Bladder cancer is the 10th most common cancer worldwide. Approximately 75% of patients with bladder cancer will present with non-muscle invasive disease. Patients are usually treated with transurethral resection of bladder tumor (TURBT), in addition to adjuvant intravesical therapy (chemotherapy or anti-cancer immunotherapy with Bacillus Calmette Guerin- BCG) for those at intermediate-risk and high-risk of recurrence and progression. For many years, urine has been thought to be “sterile”; however, advanced microbiological and molecular techniques, including 16S ribosomal RNA (16S rRNA) sequencing, have negated that previous paradigm and confirmed the presence of a urinary microbiome. The urinary microbiome has been associated with several urological diseases, including interstitial cystitis, urgency urinary incontinence, neurogenic bladder dysfunction, and others. More recently, many reports are emerging about the role of the urinary microbiome in urothelial carcinogenesis, including gender disparity in bladder cancer and responses to treatments. The urinary microbiome may serve as a biomarker that can help with risk stratification as well as prediction of the response to intravesical therapies. However, the microbiome literature has been hampered by the lack of a unified standardized methodology for sample collection, type, preservation, processing, as well as bioinformatics analysis. Herein we describe and critique the literature on the association between urinary microbiome and bladder cancer and highlight some of the future directions.

Objective : To describe urinary tract infections (UTIs) after robot-assisted radical cystectomy (RARC) and investigate the variables associated with it. Methods and Material: A retrospective review of 616 patients who underwent RARC 2005-2019 was performed. Patients were divided into those who developed UTI and those who did not. Patients who developed UTI were further subdivided into three subgroups according to the onset, number and severity. Kaplan-Meier method was used to depict time to UTI. Multivariate analysis was used to investigate variables associated with UTI. Result: 240 (39%) patients were diagnosed with UTI after RARC; 48% occurred within 30-days, 17% within 30-90 days, and 35% at 90 days after RARC. 23% of the patients presented with urosepsis. The median (IQR) time to develop UTI was 1 (0.3, 7) month. On multivariate analysis, patients who received neobladders (OR 2.80; 95% CI 1.50-5.20; p < 0.01), prolonged hospital stay (OR 1.06; 95% CI 1.03-1.08; p < 0.01), receiving adjuvant chemotherapy (OR 2.20; 95% CI 1.40-3.60; p < 0.01), had poor renal function postoperatively (OR 2.30; 95% CI 1.30-3.80; p<0.01), postoperative hydronephrosis (OR 2.50; 95% CI 1.40-4.50; p < 0.01), ureteroileal anastomotic stricture (OR 2.90; 95% CI 1.50-5.70; p < 0.01) and stented ureteroileal anastomosis (OR 9.35; 95% CI 1.23-71.19; p = 0.03) were associated with UTI after RARC. Conclusion: UTI is common after RARC mainly within 1st month after RARC. Enterococcus Fecalis was the most common causative organism.

There is lack of a standardized measure of technical proficiency and skill acquisition for robot-assisted surgery (RAS). Learning surgical skills, in addition to the interaction with the machine and the new surgical environment adds to the complexity of the learning process. Moreover, evaluation of surgeon performance in operating room is required to optimize patient safety. In this study, we investigated the dynamic changes of RAS trainee’s brain functional states by practice. We also developed brain functional state measurements to find the relationship between RAS skill acquisition (especially human-machine interaction skills) and reconfiguration of brain functional states. This relationship may help in providing trainees with helpful, structured feedback regarding skills requiring improvement and will help in tailoring training activities.

Introduction: We sought to evaluate the Robotic Anastomosis Competency Evaluation (RACE) — a validated tool that objectively quantifies surgical skills specifically for urethrovesical anastomosis (UVA) — as a means to track progress of trainees, as well as to determine its predictive value. Methods: UVAs performed by trainees at our institution were evaluated using RACE over a period of two years. Trainees were supervised by an experienced robotic surgeon. Outcomes included traineerelated variables (RACE score, proportion of UVA performed by trainee, and suturing speed), and clinical outcomes (total UVA duration, postoperative urinary continence, and UVA-related complications). Significance was determined using linear regression analysis. Results: A total of 51 UVAs performed by six trainees were evaluated. Trainee RACE scores (19.8 to 22.3; p=0.01) and trainee proportion of UVA (67% to 80%; p=0.003) improved significantly over time. Trainee suture speed was significantly associated with RACE score (mean speed range 0.54–0.74 sutures/minute; p=0.03). Neither urinary continence at six weeks nor at six months was significantly associated with RACE score (p=0.17 and p=0.15, respectively), and only one UVA-related postoperative complication was reported. Conclusions: Trainee RACE scores improved and the proportion of UVA performed by trainees increased over time. RACE can be used as an objective measure of surgical performance during training. Strict mentor supervision allowed safe training without compromising patient outcomes.