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Abstract：
BACKGROUND: The purpose of this study was to explore the relevant risk factors associated with biliary complications (BCs) in patients with end-stage hepatic alveolar echinococcosis (HAE) following ex vivo liver resection and autotransplantation (ELRA) and to establish and visualize a nomogram model.
METHODS: This study retrospectively analysed patients with end-stage HAE who received ELRA treatment at the First Affiliated Hospital of Xinjiang Medical University between August 1, 2010 and May 10, 2023. The least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize the feature variables for predicting the incidence of BCs following ELRA. Multivariate logistic regression analysis was used to develop a prognostic model by incorporating the selected feature variables from the LASSO regression model. The predictive ability, discrimination, consistency with the actual risk, and clinical utility of the candidate prediction model were evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Internal validation was performed by the bootstrapping method.
RESULTS: The candidate prediction nomogram included predictors such as age, hepatic bile duct dilation, portal hypertension, and regular resection based on hepatic segments. The model demonstrated good discrimination ability and a satisfactory calibration curve, with an area under the ROC curve (AUC) of 0.818 (95% CI 0.7417-0.8958). According to DCA, this prediction model can predict the risk of BCs occurrence within a probability threshold range of 9% to 85% to achieve clinical net benefit.
CONCLUSIONS: A prognostic nomogram with good discriminative ability and high accuracy was developed and validated to predict BCs after ELRA in patients with end-stage HAE.
METHODS: This study retrospectively analysed patients with end-stage HAE who received ELRA treatment at the First Affiliated Hospital of Xinjiang Medical University between August 1, 2010 and May 10, 2023. The least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize the feature variables for predicting the incidence of BCs following ELRA. Multivariate logistic regression analysis was used to develop a prognostic model by incorporating the selected feature variables from the LASSO regression model. The predictive ability, discrimination, consistency with the actual risk, and clinical utility of the candidate prediction model were evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Internal validation was performed by the bootstrapping method.
RESULTS: The candidate prediction nomogram included predictors such as age, hepatic bile duct dilation, portal hypertension, and regular resection based on hepatic segments. The model demonstrated good discrimination ability and a satisfactory calibration curve, with an area under the ROC curve (AUC) of 0.818 (95% CI 0.7417-0.8958). According to DCA, this prediction model can predict the risk of BCs occurrence within a probability threshold range of 9% to 85% to achieve clinical net benefit.
CONCLUSIONS: A prognostic nomogram with good discriminative ability and high accuracy was developed and validated to predict BCs after ELRA in patients with end-stage HAE.
摘要：
背景：本研究的目的是探讨终末期肝泡型包虫病（HAE）患者离体肝切除和自体移植（ELRA）后胆道并发症（BCs）的相关危险因素，并建立和可视化列线图模型。
方法：本研究回顾性分析2010年8月1日至2023年5月10日在新疆医科大学第一附属医院接受ELRA治疗的终末期HAE患者。应用最小绝对收缩和选择算子（LASSO）回归模型来优化特征变量，以预测ELRA后BCs的发生率。通过纳入从LASSO回归模型中选择的特征变量，使用多变量逻辑回归分析来建立预后模型。预测能力，歧视,与实际风险的一致性，使用受试者工作特征（ROC）曲线评估候选预测模型的临床实用性，校准图,和决策曲线分析(DCA)。内部验证通过引导方法执行。
结果：候选预测列线图包括预测因子，例如年龄，肝胆管扩张，门静脉高压症,并根据肝段进行定期切除。该模型表现出良好的辨别能力和令人满意的校准曲线，ROC曲线下面积（AUC）为0.818（95%CI0.7417-0.8958）。根据DCA,该预测模型可以在9％至85％的概率阈值范围内预测BCs发生的风险，以实现临床净收益。
结论：开发并验证了具有良好辨别能力和高准确性的预后列线图，以预测终末期HAE患者ELRA后的BCs。
方法：本研究回顾性分析2010年8月1日至2023年5月10日在新疆医科大学第一附属医院接受ELRA治疗的终末期HAE患者。应用最小绝对收缩和选择算子（LASSO）回归模型来优化特征变量，以预测ELRA后BCs的发生率。通过纳入从LASSO回归模型中选择的特征变量，使用多变量逻辑回归分析来建立预后模型。预测能力，歧视,与实际风险的一致性，使用受试者工作特征（ROC）曲线评估候选预测模型的临床实用性，校准图,和决策曲线分析(DCA)。内部验证通过引导方法执行。
结果：候选预测列线图包括预测因子，例如年龄，肝胆管扩张，门静脉高压症,并根据肝段进行定期切除。该模型表现出良好的辨别能力和令人满意的校准曲线，ROC曲线下面积（AUC）为0.818（95%CI0.7417-0.8958）。根据DCA,该预测模型可以在9％至85％的概率阈值范围内预测BCs发生的风险，以实现临床净收益。
结论：开发并验证了具有良好辨别能力和高准确性的预后列线图，以预测终末期HAE患者ELRA后的BCs。
