| 冯斯奕,涂海斌,陈丽红,张菊珍,彭素妤.超声结合血清学指标对自身免疫性肝病相关肝硬化的预测价值[J].现代生物医学进展英文版,2023,(13):2472-2478. |
| 超声结合血清学指标对自身免疫性肝病相关肝硬化的预测价值 |
| Predictive Value of the Autoimmune Liver Disease Related Cirrhosis by Ultrasound Combined with Serological Indexes |
| Received:January 23, 2023 Revised:February 18, 2023 |
| DOI：10.13241/j.cnki.pmb.2023.13.014 |
| 中文关键词: 自身免疫性肝病相关肝硬化 超声 血清学 预测 无创模型 |
| 英文关键词: Autoimmune liver disease related cirrhosis Ultrasound Serology Prediction Noninvasive model |
| 基金项目:福州市科技计划基金项目(2021-S-109)；福建省自然科学基金项目（2022J011285） |
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| 中文摘要: |
| 摘要 目的：本研究旨在结合二维剪切波弹性成像（2D-SWE）技术、超声特征及血清学指标构建自身免疫性肝病（AILD）相关肝硬化的预测模型并评估该模型的预测效能。方法：收集2019年 月至2022年5月于福建医科大学孟超肝胆医院行肝脏活检确诊为AILD，并行肝胆脾超声、2D-SWE及相关血清学检查的患者。根据病理结果，分为肝硬化组与非肝硬化组。通过多因素logistic回归分析筛选出AILD相关肝硬化的独立风险因素，构建AILD相关肝硬化的列线图模型（AILDC）。采用Bootstrap法对模型进行内部验证，绘制ROC曲线，校准曲线及临床决策曲线评估模型的区分度、校准度及临床净获益。结果：共纳入AILD255例，肝硬化组共45例。logistic回归分析结果显示：肝硬度（OR:1.322，95%CI:1.186-1.474），脾脏厚度＞4 cm（OR:5.154，95%CI: 1.943-13.674），补体C4（OR:0.001，95%CI:0.000-0.674 ），高尔基体-73（OR:1.014，95%CI: 1.002-1.027 ）均是AILD肝硬化独立预测指标。AILDC的最佳截断值为80，敏感度84.4%，特异度78.6%；曲线下面积（Area under curve, AUC）0.866。AILD肝硬度的最佳截断值为10 Kpa，敏感度为71.1%，特异度为85.2%，AUC为0.803。相较于其它无创指标，AILDC具有更高的净重新分类指数、综合判别改善指数及临床决策曲线。结论：AILDC具有较好的预测效能，优于其他无创指标，适于在临床中运用并进一步推广。 |
| 英文摘要: |
| ABSTRACT Objective: To construct a prediction model about the autoimmune liver disease(AILD) related cirrhosis based on two-dimensional shear wave elasticity (2D-SWE), ultrasound and serological indexes, and to evaluate the predicting effciency. Objective To construct a prediction model about the autoimmune liver disease(AILD) related cirrhosis based on two-dimensional shear wave elasticity (2D-SWE), ultrasound and serological indexes, and to evaluate the predicting effciency. Methods: Patients with AILD confirmed by liver biopsy with liver ultrasound, 2D-SWE and serological examination were collected from 2019.01 to 2022.05. Patients were divided into cirrhotic and non-cirrhotic groups. Independent risk factors for the AILD related cirrhosis were selected by multivariate logistic regression analysis, and a nomogram model(AILDC) of the AILD related cirrhosis was constructed. The internal was validated on the model by Bootstrap method, and the Receiver Operating Charateristic curve, calibration curve and clinical decision curve were drew to evaluate the differentiation degree, calibration degree and the clinical net benefit of the model. Results: A total of 255 patients, 45 had liver cirrhosis. Multivariate logistic regression analysis showed that liver stiffness (OR:1.322,95%CI:1.186-1.474), spleen thickness> 4 cm (OR: 5.154,95%CI: 1.943-13.674), complement C4(OR:0.001,95%CI:0.000-0.674), and the Golgi apparatus protein-73 (OR: 1.014,95%CI: 1.0 0 2-1.027) were all independent predictors of AILD related cirrhosis. The optimal cut-off for AILDC was 80, sensitivity 84.4%, specificity 78.6%, the area under curve(AUC) was 0.866. The optimal cutoff value for AILD liver stiffness was 10 Kpa, with the sensitivity of 71.1%, and the specificity of 85.2%, and an AUC of 0.803. Compared with other non-invasive indicators, AILDC has a higher net reclassification index, comprehensive discriminant improvement index, and clinical decision curve. Conclusion: The AILDC has better predictive efficacy than other noninvasive indices, and worth further promotion in clinical practice. |
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