Clinical Nomograms to Predict Stone-Free Rates after Shock-Wave Lithotripsy: Development and Internal-Validation |
| |
Authors: | Jung Kwon Kim Seung Beom Ha Chan Hoo Jeon Jong Jin Oh Sung Yong Cho Seung-June Oh Hyeon Hoe Kim Chang Wook Jeong |
| |
Institution: | 1. Department of Urology, Seoul National University Hospital, Seoul, Korea;2. Department of Urology, Seoul National University Bundang Hospital, Seongnam, Korea;3. Department of Urology, Seoul National University Boramae Medical Center, Seoul, Korea;Sun Yat-sen University, CHINA |
| |
Abstract: | PurposeShock-wave lithotripsy (SWL) is accepted as the first line treatment modality for uncomplicated upper urinary tract stones; however, validated prediction models with regards to stone-free rates (SFRs) are still needed. We aimed to develop nomograms predicting SFRs after the first and within the third session of SWL. Computed tomography (CT) information was also modeled for constructing nomograms.Materials and MethodsFrom March 2006 to December 2013, 3028 patients were treated with SWL for ureter and renal stones at our three tertiary institutions. Four cohorts were constructed: Total-development, Total-validation, CT-development, and CT-validation cohorts. The nomograms were developed using multivariate logistic regression models with selected significant variables in a univariate logistic regression model. A C-index was used to assess the discrimination accuracy of nomograms and calibration plots were used to analyze the consistency of prediction.ResultsThe SFR, after the first and within the third session, was 48.3% and 68.8%, respectively. Significant variables were sex, stone location, stone number, and maximal stone diameter in the Total-development cohort, and mean Hounsfield unit (HU) and grade of hydronephrosis (HN) were additional parameters in the CT-development cohort. The C-indices were 0.712 and 0.723 for after the first and within the third session of SWL in the Total-development cohort, and 0.755 and 0.756, in the CT-development cohort, respectively. The calibration plots showed good correspondences.ConclusionsWe constructed and validated nomograms to predict SFR after SWL. To the best of our knowledge, these are the first graphical nomograms to be modeled with CT information. These may be useful for patient counseling and treatment decision-making. |
| |
Keywords: | |
|
|