Retrospective study examines patient and operative factors impacting surgical outcomes in ureteral reimplantation, suggesting improved patient management strategies.
Purpose: To examine the impact of predetermined patient and operative factors on ureteral reimplantation surgical outcomes using a rigorous, patient-centric definition of success. Materials and Methods: This was a retrospective single-institution study using a prospectively maintained database of ureteral reimplantation cases (2003-2024). Predefined independent variables included age, frailty index (5-Factor Adjusted Clinical Groups), pelvic radiation history, operative approach (robotic vs nonrobotic), and revision status. Outcome measures included surgical success ( defined as asymptomatic, freedom from stent/nephrostomy tube, no further interventions, and no obstruction on imaging), hospital length of stay (LOS), and 30-day complications. Statistical analyses included linear modeling and logistic regression. Results: One hundred two patients (median age: 50 years) were identified. Overall surgical success was 85%. Pelvic radiation history was inversely correlated with surgical success (63% for radiated vs 91% for nonradiated; P = .002). Higher frailty index scores and the use of the surgical robot predicted 30-day complications. Frailty index score, the use of the nonrobotic approach, and increasing age (estimate: 0.08 ± 0.04 days added per year, P = .03) significantly predicted a longer hospital LOS. The robotic and open approaches had comparable success rates (83% vs 89%, P = .56). Conclusions: Patients undergoing ureteral reimplantation with a history of pelvic radiation demonstrate significantly decreased surgical success, underscoring the challenge of managing radiation-induced strictures. Higher frailty index scores, robotic compared with the open approach, and increasing age are critical preoperative risk factors predicting prolonged hospital stay and increased 30-day complication rates. These findings can improve patient counseling and guide perioperative optimization strategies.
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Doersch et al. (2026) studied this question.
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