Creation of a Pediatric Choledocholithiasis Prediction Model

ABSTRACT Background: Definitive non‐invasive detection of pediatric choledocholithiasis could allow more efficient identification of those patients who are most likely to benefit from therapeutic endoscopic retrograde cholangiopancreatography (ERCP) for stone extraction. Objective: To craft a pediat...

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Published inJournal of pediatric gastroenterology and nutrition Vol. 73; no. 5; pp. 636 - 641
Main Authors Cohen, Reuven Zev, Tian, Hongzhen, Sauer, Cary G., Willingham, Field F., Santore, Matthew T., Mei, Yajun, Freeman, A. Jay
Format Journal Article
LanguageEnglish
Published 01.11.2021
Subjects
cholangiogram
choledocholithiasis
cholelithiasis
endoscopic retrograde cholangiopancreatography
endoscopy
machine learning
pediatric
prediction model
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Summary:ABSTRACT Background: Definitive non‐invasive detection of pediatric choledocholithiasis could allow more efficient identification of those patients who are most likely to benefit from therapeutic endoscopic retrograde cholangiopancreatography (ERCP) for stone extraction. Objective: To craft a pediatric choledocholithiasis prediction model using a combination of commonly available serum laboratory values and ultrasound results. Methods: A retrospective review of laboratory and imaging results from 316 pediatric patients who underwent intraoperative cholangiogram or ERCP due to suspicion of choledocholithiasis were collected and compared to presence of common bile duct stones on cholangiography. Multivariate logistic regression with supervised machine learning was used to create a predictive scoring model. Monte‐Carlo cross‐validation was used to validate the scoring model and a score threshold that would provide at least 90% specificity for choledocholithiasis was determined in an effort to minimize non‐therapeutic ERCP. Results: Alanine aminotransferase (ALT), total bilirubin, alkaline phosphatase, and common bile duct diameter via ultrasound were found to be the key clinical variables to determine the likelihood of choledocholithiasis. The dictated specificity threshold of 90.3% yielded a sensitivity of 40.8% and overall accuracy of 71.5% in detecting choledocholithiasis. Positive predictive value was 71.4% and negative predictive value was 72.1%. Conclusion: Our novel pediatric choledocholithiasis predictive model is a highly specific tool to suggest ERCP in the setting of likely choledocholithiasis.
Bibliography:Supplemental digital content is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal's Web site
www.jpgn.org
Author Contributions: All seven authors made substantial contributions to the drafting of this study and interpreting the collected data. Reuven Zev Cohen is responsible for study conception, data acquisition and analysis. All seven authors drafted the manuscript and revised it with significant contextual contribution. All seven authors have given final approval of this manuscript version and have agreed to be accountable for all aspects of the work.
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The authors report no conflicts of interest.
Source of Funding: No honorarium, grant, or other form of payment was given to anyone to produce the manuscript.
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ISSN:0277-2116
1536-4801
DOI:10.1097/MPG.0000000000003219