Patient Determinants for Histologic Diagnosis of NAFLD in the Real World: A TARGET-NASH Study.
Alfred Sidney BarrittStephanie WatkinsNorman GitlinSamuel KleinAnna S LokRohit LoombaCheryl SchoenRajender K ReddyHuy Ngoc TrinhAndrea R MospanMiriam B VosL Michael WeissKenneth CusiBrent A Neuschwander-TetriArun J SanyalPublished in: Hepatology communications (2021)
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 <69 had only a 0.06 probability of undergoing liver biopsy. ALT was the dominant variable that determined liver biopsy. Conclusions: In this real-world cohort of patients with NAFLD, two-thirds of patients did not have a liver biopsy. These patients were more likely to be non-White, older, with a normal ALT, showing potential gaps in or knowledge about this population.
Keyphrases
- machine learning
- cardiovascular disease
- ultrasound guided
- end stage renal disease
- body mass index
- newly diagnosed
- ejection fraction
- fine needle aspiration
- mental health
- healthcare
- type diabetes
- prognostic factors
- peritoneal dialysis
- clinical practice
- physical activity
- primary care
- rheumatoid arthritis
- metabolic syndrome
- electronic health record
- patient reported outcomes
- adipose tissue
- risk assessment
- artificial intelligence
- middle aged
- weight loss
- quality improvement
- data analysis