Login to Access Video or Poster Abstract: MP62-19
Sources of Funding: NextMed, Inc.

Introduction

Shockwave lithotripsy (SWL) is a primary treatment for nephrolithiasis that has been used widely for the past 3 decades. Recently, this technology has come under fire because of declining outcomes in the face of improving alternative technologies. Multiple authors have described pre-operative parameters that improve the success of SWL, including stone size, location, density, and skin-to-stone distance. Using these and other parameters, we present a predictive analytic model to help urologists select the most effective treatment modality with the highest likelihood of success and lowest likelihood of complication.

Methods

We performed a random 70/30 split of 7,000 SWL treatment records for renal and ureteral stones from 2010-2016 to train and validate a generalized linear mixed model (GLMM) using statistical software (PROC GLIMMIX in SAS 9.4). This model uses 9 parameters: stone size, Hounsfield Units (HU), body mass index (BMI), stone location, anesthesia type, SWL machine type, anticoagulant use, age, and gender to predict treatment success, defined as stone free or fragments ? 4mm, and to predict treatment complications. Actual treatment success and complications were obtained from self-reported physician follow-up surveys tied to the original SWL treatment record. Both treatment and follow-up data are housed in The Stone Disease Registry.

Results

The training model was significantly related to treatment success, Likelihood Ratio (LR) Chi-square = 1136.02 , p < .0001, Area under the curve (AUC) = .82. This model was in turn a good predictor of success in the validation dataset, AUC = .81. The training model was also significantly related to complications, Likelihood Ratio Chi-square = 538.75, p < .0001, AUC = .91. This model was a fair predictor of complication rate in the validation dataset, AUC = .77.

Conclusions

This novel predictive analytic model provides accurate prediction of treatment success and complications for SWL. Given the robust model fit to the validation data, we conclude that this model will be useful in prospectively predicting success for the treatment of urinary stones with SWL. This has the potential to assist urologists in prospectively making evidence-based decisions on which treatment modality will be most effective in maximizing success and minimizing complications and costs for treatment of urinary stones.

Funding

NextMed, Inc.