Wilson病脂代谢异常患者发生肝纤维化的列线图预测模型的建立与验证

目的 建立并验证Wilson病（WD）脂代谢异常患者发生肝纤维化的列线图预测模型。方法 回顾性收集2018年12月～2021年12月就诊于安徽中医药大学第一附属医院脑病科的500例WD脂代谢异常患者的临床资料，并将其分为建模人群和验证人群。在建模人群中通过LASSO回归、多因素Logistic回归分析筛选出WD脂代谢异常患者发生肝纤维化的独立危险因素，并对其建立列线图预测模型。采用受试者工作特征曲线（ROC）的曲线下面积（AUC）、校准曲线和决策曲线分别在建模人群和验证人群中对列线图预测模型进行内外部验证以判断其区分度、校准度和临床实用性。结果 甘油三酯、总胆固醇、低密度脂蛋白胆固醇和载脂蛋白B为WD脂代谢异常患者发生肝纤维化的独立危险因素（P<0.05）。列线图预测模型在建模人群和验证人群中均具有良好的区分度、校准度和临床实用性。结论 本研究所建立的列线图预测模型具有较高的准确性，可方便地用于WD脂代谢异常患者发生肝纤维化的早期识别和风险预测。

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

Objective To establish and validate predictive nomogram for liver fibrosis in patients with Wilson disease (WD) showing abnormal lipid metabolism. Methods 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. Results 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. Conclusion 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.