Development, validation, and web deployment of a rebleeding risk prediction model for acute non-variceal upper gastrointestinal bleeding in a Chinese population.

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 ( Results: Five independent predictors were identified: syncope, pulse rate, red cell distribution width, serum albumin, and bowel sounds. 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.