Development of a deep learning model for guiding treatment decisions of acute variceal bleeding in patients with cirrhosis
Year: 2025
Type: article
Study design: Observational Study
Abstract: 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 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.
Authors: Yi Xiang, Na Yang, Tian-Lei Zheng, Yi-Fei Huang, Tian-Yu Liu, De-Qiang Ma, Sheng-Juan Hu, Wen-Hui Zhang, Hui-Ling Xiang, Li-Yao Zhang, Li-Li Yuan, Xing Wang, Tong Dang, Guo Zhang, Bin Wu, Li-Jun Peng, Min Gao, Dong-Li Xia, Zhen-Bei Liu, Jia Li, Ying Song, Xi-Qiao Zhou, Xing-Si Qi, Jing Zeng, Xiao-Yan Tan, Ming-Ming Deng, Hai-Ming Fang, Sheng-Lin Qi, Song He, Yong-Feng He, Bin Ye, Wei Wu, Jiang-Bo Shao, Wei Wei, Jian-Ping Hu, Xin Yong, Chao-Hui He, Jin-Lun Bao, Yue-Ning Zhang, Rui Ji, Yang Bo, Wei Yan, Hong-Jiang Li, Sheng-Li Li, Shi Geng, Lei Zhao, Bin Liu, Xiao-Long Qi
Institutions: Zhongda Hospital Southeast University, Gannan Medical University, First Affiliated Hospital of Gannan Medical University, State Key Laboratory of Digital Medical Engineering, Southeast University, Xuzhou Medical College, Affiliated Hospital of Xuzhou Medical College, Nanjing Medical University, China University of Mining and Technology, Third Affiliated Hospital of Sun Yat-sen University, Hubei University of Medicine, Taihe Hospital, Ningxia Hui Autonomous Region Peoples Hospital, Ningxia Medical University, Chinese PLA General Hospital, Tianjin Third Central Hospital, The Sixth People's Hospital of Shenyang, Inner Mongolia University of Science and Technology, Baotou Medical College, The People's Hospital of Guangxi Zhuang Autonomous Region, Linyi People's Hospital, Sir Run Run Shaw Hospital, Zhejiang University, Chongqing University, Fuling Center Hospital of Chongqing, Jiangsu Province Hospital, Qingdao University, Affiliated Hospital of Qingdao University, Guangzhou Medical University, Southwest Medical University, Affiliated Hospital of Southwest Medical University, Anhui Medical University, Second Affiliated Hospital of Anhui Medical University, Second Affiliated Hospital of Chongqing Medical University, Ankang City Central Hospital, Wenzhou Medical University, Lishui Central Hospital, First Affiliated Hospital of Wenzhou Medical University, Jinhua Central Hospital, Yinchuan First People's Hospital, Zunyi Medical University, Capital Medical University, Beijing YouAn Hospital, First Hospital of Lanzhou University, Tongji Hospital, Huazhong University of Science and Technology, Baoding People's Hospital
Language: English
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4415942846