A Non-Invasive Prediction Model for Non-Alcoholic Steatohepatitis (NASH) in Pediatric Patients with Non-Alcoholic Fatty Liver Disease (NAFLD)

Abstract

Purpose: NAFLD affects around 10% of children in the United States, and it encompasses a spectrum of diseases that range from simple steatosis to the aggressive form of NASH. Our study aim was to develop a non-invasive prediction model for NASH in pediatric patients with NAFLD. Methods: Anthropometric, laboratory, and histologic data were obtained in a cohort of children with biopsy-proven NAFLD. Multivariable logistic regression analysis was employed to create a nomogram predicting the risk of NASH on liver biopsy. An automated stepwise variable selection method performed on 1,000 bootstrap samples was used to choose the final model. Internal validation of the model was performed by means of bootstrapping, and calibration was assessed graphically. Results: A total of 302 pediatric patients were included in this analysis, with a mean age of 12.3 ± 3.1 years and a mean BMI percentile of 94.3 ± 6.9. NASH was present in 67% of patients. Following stepwise variable selection, total cholesterol, waist circumference percentile, and total bilirubin were included as predictive variables in the model. The nomogram demonstrated good discrimination with an area under the receiver operating characteristics curve of 0.737. The calibration curve was also noted to have good agreement between observed and predicted probabilities. Conclusion: We have constructed a nomogram with reasonable accuracy that can predict the risk of NASH in pediatric patients with NAFLD. This nomogram utilizes simple clinical variables and routine tests, which are readily available to most practitioners.Figure 1: Nomogram for the prediction of NASH in pediatric NAFLD.Figure 2: Receiver operating characteristics curve.