A non-invasive prediction model for non-alcoholic steatohepatitis in paediatric patients with non-alcoholic fatty liver disease

BackgroundNon-alcoholic fatty liver disease encompasses a spectrum of diseases that range from simple steatosis to the aggressive form of non-alcoholic steatohepatitis. Non-alcoholic steatohepatitis is currently diagnosed through liver biopsy.AimTo develop a non-invasive predictive model of non-alcoholic steatohepatitis in children with non-alcoholic fatty liver disease.MethodsAnthropometric, laboratory, and histologic data were obtained in a cohort of children with biopsy-proven non-alcoholic fatty liver disease. Multivariable logistic regression analysis was employed to create a nomogram predicting the risk of non-alcoholic steatohepatitis. Internal validation was performed by bootstrapping.ResultsThree hundred and two children were included in this analysis with a mean age of 12.3 ± 3.1 years, a mean body mass index percentile of 94.3 ± 6.9, and non-alcoholic steatohepatitis was present in 67%. Following stepwise variable selection, total cholesterol, waist circumference percentile, and total bilirubin were included as variables in the model, with good discrimination with an area under the receiver operating characteristic curve of 0.737.ConclusionsA nomogram was constructed with reasonable accuracy that can predict the risk of non-alcoholic steatohepatitis in children with non-alcoholic fatty liver disease. If validated externally, this tool could be utilized as a non-invasive method to diagnose non-alcoholic steatohepatitis in children with non-alcoholic fatty liver disease.