A practical use of noninvasive tests in clinical practice to identify high-risk patients with nonalcoholic steatohepatitis. Academic Article uri icon

Overview

abstract

  • BACKGROUND: Patients with nonalcoholic fatty liver disease (NAFLD) with type 2 diabetes (T2D) or other components of metabolic syndrome are at high risk for disease progression. We proposed an algorithm to identify high-risk NAFLD patients in clinical practice using noninvasive tests (NITs). METHODS: Evidence about risk stratification of NAFLD using validated NITs was reviewed by a panel of NASH Experts. Using the most recent evidence regarding the performance of NITs and their application in clinical practice were used to develop an easy-to-use algorithm for risk stratification of NAFLD patients seen in primary care, endocrinology and gastroenterology practices. RESULTS: The proposed algorithm uses a three-step process to identify NAFLD patients who are potentially at high risk for adverse outcomes. The first step is to use clinical data to identify most patients who are at risk for having potentially progressive NAFLD (e.g. having T2D or multiple components of metabolic syndrome). The second step is to calculate the FIB-4 score as a NIT that can further risk stratifying individuals who are at low risk for progressive liver disease and can be managed by their primary healthcare providers to manage their cardiometabolic comorbidities. The third step is to use second-line NITs (transient elastography or enhanced liver fibrosis tests) to identify those who at high risk for progressive liver disease and should be considered for specially care by providers with NASH expertise. CONCLUSIONS: The use of this simple clinical algorithm can identify and assist in managing patients with NAFLD at high risk for adverse outcomes.

publication date

  • December 13, 2022

Research

keywords

  • Diabetes Mellitus, Type 2
  • Metabolic Syndrome
  • Non-alcoholic Fatty Liver Disease

Identity

Scopus Document Identifier

  • 85144092565

Digital Object Identifier (DOI)

  • 10.1111/apt.17346

PubMed ID

  • 36511349

Additional Document Info

volume

  • 57

issue

  • 3