Summary - Salute per tutti

Indications, prediction of success and methods to improve outcome of shock wave lithotripsy of renal and upper ureteral calculiproducing unnecessary renal or surrounding organs tissuedamage. In this patient population SWL is not recommendedin the context of patients with BMI?30, stonesize > 10 mm and stone density in CT scan > 900 HU(9). Finally, although SWL can be effectively and safelyadministered to patients on antithrombotic therapywhen performed through a heparin window, it isabsolutely contraindicated in patients with uncorrectedbleeding diatheses. In case withdrawal of anticoagulationtherapy is precluded flexible ureteroscopy is the preferredtreatment option (9).Predicting the outcome of SWLTaking into consideration that several factors affect SWLoutcome, it makes sense that a model or other factorscapable of predicting SWL success rate in advance, couldbe of major importance.NomogramsRecently, a multivariate analysis and logistic regressionanalysis on patient age, sex and body mass index, numberof stones in each treatment, stone size, side and locationwas published. Stone size, location and numberwere identified as significant variables on multivariateanalysis and were included in a prediction nomogram.According to this nomogram the stone-free probabilitywas highest for solitary proximal ureteral stones 21 mm (10.5%) (42).In another study, the success rate of SWL at 3 months forthe treatment of renal stones < 30 mm could be predictedby stone size, location and number, radiological renalfeatures and congenital renal anomalies. Other factorsincluding age, sex, nationality, de novo or recurrentstone formation and ureteric stenting had no significantimpact on the overall success rate (43).Failure of SWL in the treatment of ureteral stones is significantlyrelated to pelvic location, stone size > 10 mm,ureteral obstruction and obesity (BMI > 30). Thestrongest independent predictors of failure were pelvicstones and stones > 10 mm (44). In another study on themanagement of ureteral calculi, stone size was the onlysignificant factor correlating with failure (45).Artificial neural networks (ANN)ANN has recently been created to predict the outcome ofSWL. In a recent study of predicting optimum renalstone fragmentation after SWL, ANN identified stonesize as the most influential variable, followed by totalnumber of shocks given and 24-hour urinary volume.ANN accurately predicted optimal fragmentation in 77%of patients and identified all patients in whom fragmentationdid not occur (46).In another study an ANN, that incorporated bothanatomic factors and dynamic measurements of urinarytransport from intravenous urograms, was created to predictclearance of lower pole stones. ANN was shown tohave a 92% predictive accuracy. Overall stone clearancewas reported at 68% and the most influential prognosticvariables were pathological urinary transport, infundibuloureteropelvicangle 2, body mass index and calicealpelvic height which had a 15-fold relative weight overother inputs (47).An artificial neural network can also help in accurateprediction of those who would be stone-free after SWLfor ureteral stones. In a recent study, for a total stone-freerate of 93.3%, an ANN including demographic patientdata and stone characteristics showed that stone length,location, stent use and stone width were the most influentialinput variables. Comparing logistic regression withANN revealed a sensitivity of 100 and 77.9%, a specificityof 0 and 75%, a positive predictive value of 93.2and 97.2% and an overall accuracy of 93.2 and 77.7%,respectively (48).Computed Tomography (CT) findingsNo consensus exists on the use of Hounsfield units (HU)and stone fragility, although evidence points to higherHU being SWL resistant (49, 50). The various characteristicsof renal stones as determined by non-contrast CTscan have been related to SWL outcome. A recent multivariateanalysis demonstrated that a stone burden ofmore than 700 mm 3 , the presence of non-round/ovalstones and a maximal stone density of more than 900HU were statistically significant predictors of a failureoutcome for SWL (51). Another study showed a worstSWL outcome in patients with calculus densities of> 750 HU and diameters of > 11 mm, with stone-freerates of only 60% and with re-treatment rates of 77%(52). Total stone volume, mean attenuation value and theheterogeneity of the attenuation value histogram successfullypredicted SWL success rate in renal and proximalureteral stones with an accuracy of 82.1%, 83.9%and 91.1%, respectively (53). In a recent retrospectivestudy, logistic regression analysis showed that the skinto-stonedistance was the only significant predictor ofstone-free status after SWL, compared to BMI and HUdensity. A distance more than 10 cm was associated withtreatment failure (54).Methods to improve SWL outcomeShock wave rateShock wave rate is well known to affect stone fragmentation.Several prospective randomized studies showedthat for renal or proximal ureteral stones slow-rate SWL(60-90 shocks/min) resulted in a better outcome thanfast-rate SWL (120 shocks/min) (10, 55-59). Overallsuccess rates of 75-98.7% have been reported for theslow-rate group compared to 61-90% for the fast-rategroup (56,57). This benefit is more marked for largerstones. For a diameter between 10-20 mm stone-freerates of 32-46% and 67-71% have been reported for thefast-rate (120 shocks/min) and the slow-rate (60-80shocks/min) groups, respectively (57, 58). When stones< 10 mm are being treated differences between the twogroups become less significant. It is difficult to decideabout the optimal shockwave frequency. A recentprospective randomized study compared 60, 90 and 120shockwaves per minute frequencies and showed that theoptimal frequency in terms of duration, efficacy andanalgesic and sedative requirement at the same totalenergy level, was the 90 shocks per minute (58). SlowerArchivio Italiano di Urologia e Andrologia 2010; 82, 159