Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic...

Area of Science:

• Hepatology
• Medical Informatics
• Public Health

Background:

• Nonalcoholic fatty liver disease (NAFLD) is a growing global health concern.
• Early detection of hepatic steatosis (HS) is crucial for timely hepatology referral.
• Vibration-controlled transient elastography (VCTE) with controlled attenuation parameter (CAP) is a validated non-invasive tool for HS diagnosis.

Purpose of the Study:

• To develop and validate a novel clinical predictive algorithm for HS in Veterans.
• To identify key clinical predictors of elevated CAP scores.
• To establish an optimal CAP threshold for HS detection in the Veteran population.

Main Methods:

• Retrospective analysis of 403 Veterans with VCTE data from the Greater Los Angeles VA Healthcare System.
• Exclusion of patients with alcohol-associated liver disease, specific hepatitis C genotypes, malignancies, or liver transplantation.
• Linear regression to identify predictors of NAFLD; receiver operating characteristic analysis to determine CAP thresholds using biopsy, MRI, and ultrasound as gold standards.

Main Results:

• The cohort was diverse: 26% Black/African American, 20% Hispanic.
• Predictors of elevated CAP included diabetes, cholesterol, triglycerides, BMI, and Hispanic ethnicity.
• The predictive model showed strong correlation (r=0.61) with actual CAP scores, with 82% sensitivity and 83% specificity in validation. Optimal CAP cutoff was 273.5 dB/m (AUC=75.5%).

Conclusions:

• A novel clinical algorithm effectively predicts HS in Veterans, aiding in risk stratification.
• This tool facilitates linking at-risk Veterans to non-invasive testing and sub-specialty care.
• Future research should explore the algorithm's applicability in non-specialty clinics due to potential referral biases.