S2922 Development and Validation of a Simplified Risk Model to Predict High Liver Fat Using NHANES Elastography Data

Introduction: Hepatic steatosis, the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), is increasingly prevalent. Early risk stratification can facilitate targeted screening and prevention. We aimed to develop and validate a simplified, interpretable logistic regression model for predicting high liver fat content using U.S. population-based data. Methods: We analyzed NHANES 2021–2022 data on 2,151 adults with valid transient elastography and laboratory results. High liver fat was defined as CAP ≥275 dB/m. Candidate predictors included demographics, BMI category, diabetes, CRP, liver stiffness (LSM), and lifestyle variables. We trained full logistics LASSO logistic, and random forest models using 5-fold cross-validation. A simplified logistic model using 8 top predictors was derived. Model performance was evaluated using AUC, calibration, and diagnostic metrics at optimal thresholds based on Youden’s Index. Results: High liver fat was present in 44% of participants. The AUC for the full logistic model was 0.825, LASSO 0.831, and random forest 0.821. The simplified model retained excellent discrimination (AUC = 0.795) with the following formula: logit (P) = 0.3083 − 2.4617 × BMI_Normal + 1.2299 × BMI_Obesity III − 0.9276 × BMI_Overweight + 0.9701 × BMI_Obesity II + 0.6942 × Diabetes − 0.5378 × Female − 0.0093 × CAP + 0.0022 × LSM At an optimized threshold of 0.45, the simplified model achieved sensitivity 66.4%, specificity 77.0%, PPV 64.6%, NPV 78.4%, and F1-score 65.5%. Calibration plots demonstrated strong agreement between predicted and observed probabilities. Conclusion: We present a simplified logistic model that retains high predictive performance using only 8 clinical variables. It offers an accessible, interpretable screening tool for MASLD risk stratification, with potential integration into primary care workflows and large-scale public health applications.