The learning curve for robotic-assisted pyeloplasty in urologists with no prior robotic experience using an ex vivo model: A prospective, controlled study

Submitted: August 30, 2024
Accepted: September 9, 2024
Published: November 11, 2024
Abstract Views: 485
PDF: 148
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Introduction: Despite the increasing trend of utilizing robotic techniques in pyeloplasty, little is known about the learning curve for robot-assisted pyeloplasty (RAP) amongst urologists with no prior robotic experience. Therefore, the present study aimed to evaluate the learning curve of residents in the last year or recently appointed urologists performing RAP using an ex-vivo model.

Methods: A prospective ex-vivo model study was conducted including participants who were either residents in the last year or recently appointed urologists. All participants had obtained the E-BLUS certification, or they were able to complete its 4 tasks successfully in a dry lab, without prior robotic experience. Each participant performed four consecutive RAPs using the avatera system on an ex-vivo porcine model. The primary endpoint of the present study was the change in the average time to complete the anastomosis from the first to the fourth attempt.

Results: Nine urologists and 8 residents were enrolled in this study. Each surgeon demonstrated a reduction in the time to complete anastomosis from the 1st to 4th attempt with an average of value of 4.41 ± 1.06 minutes (p = 0.003). The decrease in time was statistically significant in both urologists and residents subgroups (4.5 ± 1.41 minutes p = 0.049 and 4.33 ± 0.71 minutes p = 0.035 respectively).

Conclusions: The training on the ex-vivo model could lead, in only a few attempts, to a significant improvement in skills and in the required time of experienced-naïve surgeons to complete an RAP.

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Citations

Mao JZ, Agyei JO, Khan A, et al. Technologic Evolution of Navigation and Robotics in Spine Surgery: A Historical Perspective.
World Neurosurg. 2021; 145:159-67.
Stumpo V, Staartjes VE, Klukowska AM, et al. Global adoption of robotic technology into neurosurgical practice and research.
Neurosurg Rev. 2021; 44:2675-87.
Qureshi YA, Mohammadi B. Robotic oesophago-gastric cancer surgery. Ann R Coll Surg Engl. 2018;100:S23-30.
Thaly R, Shah K, Patel VR. Applications of robots in urology. J Robot Surg. 2007;1:3-17.
Atalla MA, Dovey Z, Kavoussi LR. Laparoscopic versus robotic pyeloplasty: man versus machine. Expert Rev Med Devices 2010;7:27-34.
Morales-Lopez RA, Perez-Marchan M, Perez Brayfield M. Current Concepts in Pediatric Robotic Assisted Pyeloplasty. Front Pediatrics 2019; 7:4.
Kearns JT, Gundeti MS. Pediatric robotic urologic surgery-2014. J Indian Assoc Pediatr Surg. 2014;19:123-8.
Howe A, Kozel Z, Palmer L. Robotic surgery in pediatric urology. Asian J Urol. 2017; 4:55-67.
Liatsikos E, Tsaturyan A, Kyriazis I, et al. Market potentials of robotic systems in medical science: analysis of the Avatera robotic
system. World J Urol. 2022; 40:283-9.
Sanchez Hurtado MA, Diaz-Guemes Martin-Portugues I, Correa Martin L, et al. Development and assessment of an ex-vivo bench
model aimed at laparoscopic ureteric reconstructive techniques. J Pediatr Urol. 2021; 17:753-5.
Wright TP. Factors affecting the cost of airplanes. J Aereonaut Sci 1936;3:122.
Tilindis J, Kleiza V. Learning curve parameter estimation beyond traditional statistic. Applied Mathematical Modelling 2017; 45:768-
Sung GT, Gill IS, Hsu TH. Robotic-assisted laparoscopic pyeloplasty: a pilot study. Urology. 1999; 53:1099-103.
Lorincz A, Knight CG, Kant AJ, et al. Totally minimally invasive robot-assisted unstented pyeloplasty using the Zeus Microwrist
Surgical System: an animal study. J Pediatr Surg. 2005; 40:418-22.
Dothan D, Raisin G, Jaber J, Kocherov S, Chertin B. Learning curve of robotic-assisted laparoscopic pyeloplasty (RALP) in children:
how to reach a level of excellence? J Robot Surg. 2021; 15:93-7.
Pakkasjärvi N, Krishnan N, Ripatti L, Anand S. Learning Curves in Pediatric Robot-Assisted Pyeloplasty: A Systematic Review. J Clin
Med. 2022; 11:6935.
Sorensen MD, Delostrinos C, Johnson MH, et al. Comparison of the learning curve and outcomes of robotic assisted pediatric pyeloplasty. J Urol 2011;185:S2517-22.
Spampinato G, Binet A, Fourcade L, et al. Comparison of the Learning Curve for Robot-Assisted Laparoscopic Pyeloplasty
Between Senior and Junior Surgeons. J Laparoendosc Adv Surg Tech A. 2021;31:478-483.
Evangelos Liatsikos, Department of Urology, University Hospital of Patras

Medical University of Vienna, Austria

How to Cite

Ayed, A., Kallidonis, P., Tatanis, V., Peteinaris, A., Liatsikos, E., & Natchagande, G. (2024). The learning curve for robotic-assisted pyeloplasty in urologists with no prior robotic experience using an <i>ex vivo</i> model: A prospective, controlled study. Archivio Italiano Di Urologia E Andrologia, 96(4). https://doi.org/10.4081/aiua.2024.12990