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Laparoscopic Cholecystectomy with Indocyanine Green Fluorescence: Choledochoscopic Stone Extraction and Primary Duct Suture
Published on: November 25, 2025
Predicting common bile duct stones by non-invasive parameters
Anas Kadah1, Tawfik Khoury2, Mahmud Mahamid3
1Department of Gastroenterology, Galilee Medical Center, Nahariya, Israel, Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, Israel.
A new score combining common bile duct width, age, and GGT levels effectively predicts common bile duct stones. This aids in diagnosing choledocholithiasis and reduces reliance on other lab tests.
Area of Science:
- Gastroenterology
- Medical Diagnostics
- Hepatobiliary Medicine
Background:
- Common bile duct (CBD) stones affect approximately 10% of patients with symptomatic gallstones.
- Clinical guidelines exist for managing suspected choledocholithiasis, but accurate prediction remains crucial.
Purpose of the Study:
- To evaluate the predictive performance of various clinical parameters for common bile duct stones.
- To develop a diagnostic score for improved prediction of CBD stones.
Main Methods:
- A cohort of 344 patients with suspected CBD stones underwent endoscopic ultrasound (EUS).
- Clinical parameters were collected and analyzed for their predictive power using univariate and multivariate regression.
- A diagnostic equation was formulated based on significant predictors.
Main Results:
- Multivariate analysis identified CBD width by ultrasound, age, and gamma-glutamyl transferase (GGT) level as significant predictors of CBD stones.
- The developed diagnostic score demonstrated a receiver operating characteristic (ROC) of 0.7287 for predicting CBD stones.
- The score showed strong predictive capability, potentially diminishing the importance of total bilirubin and transaminases.
Conclusions:
- The proposed score is a robust predictor for common bile duct stones.
- This diagnostic tool can aid clinicians in managing patients with suspected choledocholithiasis.
- The findings suggest a refined approach to predicting CBD stones, potentially simplifying diagnostic pathways.
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