Development and validation of the objective assessment of robotic suturing and knot tying skills for chicken anastomotic
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and Other Interventional Techniques
Development and validation of the objective assessment of robotic suturing and knot tying skills for chicken anastomotic model Stefano Puliatti1,2,3 · Elio Mazzone4,5 · Marco Amato1,2,3 · Ruben De Groote1,2 · Alexandre Mottrie1,2 · Anthony G. Gallagher1,6,7 Received: 10 June 2020 / Accepted: 17 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Background To improve patient safety, there is an imperative to develop objective performance metrics for basic surgical skills training in robotic surgery. Objective To develop and validate (face, content, and construct) the performance metrics for robotic suturing and knot tying, using a chicken anastomotic model. Design, setting and participants Study 1: In a procedure characterization, we developed the performance metrics (i.e., procedure steps, errors, and critical errors) for robotic suturing and knot tying, using a chicken anastomotic model. In a modified Delphi panel of 13 experts from four EU countries, we achieved 100% consensus on the five steps, 18 errors and four critical errors (CE) of the task. Study 2: Ten experienced surgeons and nine novice urology surgeons performed the robotic suturing and knot tying chicken anastomotic task. The mean inter-rater reliability for the assessments by two experienced robotic surgeons was 0.92 (95% CI, 0.9–0.95). Novices took 18.5 min to complete the task and experts took 8.2 min. (p = 0.00001) and made 74% more objectively assessed performance errors than the experts (p = 0.000343). Conclusions We demonstrated face, content, and construct validity for a standard and replicable basic anastomotic robotic suturing and knot tying task on a chicken model. Patient summary Validated, objective, and transparent performance metrics of a robotic surgical suturing and knot tying tasks are imperative for effective and quality assured surgical training. Keywords Surgical training · Proficiency-based metrics · Face · content and construct validation First authorship shared by Stefano Puliatti and Elio Mazzone. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00464-020-07918-5) contains supplementary material, which is available to authorized users. * Marco Amato [email protected] 1
ORSI Academy, Melle, Belgium
2
Department of Urology, OLV, Aalst, Belgium
3
Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
4
Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
5
Vita-Salute San Raffaele University, Milan, Italy
6
Faculty of Life and Health Sciences, Ulster University, Derry, Northern Ireland, UK
7
Faculty of Medicine, KU Leuven, Leuven, Belgium
There were more than 4500 Da Vinci robotic systems (Intuitive Surgical Inc., Sunnyvale, CA, USA) operating around the world in 2018 and are currently the most widely used robotic system [1]. There are, however, a number of new robotic surgical systems being introduced into the market pla
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