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

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

SUBMITTER: Thrift AP 

PROVIDER: S-EPMC11258165 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature