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Abstract Details
A practical use of noninvasive tests in clinical practice to identify high-risk patients with nonalcoholic steatohepatitis
1Inova Medicine, Inova Health System, Falls Church, Virginia, USA.
2Liver and Obesity Research Program, Inova Health System, Falls Church, Virginia, USA.
3Center for Liver Diseases, Department of Medicine, Inova Fairfax Medical Campus, Falls Church, Virginia, USA.
4Arizona Liver Health, Chandler, Arizona, USA.
5Division of Endocrinology, Diabetes and Metabolism, University of Florida, Gainesville, Florida, USA.
6Division of Endocrinology, Emory University School of Medicine, Atlanta, Georgia, USA.
7Baylor College of Medicine, Michael E.D. Bakey VA Medical Center, Houston, Texas, USA.
8Fatty Liver Program at Cedars-Sinai Medical Center, West Hollywood, California, USA.
9NAFLD Research Center, Division of Gastroenterology and Hepatology, Department of Medicine, University of California San Diego, La Jolla, California, USA.
10Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
11George Washington Medical Faculty Associates, Washington, District of Columbia, USA.
12Center for Outcomes Research in Liver Diseases, The Global NASH Council, Washington, District of Columbia, USA.
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.