesenia Greeff indicated no relevant financial relationships.

Samia Nadeem, MD¹, Safia Mohamed, MD², Dimitri Melki, MD³, Victoria Caruso, DO², Yesenia Greeff, MD⁴. P5768 - Enhancing Fibrosis Risk Stratification in T2DM Patients Using Machine Learning: A Real-World Outpatient Study, ACG 2025 Annual Scientific Meeting Abstracts. Phoenix, AZ: American College of Gastroenterology.