Background: Recently, robot-assisted kidney transplantation (RAKT) was recently introduced as renal replacement mini-invasive surgery. Objective: To report surgical technique, including tips and tricks, and the learning curve for RAKT. Design, setting, and participants: All consecutive RAKTs performed in the five highest-volume centers of the European Robotic Urological Society RAKT group were reviewed, and a step-by-step description of the technique was compiled. Surgical procedure: Surgeries were performed with Da Vinci Si/Xi. The patient was placed in the lithotomy position. The Trendelenburg position was set at 20–30° and the robot was docked between the legs. Measurements: Shewhart control charts and cumulative summation (CUSUM) graphs and trifecta were generated to assess the learning curve according to rewarming time (RWT), intra/postoperative complications, and renal graft function (glomerular filtration rate) on days 7 and 30, and at 1 yr. Linear regressions were performed to compare the learning curves of each surgeon. Results and limitations: Arterial anastomosis time was below the alarm/alert line in 93.3%/88.9% of RAKTs, while venous anastomosis time was below the alarm/alert line in 88.9%/73.9%. The nonanastomotic RWT exceeded +3 standard deviation (SD) in 24.7% of procedures and +2SD in 37.1%. In only 46% cases, the RWT was below the alert line. The ureteroneocystostomy time was below +2SD and +3SD in 87.9% and 90.2% of cases, respectively. CUSUM showed that the learning curve for arterial anastomosis required up to 35 (mean = 16) cases. Complications and delayed graft function rates decreased significantly and reached a plateau after the first 20 cases. Trifecta was achieved in 75% (24/32) of the cases after the first 34 RAKTs in each center. Conclusions: A minimum of 35 cases are necessary to reach reproducibility in terms of RWT, complications, and functional results. Patient summary: Robot-assisted kidney transplantation requires a learning curve of 35 cases to achieve reproducibility in terms of timing, complications, and functional results. Synergy between the surgeon and the assistant is crucial to reduce rewarming time. High-grade complications and delayed graft function are rare after ten surgeries. Hands-on training and proctorship are highly recommended. Robot-assisted kidney transplantation (RAKT) requires a learning curve of 35 cases. Teamwork is crucial to reduce rewarming time. High-grade complications are rare after ten surgeries; functional results are significantly better after 20 cases of RAKT. Hands-on training/proctorship is highly recommended.
Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group / Gallioli A.; Territo A.; Boissier R.; Campi R.; Vignolini G.; Musquera M.; Alcaraz A.; Decaestecker K.; Tugcu V.; Vanacore D.; Serni S.; Breda A.. - In: EUROPEAN UROLOGY. - ISSN 0302-2838. - ELETTRONICO. - (2020), pp. 0-0. [10.1016/j.eururo.2019.12.008]
Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group
Campi R.;Vignolini G.;Vanacore D.;Serni S.;
2020
Abstract
Background: Recently, robot-assisted kidney transplantation (RAKT) was recently introduced as renal replacement mini-invasive surgery. Objective: To report surgical technique, including tips and tricks, and the learning curve for RAKT. Design, setting, and participants: All consecutive RAKTs performed in the five highest-volume centers of the European Robotic Urological Society RAKT group were reviewed, and a step-by-step description of the technique was compiled. Surgical procedure: Surgeries were performed with Da Vinci Si/Xi. The patient was placed in the lithotomy position. The Trendelenburg position was set at 20–30° and the robot was docked between the legs. Measurements: Shewhart control charts and cumulative summation (CUSUM) graphs and trifecta were generated to assess the learning curve according to rewarming time (RWT), intra/postoperative complications, and renal graft function (glomerular filtration rate) on days 7 and 30, and at 1 yr. Linear regressions were performed to compare the learning curves of each surgeon. Results and limitations: Arterial anastomosis time was below the alarm/alert line in 93.3%/88.9% of RAKTs, while venous anastomosis time was below the alarm/alert line in 88.9%/73.9%. The nonanastomotic RWT exceeded +3 standard deviation (SD) in 24.7% of procedures and +2SD in 37.1%. In only 46% cases, the RWT was below the alert line. The ureteroneocystostomy time was below +2SD and +3SD in 87.9% and 90.2% of cases, respectively. CUSUM showed that the learning curve for arterial anastomosis required up to 35 (mean = 16) cases. Complications and delayed graft function rates decreased significantly and reached a plateau after the first 20 cases. Trifecta was achieved in 75% (24/32) of the cases after the first 34 RAKTs in each center. Conclusions: A minimum of 35 cases are necessary to reach reproducibility in terms of RWT, complications, and functional results. Patient summary: Robot-assisted kidney transplantation requires a learning curve of 35 cases to achieve reproducibility in terms of timing, complications, and functional results. Synergy between the surgeon and the assistant is crucial to reduce rewarming time. High-grade complications and delayed graft function are rare after ten surgeries. Hands-on training and proctorship are highly recommended. Robot-assisted kidney transplantation (RAKT) requires a learning curve of 35 cases. Teamwork is crucial to reduce rewarming time. High-grade complications are rare after ten surgeries; functional results are significantly better after 20 cases of RAKT. Hands-on training/proctorship is highly recommended.
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