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Limitations of Noninvasive Tests-Based Population-Level Risk Stratification Strategy for Nonalcoholic Fatty Liver Disease.
Behari, Jaideep; Bradley, Allison; Townsend, Kevin; Becich, Michael J; Cappella, Nickie; Chuang, Cynthia H; Fernandez, Soledad A; Ford, Daniel E; Kirchner, H Lester; Morgan, Richard; Paranjape, Anuradha; Silverstein, Jonathan C; Williams, David A; Donahoo, W Troy; Asrani, Sumeet K; Ntanios, Fady; Ateya, Mohammad; Hegeman-Dingle, Rozelle; McLeod, Euan; McTigue, Kathleen.
Afiliación
  • Behari J; Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Suite 201, Kaufmann Medical Building, 3471 Fifth Ave, Pittsburgh, PA, 15213, USA. behajx@upmc.edu.
  • Bradley A; Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15206, USA.
  • Townsend K; US Medical Affairs, Pfizer Inc, New York, NY, 10017, USA.
  • Becich MJ; Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15206, USA.
  • Cappella N; Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15206, USA.
  • Chuang CH; Division of General Internal Medicine, Penn State College of Medicine, Hershey, PA, 17033, USA.
  • Fernandez SA; Department of Biomedical Informatics, Wexner Medical Center, The Ohio State University, Columbus, OH, 43201, USA.
  • Ford DE; Department of General Internal Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA.
  • Kirchner HL; Department of Population Health Sciences, Geisinger Health System, Danville, PA, 17822, USA.
  • Morgan R; Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15206, USA.
  • Paranjape A; Department of Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, 19140, USA.
  • Silverstein JC; Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15206, USA.
  • Williams DA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI, 48105, USA.
  • Donahoo WT; Division of Endocrinology, Diabetes and Metabolism, University of Florida, Gainesville, FL, 32608, USA.
  • Asrani SK; Baylor University Medical Center, Dallas, TX, 75246, USA.
  • Ntanios F; US Medical Affairs, Pfizer Inc, New York, NY, 10017, USA.
  • Ateya M; US Medical Affairs, Pfizer Inc, New York, NY, 10017, USA.
  • Hegeman-Dingle R; US Medical Affairs, Pfizer Inc, New York, NY, 10017, USA.
  • McLeod E; Pfizer Health Economics and Outcomes Research, Tadworth, UK.
  • McTigue K; Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA.
Dig Dis Sci ; 69(2): 370-383, 2024 Feb.
Article en En | MEDLINE | ID: mdl-38060170
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are highly prevalent but underdiagnosed. AIMS: We used an electronic health record data network to test a population-level risk stratification strategy using noninvasive tests (NITs) of liver fibrosis. METHODS: Data were obtained from PCORnet® sites in the East, Midwest, Southwest, and Southeast United States from patients aged [Formula: see text] 18 with or without ICD-10-CM diagnosis codes for NAFLD, NASH, and NASH-cirrhosis between 9/1/2017 and 8/31/2020. Average and standard deviations (SD) for Fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), and Hepatic Steatosis Index (HSI) were estimated by site for each patient cohort. Sample-wide estimates were calculated as weighted averages across study sites. RESULTS: Of 11,875,959 patients, 0.8% and 0.1% were coded with NAFLD and NASH, respectively. NAFLD diagnosis rates in White, Black, and Hispanic patients were 0.93%, 0.50%, and 1.25%, respectively, and for NASH 0.19%, 0.04%, and 0.16%, respectively. Among undiagnosed patients, insufficient EHR data for estimating NITs ranged from 68% (FIB-4) to 76% (NFS). Predicted prevalence of NAFLD by HSI was 60%, with estimated prevalence of advanced fibrosis of 13% by NFS and 7% by FIB-4. Approximately, 15% and 23% of patients were classified in the intermediate range by FIB-4 and NFS, respectively. Among NAFLD-cirrhosis patients, a third had FIB-4 scores in the low or intermediate range. CONCLUSIONS: We identified several potential barriers to a population-level NIT-based screening strategy. HSI-based NAFLD screening appears unrealistic. Further research is needed to define merits of NFS- versus FIB-4-based strategies, which may identify different high-risk groups.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Enfermedad del Hígado Graso no Alcohólico Límite: Aged / Humans Idioma: En Revista: Dig Dis Sci Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Enfermedad del Hígado Graso no Alcohólico Límite: Aged / Humans Idioma: En Revista: Dig Dis Sci Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos