Ioannis Vakalopoulos1. 1. Department of Urology, Aristotle University of Thessaloniki, Thessaloniki, Greece. vakalj@otenet.gr
Abstract
PURPOSE: To determine statistically significant parameters that influence shockwave lithotripsy (SWL) outcome and to develop a mathematical equation to predict the possibility of a successful outcome. PATIENTS AND METHODS: Radiopaque ureteral and renal stones (1997) underwent SWL in a single center using the Siemens Lithostar Plus lithotripter. Investigated variables were stone size and location; presence of multiple stones; patient sex, age, body mass index (BMI), and ureteropelvic junction (UPJ) diameter; and Double-J catheter presence. The investigation included intravenous urography or ultrasonography and plain radiography of the kidneys, ureters, and bladder (KUB). Ten percent of the patients also underwent unenhanced CT to determine stone volume and density. Patients were evaluated 3 months postoperatively by plain radiography of the KUB. Student t test, chi-square test, and multiple logistic regression analysis were used to determine statistically significant variables and to create predictive mathematical equations. RESULTS: Kidney, ureter, total stone-free rate, and re-treatment rate were 71%, 80.9%, 74.9% and 18%, respectively. Complications included two perirenal hematomas, one significant cardiac arrhythmia, and 3.7% stein-strasse. Statistically significant variables were patient age, sex, BMI, stone size, volume, density, multiple lithiasis, and UPJ diameter. By using statistical analysis, coefficients were calculated for each significant variable. The mathematical model calculating the probability (P) of success with a mean predictive value of about 80% is: P = 1/1 + e(-z) where e is the base of the natural logarithm and z = ct + the total of products of each coefficient multiplied by each variable value. CONCLUSIONS: Using the described method, similar predictive equations can be created for different lithotripters and used by computer software to aid both patient counseling and doctor decision and ensure an optimal combination of maximum efficacy and safety and minimum cost.
PURPOSE: To determine statistically significant parameters that influence shockwave lithotripsy (SWL) outcome and to develop a mathematical equation to predict the possibility of a successful outcome. PATIENTS AND METHODS: Radiopaque ureteral and renal stones (1997) underwent SWL in a single center using the Siemens Lithostar Plus lithotripter. Investigated variables were stone size and location; presence of multiple stones; patient sex, age, body mass index (BMI), and ureteropelvic junction (UPJ) diameter; and Double-J catheter presence. The investigation included intravenous urography or ultrasonography and plain radiography of the kidneys, ureters, and bladder (KUB). Ten percent of the patients also underwent unenhanced CT to determine stone volume and density. Patients were evaluated 3 months postoperatively by plain radiography of the KUB. Student t test, chi-square test, and multiple logistic regression analysis were used to determine statistically significant variables and to create predictive mathematical equations. RESULTS: Kidney, ureter, total stone-free rate, and re-treatment rate were 71%, 80.9%, 74.9% and 18%, respectively. Complications included two perirenal hematomas, one significant cardiac arrhythmia, and 3.7% stein-strasse. Statistically significant variables were patient age, sex, BMI, stone size, volume, density, multiple lithiasis, and UPJ diameter. By using statistical analysis, coefficients were calculated for each significant variable. The mathematical model calculating the probability (P) of success with a mean predictive value of about 80% is: P = 1/1 + e(-z) where e is the base of the natural logarithm and z = ct + the total of products of each coefficient multiplied by each variable value. CONCLUSIONS: Using the described method, similar predictive equations can be created for different lithotripters and used by computer software to aid both patient counseling and doctor decision and ensure an optimal combination of maximum efficacy and safety and minimum cost.