An Electronic Health Record Model for Predicting Risk of Hepatic Fibrosis in Primary Care Patients

Authors

Aaron P Thrift
Theresa H Nguyen Wenker
Kyler Godwin
Maya Balakrishnan
Hao T Duong
Rohit Loomba
Fasiha Kanwal
Hashem B El-Serag

Publication Date

7-1-2024

Journal

Digestive Diseases and Sciences

DOI

10.1007/s10620-024-08437-2

PMID

38700632

PMCID

PMC11258165

Published Open-Access

no

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.

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