Login / Signup

Predictors of reflux persistence after endoscopic dextranomer/hyaluronic Acid copolymer injection in pediatric patients with Vesicoureteral reflux: short-term results.

Ismail Onder YilmazNebil AkdoğanMutlu DeğerIbrahim Atilla AridoganVolkan İzolNihat Satar
Published in: Scientific reports (2024)
This study aims to investigate the factors effective in predicting the persistence of reflux after the first subureteric transurethral injection (STING) of dextranomer/hyaluronic acid copolymer in pediatric patients with vesicoureteral reflux. The data of patients without a previous history of surgery to treat vesicoureteral reflux and who underwent STING for the first time between September 2011 and November 2020 were investigated retrospectively. After considering exclusion criteria, of 199 patients, 127 patients and 180 renal units were suitable for inclusion. A renal unit-based evaluation was made. Age < 61 months (univariate: p = 0.001, multivariate: p = 0.015, HR: 2.352 (1.181-4.686), OR (95% CI)), moderate reflux level (grade 3) (univariate: p < 0.001, multivariate: p = 0.019, HR: 2.703 (1.177-6.209), OR (95% CI)), DRF (differential renal function) < 45 (univariate: p = 0.020, multivariate: p = 0.047, HR: 1.992 (1.009-3.935), OR (95% CI)), and UDR (ureteral diameter ratio) > 0.15 (univariate: p < 0.001, multivariate: p = 0.005, HR: 2.786 (1.368-5.672), OR (95% CI)) were found predictors of reflux persistence after STING surgery both univariate and multivariate analysis. High reflux level (grade 4-5) was statistically significant in univariate analysis (p < 0.001) but not statistically significant in multivariate analysis (p = 0.215). In our study, UDR and DRF were found to be factors affecting reflux persistence. UDR and DRF should be considered in order to predict reflux resolution in patients who will undergo STING.
Keyphrases
  • hyaluronic acid
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • data analysis
  • prognostic factors
  • ultrasound guided
  • machine learning
  • acute coronary syndrome
  • artificial intelligence
  • patient reported