PDF(Pubmed)
Abstract：
BACKGROUND: Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE.
METHODS: In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed.
RESULTS: The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child-Pugh score, ammonia level, or the indication for TIPS.
CONCLUSIONS: 3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis.
METHODS: In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed.
RESULTS: The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child-Pugh score, ammonia level, or the indication for TIPS.
CONCLUSIONS: 3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis.
摘要：
背景：术前应预测肝性脑病（HE），以确定经颈静脉肝内门体分流术（TIPS）的合适候选者，而不是一线治疗。本研究旨在构建基于3D评估的模型来预测TIPS后的显性HE。
方法：在这项多中心队列研究中，487名接受TIPS的患者被细分为训练数据集（来自三家医院的390例）和外部验证数据集（来自另外两家医院的97例）。候选因素包括临床，血管,以及2D和3D数据。结合最小绝对收缩和算子方法，支持向量机，和等渗回归的概率校准，我们构建了四个预测模型：临床，2D,3D,和组合模型。将它们的辨别和校准进行比较，以确定最佳模型，进行亚组分析。
结果：3D模型显示出比2D模型更好的辨别力（训练：0.719vs.0.691；验证：0.730vs.0.622）。结合临床和3D因素的模型优于临床和3D模型(训练：0.802vs.0.735vs.0.719；验证：0.816与0.723vs.0.730；所有p<0.050）。此外，组合模型具有最佳的校准。最佳模型的性能不受总胆红素水平的影响，Child-Pugh评分,氨水平，或提示指示。
结论：肝脏和脾脏的3D评估提供了额外的信息来预测明显的HE，改善适合患者的TIPS机会。3D评估也可用于与肝硬化相关的类似研究。
方法：在这项多中心队列研究中，487名接受TIPS的患者被细分为训练数据集（来自三家医院的390例）和外部验证数据集（来自另外两家医院的97例）。候选因素包括临床，血管,以及2D和3D数据。结合最小绝对收缩和算子方法，支持向量机，和等渗回归的概率校准，我们构建了四个预测模型：临床，2D,3D,和组合模型。将它们的辨别和校准进行比较，以确定最佳模型，进行亚组分析。
结果：3D模型显示出比2D模型更好的辨别力（训练：0.719vs.0.691；验证：0.730vs.0.622）。结合临床和3D因素的模型优于临床和3D模型(训练：0.802vs.0.735vs.0.719；验证：0.816与0.723vs.0.730；所有p<0.050）。此外，组合模型具有最佳的校准。最佳模型的性能不受总胆红素水平的影响，Child-Pugh评分,氨水平，或提示指示。
结论：肝脏和脾脏的3D评估提供了额外的信息来预测明显的HE，改善适合患者的TIPS机会。3D评估也可用于与肝硬化相关的类似研究。
