Validation of the accuracy of the FAST™ score for detecting patients with at-risk nonalcoholic steatohepatitis (NASH) in a North American cohort and comparison to other non-invasive algorithms.

Tinsay A WoretaMark L Van NattaMariana LazoArunkumar KrishnanBrent A Neuschwander-TetriRohit LoombaAnna Mae DiehlManal F AbdelmalekNaga ChalasaniSamer GawriehSrinivasan DasarathyRaj K VuppalanchiMohammad S SiddiquiKris V KowdleyArthur McCulloughNorah A TerraultCynthia BehlingDavid E KleinerMark FishbeinPaula HertelLaura A WilsonEmily P MitchellLaura A MirielJeanne M ClarkJames TonasciaArun J Sanyalnull null

Published in: PloS one (2022)

We validated the FAST™ score for the diagnosis of at-risk NASH in a large, multi-racial population in North America, with a prevalence of at-risk NASH of 37%. Diagnostic performance varies by subgroups of NASH patients defined by race and obesity. FAST™ performed better than other non-invasive algorithms for the diagnosis of at-risk NASH.

Keyphrases

machine learning
end stage renal disease
ejection fraction
metabolic syndrome
newly diagnosed
deep learning
chronic kidney disease
type diabetes
insulin resistance
peritoneal dialysis
risk factors
prognostic factors
weight loss
body mass index
adipose tissue
physical activity
african american
skeletal muscle