Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy‐confirmed NAFLD

Abstract Background The presence of significant liver fibrosis is a key determinant of long‐term prognosis in non‐alcoholic fatty liver disease (NAFLD). We aimed to develop a novel machine learning algorithm (MLA) to predict fibrosis severity in NAFLD and compared it with the most widely used non‐invasive fibrosis biomarkers. Methods We used a cohort of 553 adults with biopsy‐proven NAFLD, who were randomly divided into a training cohort (n = 278) for the development of both logistic regression model (LRM) and MLA, and a validation cohort (n = 275). Significant fibrosis was defined as fibrosis stage F ≥ 2. MLA and LRM were derived from variables that were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. Results In the training cohort, the variables selected by LASSO algorithm were body mass index, pro‐collagen type III, collagen type IV, aspartate aminotransferase and albumin‐to‐globulin ratio. The diagnostic accuracy of MLA showed the highest values of area under the receiver operator characteristic curve (AUROC: 0.902, 95% CI 0.869‐0.904) for identifying fibrosis F ≥ 2. The LRM AUROC was 0.764, 95% CI 0.710‐0.816 and significantly better than the AST‐to‐Platelet ratio (AUROC 0.684, 95% CI 0.605‐0.762), FIB‐4 score (AUROC 0.594, 95% CI 0.503‐0.685) and NAFLD Fibrosis Score (AUROC 0.557, 95% CI 0.470‐0.644). In the validation cohort, MLA also showed the highest AUROC (0.893, 95% CI 0.864‐0.901). The diagnostic accuracy of MLA outperformed that of LRM in all subgroups considered. Conclusions Our newly developed MLA algorithm has excellent diagnostic performance for predicting fibrosis F ≥ 2 in patients with biopsy‐confirmed NAFLD.