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Robot-Assisted Radical Nephrectomy Using the Novel Avatera Robotic Surgical System: A Feasibility Study in a Porcine Model.

Kristiana GkekaTsaturyan ArmanSolon FaitatziadisAngelis PeteinarisEirini AnapliotiKonstantinos PagonisAthanasios VagionisVasileios TatanisTheofanis VrettosPanagiotis KallidonisEvangelos Liatsikos
Published in: Journal of endourology (2022)
Purpose: To evaluate the feasibility and intraoperative technical parameters of the new robot-assisted surgical system Avatera by performing bilateral nephrectomy in a live porcine model. Materials and Methods: Six pigs underwent bilateral robot-assisted radical nephrectomy using the Avatera robotic system (RS). The operations were performed by experienced open (Group 1) and laparoscopic (Group 2) surgeons. The operating time, docking time, set-up time, and console time were evaluated. Data regarding intraoperative complications (major or not), injury of adjacent organs, and technical difficulties during the operation were also recorded. Results: Robot-assisted bilateral nephrectomy was completed effectively in all sample pigs. The docking time and set-up time were similar among the two groups, whereas the console time and operating time were significantly longer for the open surgeon group. Two intraoperative minor complications occurred in the open surgeon's group. An inadvertent damage of the renal vein occurred in two cases and the bleeding was controlled effectively. Other than the initial two cases, the estimated blood loss was <50 mL in both groups. Injury of the adjacent organs was not noticed. Conclusions: The study demonstrated that radical nephrectomy using the Avatera robotic surgical system is technically feasible, safe, and valid in pigs. Our results warrant the application of the Avatera RS in other urologic procedures, collecting data for further clinical trials.
Keyphrases
  • robot assisted
  • minimally invasive
  • clinical trial
  • molecular dynamics
  • patients undergoing
  • molecular dynamics simulations
  • case report
  • protein protein
  • big data
  • oxidative stress
  • atrial fibrillation
  • deep learning