Publication Date
12-1-2023
Journal
Digestive Diseases and Sciences
DOI
10.1007/s10620-023-08043-8
PMID
37864738
PMCID
PMC10635943
PubMedCentral® Posted Date
10-21-2023
PubMedCentral® Full Text Version
Post-Print
Published Open-Access
yes
Keywords
Humans, Non-alcoholic Fatty Liver Disease, Elasticity Imaging Techniques, Liver, Veterans, Electronic Health Records, Prospective Studies, ROC Curve, Liver Diseases, Alcoholic, Biopsy, Liver Cirrhosis, Nonalcoholic fatty liver disease, Prediction, Model, Elastography
Abstract
BACKGROUND AND AIMS: Nonalcoholic fatty liver disease (NAFLD) has reached pandemic proportions. Early detection can identify at-risk patients who can be linked to hepatology care. The vibration-controlled transient elastography (VCTE) controlled attenuation parameter (CAP) is biopsy validated to diagnose hepatic steatosis (HS). We aimed to develop a novel clinical predictive algorithm for HS using the CAP score at a Veterans' Affairs hospital.
METHODS: We identified 403 patients in the Greater Los Angeles VA Healthcare System with valid VCTEs during 1/2018-6/2020. Patients with alcohol-associated liver disease, genotype 3 hepatitis C, any malignancies, or liver transplantation were excluded. Linear regression was used to identify predictors of NAFLD. To identify a CAP threshold for HS detection, receiver operating characteristic analysis was applied using liver biopsy, MRI, and ultrasound as the gold standards.
RESULTS: The cohort was racially/ethnically diverse (26% Black/African American; 20% Hispanic). Significant positive predictors of elevated CAP score included diabetes, cholesterol, triglycerides, BMI, and self-identifying as Hispanic. Our predictions of CAP scores using this model strongly correlated (r = 0.61, p < 0.001) with actual CAP scores. The NAFLD model was validated in an independent Veteran cohort and yielded a sensitivity of 82% and specificity 83% (p < 0.001, 95% CI 0.46-0.81%). The estimated optimal CAP for our population cut-off was 273.5 dB/m, resulting in AUC = 75.5% (95% CI 70.7-80.3%).
CONCLUSION: Our HS predictive algorithm can identify at-risk Veterans for NAFLD to further risk stratify them by non-invasive tests and link them to sub-specialty care. Given the biased referral pattern for VCTEs, future work will need to address its applicability in non-specialty clinics. Proposed clinical algorithm to identify patients at-risk for NAFLD prior to fibrosis staging in Veteran.
Graphical Abstract
Included in
Digestive System Diseases Commons, Gastroenterology Commons, Hepatology Commons, Medical Sciences Commons
Comments
Associated Data