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Comparing the Modified Novel Trans-Retro Approach vs Conventional Retroperitoneal Approach in Robotic-Assisted Renal Surgery: An Open Label Randomized Controlled Trial
The purpose of this study is to determine if the novel TR approach is superior to the standard RP approach. The anticipated study outcome is a time saving of at least 30% from first skin incision to detection of the renal artery compared to the conventional RP approach, and also a better workspace perception by the operating surgeon.
The trans abdominal approach (TA) for total and partial nephrectomy (PN) has been widely adopted due to the easy trocar placement and the good working space. The retroperitoneal approach (RP) has gained popularity because the renal artery is often found fast and the operation remains in an anatomically separated space, making it preferable, especially for patients who underwent abdominal surgery in the past. However, both approaches face difficulties. Trocar placement for RP can be challenging, and the working space often is limited, while TA is impaired in cases of dorsal tumors and dissection of the renal artery can be challenging due to the anatomic localization dorsally to the renal vein. Up until now, no direct systematically and prospective comparison of these two approaches was performed. The overall objective of this trial is to assess if the novel TR approach is superior to the conventional RP approach in performing robotic assisted (partial) nephrectomy. To date, no systematic, prospective, randomised study has been conducted on this topic and described in the literature.
Age
18 - No limit years
Sex
ALL
Healthy Volunteers
No
Kantonsspital Baden
Baden, Canton of Aargau, Switzerland
Start Date
May 15, 2022
Primary Completion Date
December 15, 2024
Completion Date
January 15, 2025
Last Updated
November 28, 2025
40
ACTUAL participants
Robotic-assisted laparoscopic partial nephrectomy (LPN) or laparoscopic nephrectomy (LN)
PROCEDURE
Lead Sponsor
Lukas J Hefermehl
Collaborators
NCT00050752
NCT06322745
Data Source & Attribution
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