作者
Yong Lv,Wei Bai,Xuan Zhu,Hui Xue,Jianbo Zhao,Yuzheng Zhuge,Junhui Sun,Chun‐Qing Zhang,Peng‐Xu Ding,Zaibo Jiang,Xiaoli Zhu,Weixin Ren,Yingchun Li,Kewei Zhang,Wenguang Zhang,Kai Li,Zhenyu Wang,Bohan Luo,Xiaomei Li,Zhiping Yang,Wengang Guo,Dongdong Xia,Huahong Xie,Yanglin Pan,Zhanxin Yin,Daiming Fan,Guohong Han
摘要
Background and Aim: Baveno VII workshop recommends the use of preemptive TIPS (p-TIPS) in patients with cirrhosis and acute variceal bleeding (AVB) at high- risk of treatment failure. However, the criteria defining “high-risk” have low clinical accessibility or include subjective variables. We aimed to develop and externally validate a model for better identification of p-TIPS candidates. Approach and Results: The derivation cohort included 1554 patients with cirrhosis and AVB who were treated with endoscopy plus drug (n = 1264) or p-TIPS (n = 290) from 12 hospitals in China between 2010 and 2017. We first used competing risk regression to develop a score for predicting 6-week and 1-year mortality in patients treated with endoscopy plus drugs, which included age, albumin, bilirubin, international normalized ratio, white blood cell, creatinine, and sodium. The score was internally validated with the bootstrap method, which showed good discrimination (6 wk/1 y concordance-index: 0.766/0.740) and calibration, and outperformed other currently available models. In the second stage, the developed score was combined with treatment and their interaction term to predicate the treatment effect of p-TIPS (mortality risk difference between treatment groups) in the whole derivation cohort. The estimated treatment effect of p-TIPS varied substantially among patients. The prediction model had good discriminative ability (6 wk/1 y c -for-benefit: 0.696/0.665) and was well calibrated. These results were confirmed in the validation dataset of 445 patients with cirrhosis with AVB from 6 hospitals in China between 2017 and 2019 (6-wk/1-y c-for-benefit: 0.675/0.672). Conclusions: We developed and validated a clinical prediction model that can help to identify individuals who will benefit from p-TIPS, which may guide clinical decision-making.