İsmail Onder YILMAZ, Nebil AKDOGAN, Mehmet Gurkan ARIKAN, Ozgur YILMAZ, Tunahan ATES, Mutlu DEGER, Nihat SATAR
Annals of Clinical and Analytical Medicine - 2026;17(6):566-571
Aim: To create a multivariate prediction model based on machine learning to find predictors of recurrence after Anderson-Hynes dismembered pyeloplasty. Methods: Patients younger than 15 who underwent primary open Anderson-Hynes Dismembered Pyeloplasty between 2011 and 2020 were evaluated. Logistic regression, support vector machine, and random forest were used to train a classifier for predicting recurrence, and the feature importance analysis methods were performed to understand which predictors have more weight in the models. Results: Of the patients, 134 were boys, and 43 were girls, with a mean age of 30.4 (1-168) months. Recurrence developed in 15 / 177 (8.4%) of the patients. Postoperative anteroposterior renal pelvis diameter and intraoperative urine aspiration volume were the strongest predictors of recurrence. The Random Forest model achieved the best accuracy (AUC = 0.94) in predicting recurrence in patients under 15. Conclusion. To our knowledge, this is the first study to investigate whether the amount of urine aspirated from the intraoperative renal pelvis is predictive of recurrence. We found that the probability of recurrence of ureteropelvic junction obstruction increased as the amount of urine aspirated from the intraoperative renal pelvis increased.
