PREDICTORS FOR SUCCESSFUL ENDOSCOPIC CORRECTION OF VESICOURETERAL REFLUX IN CHILDREN

Predicting the outcome of endoscopic correction of vesicoureteral reflux (VUR) is an urgent problem in pediatric urology because the identification of predictors for the effectiveness of endoscopic treatment of VUR will optimize the strategy for examination and treatment of the disease. The purpose of the research was to evaluate the statistical impact of various predictors on the success of endoscopic correction of VUR; to predict the clinical outcome by creating a mathematical model. Materials and methods used: a single-center experimental uncontrolled study was conducted in Sept. 2017 - Feb. 2022. The results of treatment of 150 pediatric patients (240 renal units) aged from 1 month till 7 years old who underwent endoscopic correction of VUR are presented. The following indicators were evaluated as predictors: gender, age of the disease manifestation, bilateral/unilateral VUR, grade of VUR, ureteral diameter ratio (UDR), cystography initial confirmation of VUR, expansion of the pelvicalyceal system and ureter according to ultrasound, the presence of urinary tract infection. Results: according to the data obtained, when comparing the grade of VUR, cystography initial confirmation of VUR, UDR, expansion of the pelvis and ureter depending on the result of treatment, the statistically significant differences have been found (p<0.001). A prognostic model has been developed to determine the probability of the outcome of endoscopic correction of VUR using the binary logistic regression method. The area under the ROC curve was 0.985±0.007 with 95% CI: 0.967-1.000. The resulting regression model was statistically significant (p<0.001). The sensitivity and specificity of the model were 96.4% and 95.3%, respectively. Conclusion: the results of the research show that UDR and the initial confirmation of VUR have greater predictive power for the success of the endoscopic urethroplasty compared to the grade of VUR alone. The use of the predictors and predictive modeling can help improving the selection of candidates for the endoscopic correction of VUR and developing personalized treatment tactics.