AIM:To analyze retrospectively the records of 294 conse-cutive patients operated upon for gallbladder stones, to determine the predictive factors of synchronous common bile duct (CBD) stones and validate prospectively the generated model. METHODS: The prognostic estimation of a biochemical test and ultrasonography alone to differentiate between the absence and presence of choledocholithiasis was assessed using receiver operating characteristics curve analysis. Multivariate analysis was employed using discriminant analysis for establishment of a best model.Prospective validation of the model was made. RESULTS: Discriminant forward stepwise analysis disclosed that high values (≥2×normal) of SGOT,ALP, conjugated bilirubin and CBD diameter on ultrasound ≥10 mm were all prognostic factors of CBD lithiasis in univariate and multivariate analysis, P<0.01. History was not included in the model.Prospective validation of the model was performed by multivariate analysis using Visual General Stepwise Regression. Positive predictive value, when considering all these predictors,was 93.3%,while the negative predictive value was 88.8%.Sensitivity of the model was 96.5% and specificity 80%. CONCLUSION: The above model can be objectively applied to predict the presence of CBD stones. METHODS: The prognostic retrospectively of the records of 294 conse-cutive patients operated upon for gallbladder stones, to determine the predictive factors of synchronous common bile duct (CBD) stones and validate prospectively generated models. METHODS: The prognostic estimation of a biochemical test and ultrasonography alone to differentiate between the absence and presence of choledocholithiasis was assessed using receiver operating characteristics curve analysis. high values ​​(≧ 2 × normal) of SGOT, ALP, conjugated bilirubin and CBD diameter on ultrasound ≥ 10 mm were all prognostic factors of CBD lithiasis in univariate and multivariate analysis, P <0.01. History was not included in the model. Prospective validation of the model was performed by multivariate analysis using Visual General Positive predictive value, when considering all these predictors, was 93.3%, while the negative predictive value was 88.8%. Sensitivity of the model was 96.5% and specificity 80%. CONCLUSION: The above model can be objectively applied to predict the presence of CBD stones.