Establishment and validation of a predictive nomogram for liver fibrosis in patients with Wilson disease and abnormal lipid metabolism

Abstract

To establish and validate predictive nomogram for liver fibrosis in patients with Wilson disease (WD) showing abnormal lipid metabolism. We retrospectively collected the clinical data of 500 patients with WD showing abnormalities in lipid metabolism, who were treated in the Department of Encephalopathy of the First Affiliated Hospital of Anhui University of Chinese Medicine from December, 2018 to December, 2021 and divided into modeling group and validation group. The independent risk factors of liver fibrosis in these patients were screened using LASSO regression and multivariate logistic regression analysis for establishment of the predictive nomogram. The area under the curve (AUC), calibration curve and decision curve of the receiver-operating characteristic curve (ROC) were used for internal and external verification of the nomogram in the modeling and validation group and evaluating the differentiation, calibration and clinical practicability of the model. Triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (Apo-B) were independent risk factors for the development of liver fibrosis in patients with WD and abnormal lipid metabolism (P < 0.05). The predictive nomogram showed good discrimination, calibration and clinical utility in both the modeling and validation groups. The established predictive nomogram in this study has a high accuracy for early identification and risk prediction of liver fibrosis in patients with WD having abnormal lipid metabolism.