Abstract：
OBJECTIVE: The transjugular intrahepatic portosystemic shunt has controversial survival benefits; thus, patient screening should be performed preoperatively. In this study, we aimed to develop a model to predict post-transjugular intrahepatic portosystemic shunt mortality to aid clinical decision making.
METHODS: A total of 811 patients undergoing transjugular intrahepatic portosystemic shunt from five hospitals were divided into the training and external validation data sets. A modified prediction model of post-transjugular intrahepatic portosystemic shunt mortality (ModelMT ) was built after performing logistic regression. To verify the improved performance of ModelMT , we compared it with seven previous models, both in discrimination and calibration. Furthermore, patients were stratified into low-, medium-, high- and extremely high-risk subgroups.
RESULTS: ModelMT demonstrated a satisfying predictive efficiency in both discrimination and calibration, with an area under the curve of .875 in the training set and .852 in the validation set. Compared to previous models (ALBI, BILI-PLT, MELD-Na, MOTS, FIPS, MELD, CLIF-C AD), ModelMT showed superior performance in discrimination by statistical difference in the Delong test, net reclassification improvement and integrated discrimination improvement (all p < .050). Similar results were observed in calibration. Low-, medium-, high- and extremely high-risk groups were defined by scores of ≤160, 160-180, 180-200 and >200, respectively. To facilitate future clinical application, we also built an applet for ModelMT .
CONCLUSIONS: We successfully developed a predictive model with improved performance to assist in decision making for transjugular intrahepatic portosystemic shunt according to survival benefits.
METHODS: A total of 811 patients undergoing transjugular intrahepatic portosystemic shunt from five hospitals were divided into the training and external validation data sets. A modified prediction model of post-transjugular intrahepatic portosystemic shunt mortality (ModelMT ) was built after performing logistic regression. To verify the improved performance of ModelMT , we compared it with seven previous models, both in discrimination and calibration. Furthermore, patients were stratified into low-, medium-, high- and extremely high-risk subgroups.
RESULTS: ModelMT demonstrated a satisfying predictive efficiency in both discrimination and calibration, with an area under the curve of .875 in the training set and .852 in the validation set. Compared to previous models (ALBI, BILI-PLT, MELD-Na, MOTS, FIPS, MELD, CLIF-C AD), ModelMT showed superior performance in discrimination by statistical difference in the Delong test, net reclassification improvement and integrated discrimination improvement (all p < .050). Similar results were observed in calibration. Low-, medium-, high- and extremely high-risk groups were defined by scores of ≤160, 160-180, 180-200 and >200, respectively. To facilitate future clinical application, we also built an applet for ModelMT .
CONCLUSIONS: We successfully developed a predictive model with improved performance to assist in decision making for transjugular intrahepatic portosystemic shunt according to survival benefits.
摘要：
目的：经颈静脉肝内门体分流术有争议的生存益处；因此，患者筛查应在术前进行.在这项研究中,我们的目的是建立一个模型来预测经颈静脉肝内门体分流术后死亡率,以帮助临床决策.
方法：将来自五家医院的811例接受经颈静脉肝内门体分流术的患者分为培训和外部验证数据集。经颈静脉肝内门体分流术后死亡率的改良预测模型(ModelMT)进行logistic回归分析。要验证ModelMT的改进性能，我们将它与以前的七个型号进行了比较，在辨别和校准方面。此外，患者被分层为低，medium-,高风险和极高风险亚组。
结果：ModelMT在辨别和校准方面表现出令人满意的预测效率，训练集中的曲线下面积为.875，验证集中的曲线下面积为.852。与以前的型号(ALBI，BILI-PLT,MELD-Na,MOTS,FIPS,MELD,CLIF-CAD)，ModelMT在Delong测试中通过统计差异显示出优异的辨别性能，净重新分类改进和综合歧视改进(所有p<.050)。在校准中观察到类似的结果。低-,medium-,高危和极高危人群的评分分别为≤160,160-180,180-200和>200.为了促进未来的临床应用，我们还为ModelMT构建了一个小程序。
结论：我们成功开发了一种预测模型，该模型具有改进的性能，可根据生存获益来辅助经颈静脉肝内门体分流术的决策。
方法：将来自五家医院的811例接受经颈静脉肝内门体分流术的患者分为培训和外部验证数据集。经颈静脉肝内门体分流术后死亡率的改良预测模型(ModelMT)进行logistic回归分析。要验证ModelMT的改进性能，我们将它与以前的七个型号进行了比较，在辨别和校准方面。此外，患者被分层为低，medium-,高风险和极高风险亚组。
结果：ModelMT在辨别和校准方面表现出令人满意的预测效率，训练集中的曲线下面积为.875，验证集中的曲线下面积为.852。与以前的型号(ALBI，BILI-PLT,MELD-Na,MOTS,FIPS,MELD,CLIF-CAD)，ModelMT在Delong测试中通过统计差异显示出优异的辨别性能，净重新分类改进和综合歧视改进(所有p<.050)。在校准中观察到类似的结果。低-,medium-,高危和极高危人群的评分分别为≤160,160-180,180-200和>200.为了促进未来的临床应用，我们还为ModelMT构建了一个小程序。
结论：我们成功开发了一种预测模型，该模型具有改进的性能，可根据生存获益来辅助经颈静脉肝内门体分流术的决策。
