4.7 Review

Role of Noninvasive Tests in Clinical Gastroenterology Practices to Identify Patients With Nonalcoholic Steatohepatitis at High Risk of Adverse Outcomes: Expert Panel Recommendations

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AMERICAN JOURNAL OF GASTROENTEROLOGY
卷 116, 期 2, 页码 254-262

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LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.14309/ajg.0000000000001054

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  1. Intercept Pharmaceuticals

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Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are common liver diseases often associated with obesity, insulin resistance, and type 2 diabetes mellitus. The stage of fibrosis is the most important predictor of liver outcomes in NAFLD and NASH, underscoring the importance of early identification of patients with advanced fibrosis.
Nonalcoholic fatty liver disease (NAFLD) is generally considered a silent and potentially reversible condition. The subtype of NAFLD that can be classified as nonalcoholic steatohepatitis (NASH) can progress to advanced fibrosis and cirrhosis. Because of the metabolic nature of the pathogenic mechanism underlying NAFLD and NASH, it is often accompanied by common comorbidities such as obesity, insulin resistance, and type 2 diabetes mellitus. The increase in the prevalence of these comorbidities has resulted in a parallel increase in the prevalence of NAFLD and NASH, globally, nationally, and even in children. In recent years, it has been identified that the stage of fibrosis is the most important predictor of liver outcomes; therefore, identifying patients with NAFLD and NASH with more advanced stages of fibrosis can be essential for optimal management. Several noninvasive tools for diagnosing and staging NAFLD and NASH are available, but simple and straightforward recommendations on the use of these tools are not. Recognizing these unmet needs, hepatologists who are members of the American College of Gastroenterology and the Chronic Liver Disease Foundation created a practical decision tree/algorithm to risk stratify NAFLD/NASH as a resource in gastroenterology/hepatology clinical practices. This review will provide insight into how this algorithm was developed, describe it in detail, and provide recommendations for its use in clinical practice.

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