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ID06697691

Developing a Clinical Prediction Model to Identify In-Hospital Rebleeding Risk in Adults with Acute Non-Variceal Upper Gastrointestinal Bleeding

Led by Junwei Yan · Updated on 2024-11-27

1000

Participants Needed

1

Research Sites

N/A

Total Duration

AI-Summary

What this Trial Is About

Acute Non-variceal Upper Gastrointestinal Bleeding ANVUGIB is a serious condition causing symptoms such as vomiting blood or passing black stools. Despite improved treatments, 10% to 30% of patients experience rebleeding soon after initial treatment, which raises the risk of death. Researchers are working to develop a new, more accurate prediction model to better identify patients at high risk of rebleeding compared to current tools like the Glasgow-Blatchford Score and Rockall Score. This model aims to help doctors make better decisions about preventive treatments and monitoring to reduce complications and improve outcomes. This observational study collects patient data during routine care to build and test this new prediction model. It will combine patient symptoms, lab test results, and imaging findings to improve risk assessment for rebleeding. Participants include adults diagnosed with ANVUGIB who receive an endoscopy within 24 hours of admission confirming non-variceal bleeding. The study focuses on evaluating rebleeding events within seven days of hospitalization. Participants will be monitored using data from their clinical care, including symptoms, lab tests, and imaging results. Researchers will measure the occurrence of rebleeding up to seven days after admission. No experimental treatments are given, and the study directly uses existing patient information to develop the prediction model. Participation involves routine procedures, and the study aims to improve future care by providing doctors with a better tool for identifying high-risk patients.

CONDITIONS

Brief Title

Clinical Prediction Model for In-Hospital Rebleeding in Acute Non-Variceal Upper Gastrointestinal Bleeding

Research Team

J

junwei yan

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