Xia Pan, Wang Xiang-Qian, Shang-Guan Chao-Liang, Wang Zheng, Xu Wei, Wang Jin-Peng, Liu Zhen, Zhu Hai-Hong
Department of Graduate School, Qinghai University, Xining, Qinghai Province, China.
Department of General Surgery, Qinghai Provincial People's Hospital, Xining, Qinghai Province, China.
Saudi J Gastroenterol. 2025 Mar 1;31(2):75-81. doi: 10.4103/sjg.sjg_240_24. Epub 2025 Jan 31.
Patients with multiple organ metastases from hepatic alveolar echinococcosis have a high mortality rate. However, predictors of multi-organ metastasis have not been identified. We aimed to develop a nomogram that predicts multi-organ metastasis in patients with hepatic alveolar echinococcosis.
We retrospectively screened patients with hepatic alveolar echinococcosis who were treated between January 2016 and December 2021 at Qinghai Provincial People's Hospital, China. The outcome of the nomogram was multi-organ metastasis of hepatic alveolar echinococcosis. We collected patients' basic characteristics, disease course, imaging, and blood laboratory results. The Least Absolute Shrinkage Selection Operator (LASSO) analysis selected the predictors preliminarily. A predictive model was constructed by multivariate logistic regression and presented as a nomogram. The performance of the nomogram was measured by the receiver operating characteristic (ROC) curve, calibration diagram, and decision curve analysis (DCA). The model was internally validated by calculating the performance of the validation cohort.
A total of 353 patients were enrolled in this study. Ninety five (26.9%) patients presented with multi-organ metastases. All participants were randomized into a development cohort ( n = 249) and a validation cohort ( n = 104). Predictors in this nomogram were the course of the disease, the long diameter of the lesion, multiple intrahepatic lesions, and medication. The ROC curve of the training set was 0.907 (95% CI: 0.870, 0.943). A similar ROC curve was achieved at the validation set (0.927, 95% CI: 0.876, 0.979). The calibration curve demonstrated that the prediction outcome was correlated with the observed outcome.
The nomogram can predict the risk of multi-organ metastasis in patients with hepatic alveolar echinococcosis, and help clinicians develop or adjust a reasonable diagnosis and treatment plan in time.
肝泡型包虫病多器官转移患者死亡率高。然而，多器官转移的预测因素尚未明确。我们旨在开发一种预测肝泡型包虫病患者多器官转移的列线图。
我们回顾性筛选了2016年1月至2021年12月在中国青海省人民医院接受治疗的肝泡型包虫病患者。列线图的结果是肝泡型包虫病的多器官转移。我们收集了患者的基本特征、病程、影像学和血液实验室检查结果。最小绝对收缩选择算子（LASSO）分析初步筛选出预测因素。通过多因素逻辑回归构建预测模型并以列线图形式呈现。通过受试者工作特征（ROC）曲线、校准图和决策曲线分析（DCA）评估列线图的性能。通过计算验证队列的性能对模型进行内部验证。
本研究共纳入353例患者。95例（26.9%）患者出现多器官转移。所有参与者被随机分为开发队列（n = 249）和验证队列（n = 104）。该列线图的预测因素为病程、病灶长径、肝内多发病灶和用药情况。训练集的ROC曲线为0.907（95%CI：0.870，0.943）。验证集也获得了类似的ROC曲线（0.927，95%CI：0.876，0.979）。校准曲线表明预测结果与观察结果相关。
该列线图可预测肝泡型包虫病患者多器官转移的风险，并有助于临床医生及时制定或调整合理的诊断和治疗方案。
