Introduction and Objectives: Computed tomography (CT) is one of the most commonly used diagnostic modalities for urinary stone disease. In this study we developed a CT and clinical parameter-based prediction model for shockwave lithotripsy (SWL) outcome in proximal ureteral stones. Materials and Methods: Data from 223 patients with single proximal ureteral stones treated with SWL between January 2009 and January 2015 were reviewed retrospectively. Clinical parameters including age, sex, body weight, and body mass index (BMI) were analyzed in combination with stone-related CT parameters (stone diameter, height, volume, location, Hounsfield units [HU], stone-to-skin distance [SSD]), and secondary signs (hydronephrosis, perinephric edema, and rim sign). Based on the cutoff values determined by c-statistics, a scoring system for the prediction of SWL outcome was developed. Results: The success rate was 65.9% (147/223), and in a univariate analysis body weight, BMI, SSD (vertical, horizontal), HU, stone diameter, height, volume, and all secondary signs were significantly associated with the success of SWL. However, on multivariate analysis only BMI (odds ratio [OR] = 1.322, confidence interval [CI] 1.156, 1.512, p = 0.00), stone diameter (OR = 1.397, CI 1.259, 1.551, p = 0.00), and perinephric edema (grade 0-1 vs 3-4, OR = 2.831, CI 1.032, 7.764, p = 0.043) were independent predictors of SWL success. The prediction model based on the logistic regression analysis was as follows: SWL success = 1/[1 + exp (-10.165 + 0.279 × [BMI] + 0.334 × [diameter] + 1.040 [perinephric edema])], having an area under the curve of 0.881. In the prediction model based on these parameters, scores of 0, 1, 2, and 3 correlated with SWL success rates of 98.5%, 65.7%, 31.4%, and 0%, respectively. Conclusions: BMI, stone diameter, and perinephric edema were independent predictors of SWL outcome and a prediction model based on these parameters will facilitate decision-making for SWL in proximal ureteral stones.
Bibliographical notePublisher Copyright:
© Mary Ann Liebert, Inc. 2016.
All Science Journal Classification (ASJC) codes