Development of a deep learning model for guiding treatment decisions of acute variceal bleeding in patients with cirrhosis.
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Xiang Yi, Yang Na, Zheng Tian-Lei, Huang Yi-Fei, Liu Tian-Yu, Ma De-Qiang, Hu Sheng-Juan, Zhang Wen-Hui, Xiang Hui-Ling, Zhang Li-Yao, Yuan Li-Li, Wang Xing, Dang Tong, Zhang Guo, Wu Bin, Peng Li-Jun, Gao Min, Xia Dong-Li, Liu Zhen-Bei, Li Jia, Song Ying, Zhou Xi-Qiao, Qi Xing-Si, Zeng Jing, Tan Xiao-Yan, Deng Ming-Ming, Fang Hai-Ming, Qi Sheng-Lin, He Song, He Yong-Feng, Ye Bin, Wu Wei, Shao Jiang-Bo, Wei Wei, Hu Jian-Ping, Yong Xin, He Chao-Hui, Bao Jin-Lun, Zhang Yue-Ning, Ji Rui, Bo Yang, Yan Wei, Li Hong-Jiang, Li Sheng-Li, Geng Shi, Zhao Lei, Liu Bin, Qi Xiao-Long
Аннотация
AIM: To develop an artificial intelligence (AI)-driven model to guide AVB treatment decisions, and identify candidates eligible for p-TIPS.
METHODS: Patients with cirrhosis and AVB, from two multicenter retrospective cohorts in China, who received endoscopic variceal ligation plus pharmacotherapy (n = 1227) or p-TIPS (n = 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 from aggressive interventions such as p-TIPS. In contrast, advancing the treatment strategy for low-risk patients did not notably improve the short-term prognosis.
CONCLUSION: The AI-AVB model can predict treatment outcomes, stratify the failure risk in cirrhotic patients with AVB, aid in clinical decisions, identify p-TIPS beneficiaries, and optimize personalized treatment strategies.