Publication Date
7-1-2024
Journal
Digestive Diseases and Sciences
DOI
10.1007/s10620-024-08437-2
PMID
38700632
PMCID
PMC11258165
Published Open-Access
yes
Keywords
Humans, Female, Male, Middle Aged, Electronic Health Records, Primary Health Care, Risk Assessment, Liver Cirrhosis, Risk Factors, Non-alcoholic Fatty Liver Disease, Adult, Aged, Elasticity Imaging Techniques, Predictive Value of Tests, Obesity, Body Mass Index, Fatty liver, Obesity, Veterans, Liver cancer
Abstract
BACKGROUND: One challenge for primary care providers caring for patients with nonalcoholic fatty liver disease is to identify those at the highest risk for clinically significant liver disease.
AIM: To derive a risk stratification tool using variables from structured electronic health record (EHR) data for use in populations which are disproportionately affected with obesity and diabetes.
METHODS: We used data from 344 participants who underwent Fibroscan examination to measure liver fat and liver stiffness measurement [LSM]. Using two approaches, multivariable logistic regression and random forest classification, we assessed risk factors for any hepatic fibrosis (LSM > 7 kPa) and significant hepatic fibrosis (> 8 kPa). Possible predictors included data from the EHR for age, gender, diabetes, hypertension, FIB-4, body mass index (BMI), LDL, HDL, and triglycerides.
RESULTS: Of 344 patients (56.4% women), 34 had any hepatic fibrosis, and 15 significant hepatic fibrosis. Three variables (BMI, FIB-4, diabetes) were identified from both approaches. When we used variable cut-offs defined by Youden's index, the final model predicting any hepatic fibrosis had an AUC of 0.75 (95% CI 0.67-0.84), NPV of 91.5% and PPV of 40.0%. The final model with variable categories based on standard clinical thresholds (i.e., BMI ≥ 30 kg/m
CONCLUSIONS: Our results demonstrate that standard thresholds for clinical risk factors/biomarkers may need to be modified for greater discriminatory ability among populations with high prevalence of obesity and diabetes.
Included in
Biochemistry, Biophysics, and Structural Biology Commons, Health Information Technology Commons, Hepatology Commons, Medical Sciences Commons, Primary Care Commons, Quality Improvement Commons
