Shock Wave Lithotripsy Success Research Articles

Predictors of Success of Shock Wave Lithotripsy for Ureteral Stones Based on Computed Tomography Scan parameters

Background: For shock wave lithotripsy has proven to be an effective, safe and truly minimally invasive option for the treatment of nephrolithiasis. Various technical factors as well as patient selection can impact the success of the procedure. Objective: The aim of this study was to identify the parameters on NCCT that may predict the success of shock wave lithotripsy (SWL) in ureteral stones. Patients and Methods: 102 patients who underwent SWL for ureteral stones at sayed Galal University Hospital from January 2015 to August 2018 diagnosed by non-contrasted computed tomography were studied. The failure was defined as remnant stones ＞4 mm. We assessed age, sex, body mass index, stone size, location, skin-to-stone distance (SSD), presence of JJ and the presence of secondary signs (hydronephrosis, renal enlargement, perinephric fat stranding, and tissue rim sign). Results: 102 patients with success rate 61.8%, stone size, stone density were significantly associated with outcome of SWL. While SSD, JJ and secondary signs (hydronephrosis, perinephric fat stranding and tissue rim sign) were insignificant. On multivariate analysis, stone size and stone density were the independent factors affecting the outcome of SWL. Conclusions: The study demonstrated that stone size and density are significant and independent predictors of outcome in patients with upper ureteral stones. However SSD and signs of impaction still have to be evaluated.

Predictors of Success of Shock Wave Lithotripsy for Ureteral Stones Based on Computed Tomography Scan parameters

Background: For shock wave lithotripsy has proven to be an effective, safe and truly minimally invasive option for the treatment of nephrolithiasis. Various technical factors as well as patient selection can impact the success of the procedure. Objective: The aim of this study was to identify the parameters on NCCT that may predict the success of shock wave lithotripsy (SWL) in ureteral stones. Patients and Methods: 102 patients who underwent SWL for ureteral stones at sayed Galal University Hospital from January 2015 to August 2018 diagnosed by non-contrasted computed tomography were studied. The failure was defined as remnant stones ＞4 mm. We assessed age, sex, body mass index, stone size, location, skin-to-stone distance (SSD), presence of JJ and the presence of secondary signs (hydronephrosis, renal enlargement, perinephric fat stranding, and tissue rim sign). Results: 102 patients with success rate 61.8%, stone size, stone density were significantly associated with outcome of SWL. While SSD, JJ and secondary signs (hydronephrosis, perinephric fat stranding and tissue rim sign) were insignificant. On multivariate analysis, stone size and stone density were the independent factors affecting the outcome of SWL. Conclusions: The study demonstrated that stone size and density are significant and independent predictors of outcome in patients with upper ureteral stones. However SSD and signs of impaction still have to be evaluated.

Impact of body mass index on success, complications and failure of extracorporeal shock wave lithotripsy

Introduction: Extracorporeal shock wave lithotripsy (ESWL) is known as the most common method in treating urinary stones (70%) and is a selective method to treat small urinary stones (<2-2.5 cm). General opinion is that the success rate of ESWL in obese patients is low and risk of complications is high. Objectives: The aim of this study was to evaluate the impact of body mass index (BMI) and the stone-to-skin distance on success, complications and failure of ESWL. Patients and Methods: In this study, 222 patients who referred to lithotripsy clinic of Kashani hospital of Shahrekord to break stones with diameters of 5 to 20 mm participated in the study. Their urinary stones were in kidneys, lower calyces, upper calyces, pelvis or proximal ureter and were divided into two equal groups of BMI >25 kg/m2 and BMI <25 kg/m2 . Assessing the position and diameter of the stones was based on radiographic findings such as sonography and CT scan. Regarding success, failure and complications of lithotripsy, two groups were compared. Results: Around 105 and 117 patients out of 222 subjects, were respectively assigned in BMI>25 kg/ m2 and BMI <25 kg/m2 groups. The failure and success of both groups were statistically significant (P=0.023). In terms of treatment outcomes, difference between two groups was high in low skinto-stone distance (SSD). Logistic regression showed that both BMI and SSD are effective factors in treating the stone while SSD was more effective (P=0.004) than BMI (P=0.023) since SSD>7.5 cm predicts treatment failure. Conclusion: BMI and SSD are effective factors on the outcome of ESWL. Higher BMI and SSD> 7.5 cm are effective factors on treatment failure.

Impact of Case Volume on Shock Wave Lithotripsy Outcomes: Data from the National Shock Wave Lithotripsy Database of New Zealand.

Purpose: To assess the impact of individual operator case volume on shock wave lithotripsy (SWL) treatment outcomes in more than 9000 stone cases over a 20-year period in New Zealand. Materials and Methods: Stone cases treated with SWL on the Mobile Medical Technology (MMT) vehicle between June 19, 1995, and December 1, 2014, were identified. Data collection was undertaken prospectively for patient, stone, and treatment characteristics, and retrospectively for treatment outcomes. Multivariate analysis using binary logistic regression was undertaken to assess whether radiographer stone case volume (stones/year) was an independent predictor of SWL success (stone free or clinically insignificant residual fragments ≤4 mm at follow-up). Results: Sixteen radiographers delivered treatment to the included cohort (9039 stone cases), with a median case volume (stones/year) of 73 (range: 37-197) and median total of 425 stones treated (range: 71-1721). The two radiographers with highest case volumes achieved the highest success rates. Radiographer case volume (stones/year) was independently associated with SWL success (odds ratio [OR]: 1.004, 95% confidence interval [CI]: 1.003-1.005, p < 0.0001) and reduced need for post-SWL hospital admission (OR: 0.997, 95% CI: 0.994-1.000, p = 0.028), but there was no associated decrease in post-SWL urosepsis (OR: 0.999, 95% CI: 0.974-1.025, p = 0.941), perinephric hematoma (OR: 1.003, 95% CI: 0.985-1.020, p = 0.778), or need for auxiliary procedures (OR: 1.000, 95% CI: 0.998-1.002, p = 0.871). Conclusions: SWL success rates can be improved by increasing the frequency of cases performed by individual operators. In this multicenter cohort of more than 9000 stone cases treated over a 20-year period, the best outcomes were seen for those radiographers performing >150 cases per year.

Effect degree of hydronephrosis on the success rate of shock wave lithotripsy in patients with ureteral calculi

Objective To investigate the effect of the degree of hydronephrosis on the success rate of shock wave lithotripsy (SWL) in patients with ureteral calculi (UC). Methods From January 2013 to May 2017, 420 patients with UC received SWL in our hospital were selected as study subjects. The patients were divided into the successful group (n=310) and the failure group (n=110) according to success of stone removal 3 months after the operation. The differences in clinical characteristics between the two groups were compared, and the differences of clinical features in patients with different hydronephrosis groups were compared. Univariate and multivariate logistic regression analysis were used to investigate the risk factors of success of SWL in UC patients. Results The stone heterogeneity index in successful group was significantly higher than in failure group (P<0.05), the age, stone diameter, stone density and severity of hydronephrosis were significantly lower than those of failure group (P<0.05). The patients with different degree of hydronephrosis had significant differences in sex, stone site, stone diameter, stone density, and one-session success (P<0.05). Univariate and multivariate logistic regression analysis showed that stone diameter≥10.00 mm, stone density≥700.00 HU, stone heterogeneity index≤235.00 HU, degree of hydronephrosis≥3 grade could increase risk of failure of SWL surgery in UC patients (P<0.05). Conclusions The 3 and 4 levels of renal hydronephrosis before operation are not beneficial to the effect of SWL in UC patients. Key words: Ureteral Calculi; Lithotripsy; Hydronephrosis

Comparison of the results of shock wave lithotripsy with ultrasonic and fluoroscopic focus in pediatric age group; Fluoroscopic focusing how much is needed?

Aim: Shock wave lithotripsy (SWL) is a well-known technique used in the treatment of kidney stones since the early eighties. For successful SWL, accurate visualization of the shock waves is performed by ultrasound (US) or fluoroscope (FS) to fully focus the shock waves on the stone. In this study, we aimed to investigate the effect of US and FS methods used for focusing on stone in SWL treatment of renal stones in pediatric patients on side effect and treatment success.Material and Methods: Between January 2008 and January 2018, 495 children under 16 years of age who were treated with SWL using ultrasonic and fluoroscopic focusing were included in the study. Patients with fluoroscopic focus were classified as Group 1 and patients with ultrasonic focus were classified as Group 2. Demographic data, SWL parameters and success rates of the two groups were compared..Results: There was no significant difference between the demographic data, stone localization and stone sizes of the groups. SWL success rate was 90.5% in group 1 and 92.3% in group 2. There was no significant difference between the two groups in terms of success (p = 0.474). Complication rates were 0.2% (1 patient) and 0.4% (2 patients) in groups 1 and 2, respectively, and there was no statistically significant difference (p = 0.495). With increasing clinical experience, ultrasonic focusing rates have increased and fluoroscopic focusing rates have decreased over the years. Conclusion: In the pediatric age group, we recommend the use of ultrasonic focusing with the additional advantage of avoiding radiation, avoiding the fluoroscopic focusing method using ionized radiation for SWL. We believe that randomized prospective studies will be more informative and support our study.

Advanced non-contrasted computed tomography post-processing by CT-Calculometry (CT-CM) outperforms established predictors for the outcome of shock wave lithotripsy.

To evaluate the predictive value of advanced non-contrasted computed tomography (NCCT) post-processing using novel CT-calculometry (CT-CM) parameters compared to established predictors of success of shock wave lithotripsy (SWL) for urinary calculi. NCCT post-processing was retrospectively performed in 312 patients suffering from upper tract urinary calculi who were treated by SWL. Established predictors such as skin to stone distance, body mass index, stone diameter or mean stone attenuation values were assessed. Precise stone size and shape metrics, 3-D greyscale measurements and homogeneity parameters such as skewness and kurtosis, were analysed using CT-CM. Predictive values for SWL outcome were analysed using logistic regression and receiver operating characteristics (ROC) statistics. Overall success rate (stone disintegration and no re-intervention needed) of SWL was 59% (184 patients). CT-CM metrics mainly outperformed established predictors. According to ROC analyses, stone volume and surface area performed better than established stone diameter, mean 3D attenuation value was a stronger predictor than established mean attenuation value, and parameters skewness and kurtosis performed better than recently emerged variation coefficient of stone density. Moreover, prediction of SWL outcome with 80% probability to be correct would be possible in a clearly higher number of patients (up to fivefold) using CT-CM-derived parameters. Advanced NCCT post-processing by CT-CM provides novel parameters that seem to outperform established predictors of SWL response. Implementation of these parameters into clinical routine might reduce SWL failure rates.

Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones

We sought to determine the predictive value of 3-dimensional texture analysis of computerized tomography images for successful shock wave lithotripsy in patients with kidney stones. Patients with preoperative and postoperative computerized tomography, previously untreated kidney stones and a stone diameter of 5 to 20 mm were included in study. A total of 224, 3-dimensional texture analysis features of each kidney stone, including attenuation measured in HU and the clinical variables body mass index, initial stone size and skin to stone distance, were analyzed using 5 commonly used machine learning models. The data set was split in a ratio of 2/3 for model derivation and 1/3 for validation. Machine learning based predictions of shock wave lithotripsy success in the validation cohort were evaluated by calculating sensitivity, specificity and the AUC. For shock wave lithotripsy success the 3 clinical variables body mass index, initial stone size and skin to stone distance showed an AUC of 0.68, 0.58 and 0.63, respectively. No predictive value was found for HU. A random forest classifier using 3, 3-dimensional texture analysis features had an AUC of 0.79. By combining these 3 features with clinical variables discriminatory accuracy improved further with an AUC of 0.85 for 3-dimensional texture analysis features and skin to stone distance, an AUC of 0.8 for 3-dimensional texture analysis features and body mass index, and an AUC of 0.81 for 3-dimensional texture analysis and stone size. This preliminary study indicates that the clinical variables body mass index, initial stone size and skin to stone distance show limited value to predict shock wave lithotripsy success while stone HU values were not predictive. Select 3-dimensional texture analysis features identified by machine learning provided incremental accuracy to predict the success of shock wave lithotripsy.

PD08-03 3D TEXTURE ANALYSIS IN ABDOMINAL CT AIDED BY MACHINE LEARNING CLASSIFIERS PREDICTS SHOCK WAVE LITHOTRIPSY SUCCESS

You have accessJournal of UrologyStone Disease: Shock Wave Lithotripsy1 Apr 2018PD08-03 3D TEXTURE ANALYSIS IN ABDOMINAL CT AIDED BY MACHINE LEARNING CLASSIFIERS PREDICTS SHOCK WAVE LITHOTRIPSY SUCCESS Manoj Mannil, Jochen von Spiczak, Thomas Hermanns, Hatem Alkhadi, and Christian Fankhauser Manoj MannilManoj Mannil More articles by this author , Jochen von SpiczakJochen von Spiczak More articles by this author , Thomas HermannsThomas Hermanns More articles by this author , Hatem AlkhadiHatem Alkhadi More articles by this author , and Christian FankhauserChristian Fankhauser More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.449AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Predictive variables for successful shock wave lithotripsy (SWL) in patients with kidney stones include stone composition, size, shape, skin-to-stone distance (SSD) and mean calculus attenuation in Hounsfield Units (HU). However, CT attenuation values are influenced by differences in CT scan protocol settings, region-of-interest (ROI) delineation, magnification and windowing. Therefore published CT attenuation thresholds vary from 593 HU to 1350 HU and correlations between CT attenuation and success of SWL ranges considerably between studies. The aim of this study was to overcome the limitation of CT attenuation thresholds by applying 3D texture analysis (TA). METHODS CT was performed on patients with nephrolithiasis. All patients underwent SWL and received an abdominal CT examination before and after treatment and successful SWL was defined as residual stone size < 2 mm. 3D TA was performed on initial CT after postprocessing for pixel spacing and image normalization. We tested five commonly used machine learning-based models: (1) J48 decision tree (2) k-nearest neighbour (kNN), (3) artificial neural network (aNN) with backpropagation (Multilayer Perceptron), (4) Random Forest, and (5) sequential minimal optimization (SMO). In order to account for overfitting, the data set was split in the recommended ratio of 2/3 for model derivation and 1/3 for validation. Using C statistics we evaluated the discriminatory capability of uni- and multivariaable models with the 3D TA as compared with the models without 3D TA. RESULTS The model derivation and the validation cohort consisted of 34 and 17 patients with a median age of 55.5 and 53.9 years, 35% and 11% females, a median BMI of 27 and 28 and a median stone size of 10.8 and 9.1mm, respectively. Stone disintegration was successful in 22/51 (43.1 %) patients. In a multivariable model taking initial stone size, skin-to-stone distance, and BMI into account the Random Forrest represented the best predictive ability with a sensitivity of 0.59, specificity of 0.87 and an AUC of 0.81. CONCLUSIONS Our in-vivo study indicates the potential of TA to predict successful shock wave lithotripsy. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e162 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Manoj Mannil More articles by this author Jochen von Spiczak More articles by this author Thomas Hermanns More articles by this author Hatem Alkhadi More articles by this author Christian Fankhauser More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Role of computed tomography morphodensitometry in predicting the outcome of shock wave lithotripsy

Objectives: The objective of this study was to predict the outcome of shock wave lithotripsy (SWL) on the basis of computed tomography (CT) morphodensitometry for ureteral and renal stones. We also assessed the possibility that Hounsfield unit (HU) values and the location of the stones could be used to predict the outcome of SWL. Material and methods: A prospective study was performed to measure stone size, location, composition, surface area, surface volume, stone burden, the skin-to-stone distance and the HU for solitary renal and ureteral stones by non-contrast CT studies (NCCT) from August 2013 to September 2015. Success of SWL was defined as: (1) being stone-free or (2) residual stone fragments &lt; 4 mm by radiography/NCCT. Results: Of the 100 assessed patients, 68 patients (68%) were stone-free, 12 (12%) had residual stone fragments &lt; 4 mm (clinically insignificant residual fragments) and 20 (20%) had residual stone fragments ≥ 4 mm/ancillary procedures. Multivariate analysis revealed that stone location and mean HU were significant predictors of SWL success. Receiver operating characteristic curves defined cut-off values for predicting treatment outcome. Treatment success rates were significantly higher for stones &lt; 800 HU than with stones &gt; 800 HU ( p = 0.160). Conclusion: Evaluation of stone HU values and stone location prior to SWL can predict treatment outcome and aid in the development of treatment strategies. Level of evidence 2 &amp; 4

771 - 3D texture analysis in abdominal CT aided by machine learning classifiers predicts shock wave lithotripsy success

771 - 3D texture analysis in abdominal CT aided by machine learning classifiers predicts shock wave lithotripsy success

772 - Automatic measurement of mean stone density by three-dimensional stone images for predicting shock wave lithotripsy success

772 - Automatic measurement of mean stone density by three-dimensional stone images for predicting shock wave lithotripsy success

Role of clinical and radiological parameters in predicting the outcome of shockwave lithotripsy for ureteric stones.

Introduction:Shockwave-lithotripsy (SWL) provides a noninvasive and effective option for the management of ureteric calculi. Several factors may affect the success of SWL. Identification of these predictive factors will both increase the efficacy and decrease the cost. This study was designed to identify factors affecting the outcome of SWL for ureteral stones.Materials and Methods:This study was conducted from March 2012 to November 2014 in patients with solitary ureteric calculi who were managed with SWL. Data were analyzed to identify clinical and radiological factors associated with treatment outcome. Success after SWL was described as complete stone clearance or clinically insignificant residual fragments <3 mm at 3 months after SWL.Results:A total of 110 patients with ureteric calculi were divided into two groups depending on the outcome of SWL, Group A (successful - 76%) and Group B (failed - 24%). Stone size, Skin to stone distance (SSD), secondary signs of obstruction, and presence of double J (DJ) Stent, all were significantly associated with the outcome of SWL on univariate analysis. On multivariate analysis, stone size, hounsfield unit, SSD, and DJ stent were the independent factors affecting the outcome of SWL. On Receptor-Operator Characteristic curve analysis, a cutoff value of 8.2 mm for the stone size was found which best predicts a successful outcome, with a sensitivity of 54% and specificity of 96%.Conclusion:The findings of this study suggest that Stone size, SSD, the presence of DJ stent, and stone attenuation values are the significant factors that influence the outcome of SWL in patients with ureteral stones.

Extracorporeal Shock Wave Lithotripsy success rate for upper and lower ureteric stones in Azadi-teaching (Dohuk) hospital in Kurdistan Region-Iraq

Extracorporeal Shock Wave Lithotripsy success rate for upper and lower ureteric stones in Azadi-teaching (Dohuk) hospital in Kurdistan Region-Iraq

Analysis of the factors influencing the success of the second extracorporeal shock wave lithotripsy in the treatment of upper urinary calculi

Objective To investigate the factors influencing the success of the second extracorporeal shock wave lithotripsy (ESWL) in the treatment of upper urinary calculi. Methods Clinical data of 120 patients with upper urinary tract calculi who underwent second ESWL in our hospital between January 2008 and December 2012 were retrospective analyzed.According to stones were completely discharged, all patients were divided into the successful group (n=80) and failed group (n=40). A retrospective analysis of the medical records of all patients and analysis of related factors included gender, age, duration, body mass index, stone side, stone location, stone length, stone width and number of stones, renal colic, hematuria, degree of hydronephrosis, the value of non enhanced spiral CT, impact frequency, impact energy index were performed. Risk factors for the success of second ESWL in the treatment of patients with upper urinary tract calculi were analyzed by single factor and multiple factor non-conditional logistic regression analysis. Results There were significant differences in the course of the disease, body mass index, length, width, stone, hydronephrosis, renal colic, CT value, the impact number between the two groups (P 0.05). Univariate and multivariate logistic regression analysis showed that body mass index was high, the stone length >2 cm, renal colic, hydronephrosis severity, CT value>750 HU could increase the risk of failure in second ESWL for upper urinary tract calculi (P<0.05). Conclusions We should pay attention to the patient's body mass index, stone length, renal colic, the degree of renal water, CT value, and other key indicators in the process of second use of ESWL in the treatment of upper urinary tract calculi. Key words: Urinary Calculi; Lithotripsy

86 - Predicting the success of extra-corporal shock wave lithotripsy (ESWL) in ureteric stone disease using pre-operative parameters and an artificial neural network

86 - Predicting the success of extra-corporal shock wave lithotripsy (ESWL) in ureteric stone disease using pre-operative parameters and an artificial neural network

Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis.

To apply texture analysis (TA) in computed tomography (CT) of urinary stones and to correlate TA findings with the number of required shockwaves for successful shock wave lithotripsy (SWL). CT was performed on thirty-four urinary stones in an in vitro setting. Urinary stones underwent SWL and the number of required shockwaves for disintegration was recorded. TA was performed after post-processing for pixel spacing and image normalization. Feature selection and dimension reduction were performed according to inter- and intrareader reproducibility and by evaluating the predictive ability of the number of shock waves with the degree of redundancy between TA features. Three regression models were tested: (1) linear regression with elimination of colinear attributes (2), sequential minimal optimization regression (SMOreg) employing machine learning, and (3) simple linear regression model of a single TA feature with lowest squared error. Highest correlations with the absolute number of required SWL shockwaves were found for the linear regression model (r=0.55, p=0.005) using two weighted TA features: Histogram 10th Percentile, and Gray-Level Co-Occurrence Matrix (GLCM) S(3, 3) SumAverg. Using the median number of required shockwaves (n=72) as a threshold, receiver-operating characteristic analysis showed largest area-under-the-curve values for the SMOreg model (AUC=0.84, r=0.51, p<0.001) using four weighted TA features: Histogram 10th Percentile, and GLCM S(1, 1) InvDfMom, S(3, 3) SumAverg, and S(4, -4) SumVarnc. Our in vitro study illustrates the proof-of-principle of TA of urinary stone CT images for predicting the success of stone disintegration with SWL.

A prospective randomised double-blind placebo-controlled trial to assess the effect of diuretics on shockwave lithotripsy of calculi

ObjectiveTo assess the effect of diuretics with shockwave lithotripsy (SWL) on the treatment of renal and upper ureteric calculi. Patients and methodsAdult patients with a solitary non-obstructive radio-opaque renal or upper ureteric calculus with normal renal function were included. They were prospectively randomised to receive either SWL with placebo or SWL with diuretics (40mg parenteral furosemide) in a double-blind manner with a sample size of 48 patients in each arm. The primary outcomes were the SWL success and failure rates. The secondary outcomes were the number of shocks and sessions. ResultsComplete fragmentation was achieved in 89.6% of the patients in the furosemide arm as compared to 81.3% in the placebo arm. Clearance was achieved in 77.1% of the patients in the furosemide arm as compared to 70.8% in the placebo arm. The number of shocks and the number of sessions were higher in the placebo arm. These differences were not statistically significant. ConclusionThe use of diuretics along with SWL treatment of renal and upper ureteric calculi does not show a statistically significant improvement in fragmentation or clearance.

CT Texture Analysis of Ex Vivo Renal Stones Predicts Ease of Fragmentation with Shockwave Lithotripsy.

Understanding the factors affecting success of extracorporeal shockwave lithotripsy (SWL) would improve informed decision-making on the most appropriate treatment modality for an individual patient. Although stone size and skin-to-stone distance do correlate with fragmentation efficacy, it has been shown that stone composition and architecture, as reflected by structural heterogeneity on CT, are also important factors. This study aims to determine if CT texture analysis (CTTA), a novel, nondestructive, and objective tool that generates statistical metrics reflecting stone heterogeneity, could have utility in predicting likelihood of SWL success. Seven spontaneously passed, intact renal tract stones, were scanned ex vivo using standard CT KUB and micro-CT. The stones were then fragmented in vitro using a clinical lithotripter, after which, chemical composition analysis was performed. CTTA was used to generate a number of metrics that were correlated to the number of shocks needed to fragment the stone. CTTA metrics reflected stone characteristics and composition, and predicted ease of SWL fragmentation. The strongest correlation with number of shocks required to fragment the stone was mean Hounsfield unit (HU) density (r = 0.806, p = 0.028) and a CTTA metric measuring the entropy of the pixel distribution of the stone image (r = 0.804, p = 0.039). Using multiple linear regression analysis, the best model showed that CTTA metrics of entropy and kurtosis could predict 92% of the outcome of number of shocks needed to fragment the stone. This was superior to using stone volume or density. CTTA metrics entropy and kurtosis have been shown in this experimental ex vivo setting to strongly predict fragmentation by SWL. This warrants further investigation in a larger clinical study for the contribution of CT textural metrics as a measure of stone heterogeneity, along with other known clinical factors, to predict likelihood of SWL success.

MP62-19 CROSS VALIDATION OF A PREDICTIVE ANALYTIC MODEL WHICH PREDICTS SUCCESS AND COMPLICATIONS OF SHOCKWAVE LITHOTRIPSY

You have accessJournal of UrologyStone Disease: Shock Wave Lithotripsy1 Apr 2017MP62-19 CROSS VALIDATION OF A PREDICTIVE ANALYTIC MODEL WHICH PREDICTS SUCCESS AND COMPLICATIONS OF SHOCKWAVE LITHOTRIPSY Blake Hamilton, Ryan Seltzer, Donald Gleason, Stephen Nakada, and Glenn Gerber Blake HamiltonBlake Hamilton More articles by this author , Ryan SeltzerRyan Seltzer More articles by this author , Donald GleasonDonald Gleason More articles by this author , Stephen NakadaStephen Nakada More articles by this author , and Glenn GerberGlenn Gerber More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.1950AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES 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. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e834 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Blake Hamilton More articles by this author Ryan Seltzer More articles by this author Donald Gleason More articles by this author Stephen Nakada More articles by this author Glenn Gerber More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...