Hevia M et al, 2017: Predicting the effectiveness of extracorporeal shock wave lithotripsy on urinary tract stones. Risk groups for accurate retreatment.
Hevia M, García Á, Ancizu FJ, Merino I, Velis JM, Tienza A, Algarra R, Doménech P, Diez-Caballero F, Rosell D, Pascual JI, Robles JE.
Departamento de Urología, Clínica Universidad de Navarra, Pamplona, España.
Servicio de Urología, Hospital Universitario Son Espases, Mallorca, España.
Departamento de Urología, Hospital Nuestra Señora del Rosario, Madrid, España.
INTRODUCTION: Extracorporeal shock wave lithotripsy (ESWL) is a non-invasive, safe and effective treatment for urinary tract lithiasis. Its effectiveness varies depending on the location and size of the stones as well as other factors; several sessions are occasionally required. The objective is to attempt to predict its success or failure, when the influential variables are known beforehand.
MATERIAL AND METHODS: We analysed 211 patients who had had previous CT scans and were treated with ESWL between 2010 and 2014. The influential variables in requiring retreatment were studied using binary logistic regression models (univariate and multivariate analysis): maximum density, maximum diameter, area, location, disintegration and distance from the adipose panniculus. With the influential variables, a risk model was designed by assessing all possible combinations with logistic regression (version 20.0 IBM SPSS).
RESULTS: The independent influential variables on the need for retreatment are: maximum density >864HU, maximum diameter >7.5mm and pyelocaliceal location. Using these variables, the best model includes 3risk groups with a probability of requiring significantly different retreatment: group 1-low risk (0 variables) with 20.2%; group 2-intermediate risk (1-2 variables) with 49.2%; and group 3-high risk (3 variables) with 62.5%.
CONCLUSIONS: The density, maximum diameter and pyelocaliceal location of the stones are determinant factors in terms of the effectiveness of treatment with ESWL. Using these variables, which can be obtained in advance of deciding on a treatment, the designed risk model provides a precise approach in choosing the most appropriate treatment for each particular case.
Actas Urol Esp. 2017 Mar 3. pii: S0210-4806(17)30016-5. doi: 10.1016/j.acuro.2016.12.008. [Epub ahead of print] English, Spanish
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