P5918 - Comparing Machine Learning to Conventional FIB-4 Thresholding for Fibrosis Risk Stratification: A Decision Curve Analysis Approach

Pranav Ramamurthy, MBBS, MD¹, Safia Mohamed, MD², Dimitri Melki, MD³, Victoria Caruso, DO², Yesenia Greeff, MD⁴
1University of Massachusetts Chan Medical School-Baystate Medical Center, Springfield, MA; 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: Current fibrosis screening strategies in Type 2 Diabetes Mellitus (T2DM) patients rely on FIB-4 thresholding, yet may not optimize clinical decision-making. Decision Curve Analysis (DCA) evaluates the net benefit of predictive models across a range of referral thresholds. We compared the clinical utility of machine learning (ML) vs. traditional models in identifying patients at risk of advanced fibrosis.

Methods: We included 1,149 adult patients with T2DM from a multi-provider outpatient network. Elevated fibrosis risk was defined as FIB-4 >1.3. Predictors included demographics, BMI, A1c, and provider type. We trained logistic regression and XGBoost models using 5-fold cross-validation and calculated net benefit at thresholds ranging from 0.01–0.50. DCA was conducted to compare each model to “refer-all” and “refer-none” strategies.

Results: XGBoost outperformed logistic regression in net benefit across nearly all thresholds. For example, at a threshold probability of 20%, net benefit was 0.094 (XGBoost) vs. 0.068 (logistic). Both models outperformed “refer-all” and “refer-none” strategies across realistic threshold ranges (10–30%). The highest gain in clinical utility was observed between thresholds of 15–25%, aligning with typical screening practices.

Discussion: Machine learning models such as XGBoost offer greater net clinical benefit compared to standard FIB-4 cutoff-based approaches. DCA provides a robust framework for optimizing referral thresholds and supports ML implementation in outpatient MASLD screening strategies.

Disclosures:
Pranav Ramamurthy 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.

Pranav Ramamurthy, MBBS, MD¹, Safia Mohamed, MD², Dimitri Melki, MD³, Victoria Caruso, DO², Yesenia Greeff, MD⁴. P5918 - Comparing Machine Learning to Conventional FIB-4 Thresholding for Fibrosis Risk Stratification: A Decision Curve Analysis Approach, ACG 2025 Annual Scientific Meeting Abstracts. Phoenix, AZ: American College of Gastroenterology.