Development of a deep learning model for guiding treatment decisions of acute variceal bleeding in patients with cirrhosis

作者：Yi Xiang, Na Yang, Tianlei Zheng, Yifei Huang, Tianyu Liu, De-Qiang Ma, Shengjuan Hu, Wenhui Zhang, Hui-Ling Xiang, Li-Yao Zhang, Lili Yuan, Xing Wang, Tong Dang, Guo Zhang, Bin Wu, Lijun Peng, Min Gao, Dong-Li Xia, Zhen-Bei Liu, Lee Jia, Ying Song, Xiqiao Zhou, Xingsi Qi, Jing Zeng, Xiaoyan Tan, Mingming Deng, Haiming Fang, Sheng-Lin Qi, Song He, Yongfeng He, Bin Ye, Wei Wu, Jiang-Bo Shao, Wei Wei, Jianping Hu, Xin Yong, Chaohui He, Jiadong Bao, Yuening Zhang, Rui Ji, Bo Yang, Wei Yan, Hongjiang Li, Shengli Li, Shi Geng, Lei Zhao, Bin Liu, Xiaolong Qi · 发表于：World Journal of Gastroenterology · 年份：2025 · DOI：10.3748/wjg.v31.i41.111361 · 被引用次数：2 · 研究领域：Liver Disease and Transplantation、Gastrointestinal Bleeding Diagnosis and Treatment、Liver Disease Diagnosis and Treatment

BACKGROUND: Acute variceal bleeding (AVB) in patients with cirrhosis remains life-threatening; moreover, the current risk stratification methods have certain limitations. Rebleeding and mortality after AVB remain major challenges. Although preemptive transjugular intrahepatic portosystemic shunt (p-TIPS) can improve outcomes, not all patients benefit equally. Accurate risk stratification is needed to guide treatment decisions and identify those most likely to benefit from p-TIPS. AIM: To develop an artificial intelligence (AI)-driven model to guide AVB treatment decisions, and identify candidates eligible for p-TIPS. METHODS: = 1863) were included. Baseline data within 24 hours of hospital admission were obtained. The AI-AVB model, based on the six-week failure and one-year mortality rates, was developed to predict treatment efficacy and compared with standard risk scores. Outcomes and adverse events of the treatments were compared across the high- and low-risk subgroups stratified using the AI-AVB model. RESULTS: The AI-AVB model demonstrated superior predictive performance compared to traditional risk stratification methods. In the internal validation cohort, the model achieved an area under the curve (AUC) of 0.842 for predicting six-week treatment failure and 0.954 for one-year mortality. In the external validation cohort, the AUCs were 0.814 and 0.889, respectively. The model effectively identified patients at high risk of first-line treatment failure who may benefit fro...