The aim of this study is to develop a structured training program for robotic radical nephroureterectomy (RNU), based on a Delphi consensus among a panel of experts in this field. A standard questionnaire will be used to obtain experts' opinions on the training steps for robotic RNU.
Upper tract urothelial carcinoma (UTUC) is an uncommon, yet biologically heterogeneous disease that accounts for 5-10% of all urothelial tumors. Radical nephroureterectomy (RNU) with bladder cuff excision is the gold standard for the treatment of UTUC patients. While this procedure is traditionally performed via an open approach, minimally invasive techniques have been used more frequently in recent years. A population-based analysis recently reported an increasing trend in the utilization of robotic RNU from 29% in 2010 to 53% in 2016. Robotic approach not only provides better visualization and optimal exposure during RNU but is also associated with improved perioperative outcomes compared to the open method. Previous studies have shown that patients treated during the learning phase of a surgeon are at risk of inferior outcomes relative to those treated by experienced surgeons. To overcome such suboptimal outcomes, specific training curriculums have been proposed for some urologic procedures, such as robotic radical prostatectomy, radical cystectomy, and partial nephrectomy. Nevertheless, despite the widespread use of robot for RNU in recent years, no training program is currently available to assist urology residents during their learning process. This study will provide the first training program for robotic RNU. This curriculum can help to track the progression of the trainee and ensure that defined benchmarks of skills will be reached before the trainee progresses to the next level of difficulty. In addition, it will ultimately improve patients' safety during the learning phase of the urologists.
Study Type
OBSERVATIONAL
Enrollment
24
An invitation email, including a link to the survey, will be sent to the panel of experts in the field of robotic RNU. The Delphi questionnaire will be administered via Welphi.com. In the first survey, panel members will outline the training program for robotic RNU. In subsequent surveys, the expert panel will evaluate the modified criteria using a 1 to 5-point Likert scale with space provided for suggested edits and comments. Multiple rounds will be conducted until consensus is reached. After each round of Likert responses, the study team will calculate the agreement and distribution of responses. Likert responses will be dichotomized with positive values indicating agreement and neutral or negative values indicating disagreement. For the questions that do not reach a consensus of more than 80% in the first round or need further explanation, additional rounds of the survey may be performed.
Hooman Djaladat
Los Angeles, California, United States
Degree of consensus
The level of agreement for all statements achieving consensus from the expert panel; consensus is predefined as ≥ 80% of the panel rating a given statement
Time frame: 12 months
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