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

TL;DR

This study identifies risk factors for advanced liver fibrosis in Wilson’s disease and creates a predictive model to help identify high-risk patients early.

Contribution

A novel nomogram model for predicting advanced hepatic fibrosis in Wilson’s disease patients undergoing long-term therapy.

Findings

CER, LN, HDL-C, TG, PLT, Sex, and Apo-A1 are independent risk factors for advanced hepatic fibrosis in WD.

The nomogram showed excellent discriminative power with AUC values above 0.9 in both training and validation cohorts.

Calibration curves and decision curve analysis confirmed the model's strong predictive accuracy and clinical utility.

Abstract

To 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.

We 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).

The 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.

The 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.

Figures