A Sequential Machine Learning Model for Identifying At-risk NASH by Combining Liver Stiffness Measurement and Protein Biomarkers

Background: A number of protein biomarkers have been proposed for the assessment of non-alcoholic fatty liver disease (NAFLD), but few have been externally validated and directly compared. The aim of this study was to compare the diagnostic accuracies of 13 protein biomarkers and develop a biomarker-based machine learning algorithm to predict at-risk non-alcoholic steatohepatitis (NASH) in patients with NAFLD. Methods: 281 NAFLD patients had blood biomarker tested within one week before liver biopsy. We used three machine learning methods to select biomarkers in training (70%) and testing (30%) datasets, and then input selected features into a logistic regression model to predict at-risk NASH (NAFLD activity score ≥4 with at least 1 point in each component and fibrosis stage ≥2). Results: Among 13 protein biomarkers tested, growth differentiation factor-15 (GDF-15) and Pro-C3 had the highest accuracy for at-risk NASH and advanced fibrosis (F3-4), respectively. All three machine learning models selected GDF-15, Pro-C3, and tissue inhibitor matrix metalloproteinase 1 as the best predictors of at-risk NASH, and the logistic regression FibNASH-3 model had an area under receiver-operating characteristics curve of 0.784. Using LSM ≥8kPa as the first step, the sequential model had a 79.2% positive predictive value for at-risk NASH. During a mean follow-up of 9.7 years, 3% and 16.7% of patients with low and high sequential model score developed hepatocellular carcinoma and cirrhotic complications, respectively (PConclusions: The study provides a head-to-head comparison of 13 protein biomarkers. FibNASH-3 has the ability to accurately identify at-risk NASH in NAFLD patients and predict the development of liver-related events.