Predicting Hepatic Fibrosis Risk in Wilson's Disease： Development and Validation of Prognostic Model Integrating TCM Syndromes and Modern Biomarkers
10.13422/j.cnki.syfjx.20260794
- VernacularTitle:基于中医证型及现代医学指标的Wilson病肝纤维化风险预测模型的建立与验证
- Author:
Jiafeng ZHOU
1
;
Meixia WANG
1
;
Zhuang TAO
1
;
Shuai KANG
1
;
Gang WANG
1
;
Rui WANG
1
;
Wenming YANG
1
Author Information
1. The First Affiliated Hospital of Anhui University of Chinese Medicine，Hefei 230031，China
- Publication Type:Journal Article
- Keywords:
Wilson's disease;
hepatic fibrosis;
traditional Chinese medicine （TCM） syndrome;
TCM intervention;
predictive model
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2026;32(18):197-207
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo analyze the influencing factors of hepatic fibrosis in patients with Wilson's disease （WD） and to develop and validate a predictive model for hepatic fibrosis in WD that integrates traditional Chinese medicine （TCM） syndromes and modern biomarkers， thus providing a basis for early identification of high-risk populations and medical intervention. MethodsThe clinical data of 220 WD patients diagnosed in the Department of Encephalopathy， The First Affiliated Hospital of Anhui University of Chinese Medicine between January 2010 and December 2024 were retrospectively collected. Through a random number table， patients were assigned into a training set （154 patients） and a validation set （66 patients） in a 7∶3 ratio. Univariate COX regression， LASSO regression analysis， and multivariate COX regression analysis were performed to identify independent influencing factors for hepatic fibrosis in WD patients， and a clinical prediction model was established based on these factors. The discrimination， calibration， and clinical utility of the model were evaluated based on the area under the receiver operating characteristic curve （AUC）， calibration curves， and decision curve analysis （DCA）， respectively. ResultsA total of 220 patients were included. The syndrome of combined phlegm and stasis， triglycerides （TG）， laminin （LN）， and male gender were identified as independent risk factors for hepatic fibrosis in WD patients， while TCM intervention and high-density lipoprotein cholesterol （HDL-C） were protective factors. The C-index indicated excellent discriminative ability （training set： 3-year AUC=0.908， 5-year AUC=0.869， 7-year AUC=0.829； validation set： 3-year AUC= 0.898， 5-year AUC=0.780， 7-year AUC=0.743）. Calibration curves showed good consistency. DCA demonstrated the clinical net benefit probabilities across different risk thresholds at various time points. ConclusionThe predictive model established in this study has high accuracy and can be conveniently used for the early identification and risk prediction of hepatic fibrosis in patients with WD.
