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Effect of Predictive Model on Total Double-Balloon Enteroscopy Rate: a Randomized Controlled Trial
The aim of this study is to assess the effect of predictive model on total enteroscopy rate in double-balloon enteroscopy.
Double-balloon enteroscopy is a new and effective endoscopic technique used for the diagnosis and treatment of small intestinal diseases. Due to its deep intubation ability, it can achieve complete visualization of the small intestinal mucosa, that is, total enteroscopy. Theoretically, a higher total enteroscopy rate could be associated with a greater possibility of detecting positive lesions. However, a total enteroscopy is a challenging procedure for endoscopists. Endoscopists cannot accurately predict the operational difficulty of patients before the operation, which leads to the passivity of preoperative preparation and intraoperative strategy formulation. Recently, the Department of Gastroenterology of Qilu Hospital of Shandong University has completed the development and external validation of the first double-balloon enteroscopy prediction model. A preoperative individualized assessment of the difficulty of total enteroscopy can be achieved using this model. Therefore, a multicenter randomized controlled study was designed to assess the performance of predictive model in double-balloon enteroscopy, including total enteroscopy rate, positive findings, procedural time, adverse events and other indicators.
Age
18 - 80 years
Sex
ALL
Healthy Volunteers
No
Dezhou People's Hospital
Dezhou, Shandong, China
Qilu Hospital of Shandong University (Qingdao)
Qingdao, Shandong, China
Yantai Affiliated Hospital of Binzhou Medical University
Yantai, Shandong, China
Zaozhuang Municipal Hospital
Zaozhuang, Shandong, China
Qilu Hospital of Shandong University
Jinan, Shangdong, China
Start Date
January 12, 2026
Primary Completion Date
May 31, 2027
Completion Date
August 31, 2027
Last Updated
January 13, 2026
338
ESTIMATED participants
Predictive model
OTHER
Lead Sponsor
Shandong University
Collaborators
Data Source & Attribution
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