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Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
A Risk Prediction Model for Post-endoscopic Retrograde Cholangiopancreatography Pancreatitis After Stent Insertion
Zhifeng Fu1, Jitao Song1, Yilin Pi1
1Department of Gastroenterology, Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Nangang District, Harbin, 150086, Heilongjiang Province, China.
Objectives:
Pancreatitis is the most common complication of post-endoscopic retrograde cholangiopancreatography (ERCP). There are currently no prediction models, particularly for post-ERCP pancreatitis (PEP) after biliary stent placement due to malignant biliary obstruction (MBO). To that end, we aim to develop and validate a predictive model for PEP.
Methods:
We retrospectively analyzed the data of patients who underwent ERCP for biliary stent placement due to MBO at the Second Affiliated Hospital of Harbin Medical University from January 1, 2014 to August 31, 2021. The eligible patients were randomly allocated to the development and validation cohorts. A prediction model was built using the development cohort, and the model's effect was validated using a validation cohort.
Results:
A total of 1524 patients were enrolled, including 1016 in the development cohort and 508 in the validation cohort, with an overall PEP rate of 7.1%. The model's predictors included acute pancreatitis history, the absence of pancreatic duct dilation, nonpancreatic cancer, difficult cannulation, and pancreatic injection. The area under the curve (AUC) in the development cohort was 0.810, and the incidence of PEP in the low-risk, medium-risk, and high-risk groups was 1.53%, 9.12%, and 36.36%, respectively. Meanwhile, the AUC of the validation cohort was 0.781, and the incidence of PEP in the low-risk, medium-risk, and high-risk groups was 4.17%, 8.75%, and 41.67%, respectively.
Conclusions:
This study was the first to build and validate a risk prediction model, especially for PEP after biliary stent placement due to MBO. Moreover, this model might assist clinicians in identifying high-risk patients and help implement preventive measures in a more timely manner.
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