Prospective evaluation of visual function in patients with ocular diseases after robot-assisted laparoscopic prostatectomy

Clinical significance and risk factors of urethrovesical anastomotic urinary leakage following robot-assisted radical prostatectomy: a multi-institutional study

Background

There has been a limited number of reports on the significance and risk factors of urethrovesical anastomotic urinary leakage (AUL) following robot-assisted radical prostatectomy (RARP). We aimed to analyze the clinical significance of AUL and evaluated its risk factors.

Methods

We conducted a multi-institutional study to review patients with prostate cancer undergoing RARP in three centers (The University of Tokyo Hospital, Mitsui Memorial Hospital, and Chiba Tokushukai Hospital). “Positive AUL” was defined as urinary extravasation at the anastomosis detected by post-operative cystogram and was further categorized into minor or major AUL. Univariate and multivariate analyses were performed to identify predictors of AUL. Postoperative continence rates and time to achieve continence were also analyzed.

Results

A total of 942 patients underwent RARP for prostate cancer in 3 centers. Of these patients, a cystogram after the RARP procedure was not performed in 26 patients leaving 916 patients for the final analysis. AUL was observed in 56 patients (6.1%); 34 patients (3.7%) with minor AUL and 22 patients (2.4%) with major AUL. Patients with major AUL exhibited a significantly longer time to achieve continence than those without major AUL. Multivariate analysis demonstrated that longer console time (≥ 184 min) was significantly associated with overall AUL, and higher body mass index (≥ 25 g/kg²) was a significant predictor of both major and overall AUL.

Conclusions

The presence of major AUL was associated with the achievement of urinary continence, suggesting clinical relevance of its diagnosis by postoperative cystogram. A selective cystogram has been proposed for high-risk cases. Furthermore, identification of the risk factors of AUL will lead to optimal application.