Author information

¹Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA.

²Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.

³Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Cambridge Street, Houston, TX, 7200, USA.

⁴Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA.

⁵Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.

⁶Division of Epidemiology, Department of Family Medicine and Public Health, University of California at San Diego, San Diego, CA, USA.

⁷NAFLD Research Center, Division of Gastroenterology and Hepatology, University of California at San Diego, La Jolla, CA, USA.

⁸Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Cambridge Street, Houston, TX, 7200, USA. hasheme@bcm.edu.

⁹Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA. hasheme@bcm.edu.

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/m²; FIB-4 ≥ 1.45) had lower discriminatory ability (AUC 0.65), but higher PPV (50.0%) and similar NPV (91.3%). We observed similar findings for predicting significant hepatic fibrosis.

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.