Evidence map›Paper›PMID 40809427›Full record

ArticleFrontiers in medicine2025

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

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

Abstract read
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Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

10 authors.

Jiafeng ZhouEncephalopathy Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
Zuolong LiCollege of Resources and Environment, Anhui Agricultural University, Hefei, Anhui, China.
Junwei WangGraduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China.
Zhenzhen JiangGraduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China.
Tao WangGraduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China.
Tianyu XieGraduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China.
Liangchen WangGraduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China.
Shuai KangGraduate School, Anhui University of Chinese Medicine, Hefei, Anhui, China.
Zhuang TaoEncephalopathy Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
Meixia WangEncephalopathy Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: 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. Methods: 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). Results: 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. Conclusion: 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.

Indexed as

advanced hepatic fibrosispredictive modelretrospective studyrisk factorsWilson’s disease

Identifiers

PMID40809427
PMCPMC12343619

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