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Radiographic parameters on noncontrast computerized tomography predictive of shock wave lithotripsy success
Ravi Kacker1, Lee Zhao, Amanda Macejko
1Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA.
Purpose:
Accurate prediction of shock wave lithotripsy success for given patient and radiographic parameters will lead to improved selection of patients for shock wave lithotripsy vs more invasive treatment. In this study we determined which radiographic parameters are the most predictive of shock wave lithotripsy success, and present a method to incorporate these into current and future models based on nonradiographic parameters.
Materials And Methods:
A retrospective case-control study was performed to determine average, maximum and standard deviation of stone attenuation values, stone size and skin-to-stone distance on preoperative noncontrast computerized tomography for 220 patients successfully treated with shock wave lithotripsy and 105 patients in whom shock wave lithotripsy failed.
Results:
Average stone attenuation is the best independent predictor of shock wave lithotripsy success as determined by the Student t test (p <0.0001) and receiver operating characteristic curves. Odds and likelihood ratios are provided for shock wave lithotripsy success for incremental average HU cutoffs. An average HU cutoff can be established over which the refined probability of success is below an arbitrary minimally acceptable cutoff of a 60% stone-free rate. Using pre-test probabilities of shock wave lithotripsy success from nomograms in the literature, our data suggest that shock wave lithotripsy should be first line therapy for solitary 6 to 10 mm stones with an average stone attenuation of less than 1,000 and 640 HU for the proximal ureter and renal pelvis, respectively.
Conclusions:
Average stone attenuation is a convenient radiographic measure that can be used to refine a known probability of shock wave lithotripsy success. Clinical HU cutoff guidelines can be determined based on current or future predictive nomograms based on other parameters.
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