ORIGINAL RESEARCH article
Front. Med.
Sec. Gastroenterology
This article is part of the Research TopicAdvancing Gastrointestinal Disease Diagnosis with Interpretable AI and Edge Computing for Enhanced Patient CareView all 10 articles
Development, Validation, and Web Deployment of a Rebleeding Risk Prediction Model for Acute Non-variceal Upper Gastrointestinal Bleeding in a Chinese Population
Provisionally accepted
- 1School of Medicine, Wuhan University of Science and Technology, Wuhan, China
- 2General Hospital of Central Theater Command Department of Gastroenterology, Wuhan, China
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Background: Acute non-variceal upper gastrointestinal bleeding (ANVUGIB) is a common life-threatening emergency. Despite advances in endoscopic hemostasis, the 7-day rebleeding rate remains as high as 15%–30%. Existing risk assessment tools show limited performance in Chinese populations, underscoring the need for a high-precision model tailored to local patients. Objective: To develop, validate, and deploy an individualized model for predicting 7-day rebleeding risk in Chinese patients with ANVUGIB using early clinical information. Methods: We retrospectively included 818 patients with ANVUGIB treated at the General Hospital of Central Theater Command between January 2020 and December 2023, randomly divided into a training cohort (n = 572) and an internal validation cohort (n = 246) at a 7:3 ratio. An additional 147 patients admitted between January and August 2024 were used as a temporally independent external validation cohort. Predictor variables were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable logistic regression modeling. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), and generalizability was tested in the external validation cohort. Results: Five independent predictors were identified: syncope, pulse rate, red cell distribution width, serum albumin, and bowel sounds. This is a provisional file, not the final typeset article The prediction model incorporating these variables achieved areas under the curve (AUCs) of 0.843 (95% CI 0.784–0.903), 0.833 (95% CI 0.742–0.924), and 0.825 (95% CI 0.700–0.950) in the training, internal validation, and external validation cohorts, respectively. Calibration plots and decision curve analysis confirmed good consistency and clinical utility. Conclusion: We developed and validated a 7-day rebleeding risk prediction model for ANVUGIB in a Chinese emergency department population. The model outperformed existing scoring systems, and deployment as a Shiny-based web tool enables early risk identification and individualized decision-making in emergency care.
Keywords: Acute non-variceal upper gastrointestinal bleeding, Clinical decision support, Prognostic model, Rebleeding, Risk prediction model
Received: 30 Sep 2025; Accepted: 01 Dec 2025.
Copyright: © 2025 Chen, Xu, Li, Peng, Zhang, Xu and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Weitian Xu
Qingming Wu
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