Development and validation of a practical clinical risk prediction model for post-endoscopic retrograde cholangiopancreatography pancreatitis.

ABSTRACT:

Background

Pancreatitis following endoscopic retrograde cholangiopancreatography (ERCP) can lead to significant morbidity and mortality. We aimed to develop an accurate post-ERCP pancreatitis risk prediction model using easily obtainable variables.

Methods

Using prospective multi-center ERCP data, we performed logistic regression using stepwise selection on several patient-, procedure-, and endoscopist-related factors that were determined a priori. The final model was based on a combination of the Bayesian information criterion and Akaike's information criterion performance, balancing the inclusion of clinically relevant variables and model parsimony. All available data were used for model development, with subsequent internal validation performed on bootstrapped data using

SUBMITTER: Meng ZW 

PROVIDER: S-EPMC10962486 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature