Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group.

Kidney transplantation Learning curve Regional hypothermia Robot-assisted kidney transplantation Robotic surgery Vascular anastomosis

Journal

European urology
ISSN: 1873-7560
Titre abrégé: Eur Urol
Pays: Switzerland
ID NLM: 7512719

Informations de publication

Date de publication:
08 2020
Historique:
received: 06 10 2019
accepted: 10 12 2019
pubmed: 14 1 2020
medline: 25 6 2021
entrez: 14 1 2020
Statut: ppublish

Résumé

Recently, robot-assisted kidney transplantation (RAKT) was recently introduced as renal replacement mini-invasive surgery. To report surgical technique, including tips and tricks, and the learning curve for RAKT. 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. 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. 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. 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. A minimum of 35 cases are necessary to reach reproducibility in terms of RWT, complications, and functional results. 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.

Sections du résumé

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.

Identifiants

pubmed: 31928760
pii: S0302-2838(19)30947-9
doi: 10.1016/j.eururo.2019.12.008
pii:
doi:

Types de publication

Journal Article Video-Audio Media

Langues

eng

Sous-ensembles de citation

IM

Pagination

239-247

Commentaires et corrections

Type : CommentIn
Type : CommentIn

Informations de copyright

Copyright © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Auteurs

Andrea Gallioli (A)

Department of Urology, Fundaciò Puigvert, Autonomous University of Barcelona, Barcelona, Spain; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Urology, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy. Electronic address: andrea.gallioli@gmail.com.

Angelo Territo (A)

Department of Urology, Fundaciò Puigvert, Autonomous University of Barcelona, Barcelona, Spain.

Romain Boissier (R)

Department of Urology, Fundaciò Puigvert, Autonomous University of Barcelona, Barcelona, Spain.

Riccardo Campi (R)

Department of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.

Graziano Vignolini (G)

Department of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.

Mireia Musquera (M)

Department of Urology, Hospital Clinic, Barcelona, Spain.

Antonio Alcaraz (A)

Department of Urology, Hospital Clinic, Barcelona, Spain.

Karel Decaestecker (K)

Department of Urology, Ghent University Hospital, Ghent, Belgium.

Volkan Tugcu (V)

Department of Urology, Bakirkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkey.

Davide Vanacore (D)

Department of Urology, Fundaciò Puigvert, Autonomous University of Barcelona, Barcelona, Spain; Department of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy.

Sergio Serni (S)

Department of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.

Alberto Breda (A)

Department of Urology, Fundaciò Puigvert, Autonomous University of Barcelona, Barcelona, Spain.

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