Practical recommendations for the prediction and management of common bile duct stones in patients with gallstones

Abstract

For patients with gallstones, suggestive ultrasonographic findings in those younger than 71 years and elevated direct or total bilirubin level in those older than 70 years are the most valuable and practical predictors of CBDS, and thus are the proper indications for preoperative endoscopic retrograde cholangiography.

Biochemical predictors for absence of common bile duct stones in patients undergoing laparoscopic cholecystectomy

“…It is likely that such complications will result in longer hospitalization and may increase the patients' burden with respect to costs, morbidity, and mortality [4,11]. It would therefore be practical to identify unequivocally those patients at risk of CBD stones and subject only those cases to ERCP [4,16].…”

“…Overlooked CBD stones remaining after LC might result in cholangitis or bile leakage, and a need to return to the operating room for open CBD exploration if there is a failed postoperative ERCP [8]. Numerous studies can be found in the literature on the prediction of CBD stones [1][2][3][4][5][6][7][8][11][12][13][14][15][16][17][18]. Results, however, are extremely variable and conflicting.…”

A Prospective Study on Different Biliary Diversions in Common Bile Duct Stones

“…Laparoscopic exploration of the common bile duct may be considered in patients with larger stones, but it is practised by few groups. [10][11][12] In the era of open cholecystectomy, open exploration of the common bile duct was found to be superior to ERCP in achieving stone clearance (odds ratio [OR] 2.89, 95% CI 1.81-4.61). [13] Open exploration…”

Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery

“…Consequently, non-invasive methods for diagnosis of CBDS such as prognostic model have been shown to be a desirable goal [7,8]. Previous studies have suggested various predictive models relying on logistic regression [9,10], regression trees [11], multivariate regression [3,[12][13][14], discriminant functions [15] and artificial neural networks (ANNs) for predicting CBDS [16].…”