AUTHOR=Zhou Jiafeng , Li Zuolong , Wang Junwei , Jiang Zhenzhen , Wang Tao , Xie Tianyu , Wang Liangchen , Kang Shuai , Tao Zhuang , Wang Meixia 

TITLE=Risk factor analysis and nomogram development for advanced-stage hepatic fibrosis in patients with Wilson’s disease

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1650584

DOI=10.3389/fmed.2025.1650584

ISSN=2296-858X

ABSTRACT=PurposeTo investigate the risk factors for advanced-stage hepatic fibrosis in Wilson’s disease (WD), and developed a predictive nomogram to screen high risk patients with WD for early prevention and intervention.MethodsWe retrospectively analyzed clinical data from WD in The First Affiliated Hospital of Anhui University of Chinese medicine between January 2010 and December 2024. Patients were divided into advanced hepatic fibrosis and non-advanced fibrosis groups according liver stiffness measurement. Identification of the independent risk factors for advanced hepatic fibrosis in WD was conducted through univariate and multivariate Cox regression analyses, followed by the construction of the clinical predictive model. The discriminative power, calibration, and clinical utility of the model were validated by receiver operating characteristic, calibration curves, and decision curve analysis (DCA).ResultsThe study cohort comprised 221 patients. Notably, CER, LN, HDL-C, TG, PLT, Sex, and Apo-A1 were identified as independent risk factors for advanced hepatic fibrosis in WD patients undergoing long-term maintenance therapy. The C-index demonstrated excellent discriminative capacity [training cohort: area under the curve (AUC) values of 0.918 at 36 months, 0.914 at 60 months, and 0.935 at 84 months; validation cohort: AUC values of 0.906, 0.917, and 0.888 at corresponding time points]. Calibration curves exhibited strong alignment between predicted and observed outcomes. The DCA quantified clinical benefit probability thresholds across varying time intervals.ConclusionThe nomogram predictive model demonstrated high accuracy and provides a practical tool for the early identification and risk prediction of advanced hepatic fibrosis in WD patients undergoing long-term maintenance therapy.