Current clinical algorithms for predicting common bile duct stones have only moderate accuracy. Issue 4 (11th January 2018)

• Record Type: Journal Article
• Authors: Ebrahim, Mohamed; Sorensen, Lars T.; Jorgensen, Lars N.; Kalaitzakis, Evangelos
• Is Part Of: Digestive endoscopy. Volume 30:Issue 4(2018)
• Page Start: 477
• Page End: 484
• Publication Date: 2018-01-11
• Subjects: clinical algorithm -- endoscopic retrograde cholangiopancreatography -- endoscopic ultrasound -- gallstone -- magnetic resonance cholangiopancreatography; Digestive organs -- Diseases -- Periodicals; Digestive organs -- Diseases -- Diagnosis -- Periodicals; Endoscopy -- Periodicals; Digestive System Diseases -- diagnosis -- Periodicals; Digestive System Diseases -- therapy -- Periodicals; Endoscopy -- Periodicals; 616.3
• DOI: 10.1111/den.12994
• Languages: English
• ISSNs: 0915-5635

Abstract

Abstract : Background and Aim: A risk‐stratified approach for selecting patients likely to harbor common bile duct (CBD) stones to proceed directly to endoscopic or surgical stone clearance, rather than undergo less invasive testing, has been proposed. We assessed the performance of three clinical algorithms used to predict CBD stones. Methods: All patients undergoing first‐time endoscopic retrograde cholangiopancreatography (ERCP) in 2011–2012 as a result of suspected CBD stones were enrolled prospectively in a clinical database. Data such as imaging test findings and liver function tests (LFTs) were collected 48 h prior to and on the day of ERCP. Patients were stratified into different risk groups for harboring CBD stones according to three clinical algorithms using imaging and laboratory data. Findings on ERCP were used as gold standard. Performance characteristics of each algorithm were separately calculated for each time point of LFT assessment. Results: Overall, 186 patients were analyzed, 75% of whom presented CBD stones on ERCP. Proportion of patients categorized as high‐risk for harboring CBD stones varied among the three algorithms (67% vs 73% vs 56%). Also, the algorithms showed only moderate, albeit comparable, accuracy for predicting the presence of CBD stones (0.65, 95% confidence interval [CI] 0.62–0.68 vs 0.68, 95% CI 0.63–0.67 vs 0.59, 95% CI 0.57–0.61). Similar results were obtained when performance characteristics were recalculated using LFT from 48 h prior to ERCP (data not shown). Conclusion: Three diagnostic algorithms commonly used for predicting CBD stones have comparable but only moderate accuracy. Further research is warranted to improve risk stratification of patients with suspected CBD stones.