| Autor: |
A. Sidney Barritt, Stephanie Watkins, Norman Gitlin, Samuel Klein, Anna S. Lok, Rohit Loomba, Cheryl Schoen, K. Rajender Reddy, Huy Ngoc Trinh, Andrea R. Mospan, Miriam B. Vos, L. Michael Weiss, Kenneth Cusi, Brent A. Neuschwander‐Tetri, Arun J. Sanyal |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Hepatology Communications, Vol 5, Iss 6, Pp 938-946 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2471-254X |
| DOI: |
10.1002/hep4.1689 |
| Popis: |
Much of the current data on nonalcoholic fatty liver disease (NAFLD) are derived from biopsy‐based studies that may introduce ascertainment and selection bias. Selection of patients for liver biopsy has implications for clinical practice and the reported epidemiology of NAFLD. The aim of this study was to determine patient factors predictive of histologic versus empiric clinical diagnosis of NAFLD in real‐world practice. Adults from TARGET‐NASH were included in this study. Descriptive statistics are provided for the cohort and compare the characteristics of histologic NAFLD versus patients with clinically diagnosed NAFLD, followed by logistic regression and machine‐learning models to describe predictors of liver biopsy. The records of 3,474 subjects were analyzed; median age was 59 years, 59% were female, 75% were White, and median body mass index was 32 kg/m2. Using histologic and/or clinical criteria, a diagnosis of nonalcoholic steatohepatitis was made in 37%, and cirrhosis in 33%. Comorbid conditions included cardiovascular disease (19%), mental health diagnoses (49%), and osteoarthritis (10%). Predictors of a biopsy diagnosis included White race, female sex, diabetes, and elevated alanine aminotransferase (ALT). ALT increased the odds of liver biopsy by 14% per 10‐point rise. Machine‐learning analyses showed non‐White patients with ALT |
| Databáze: |
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| Externí odkaz: |
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