Predictive validity of a training protocol using a robotic surgery simulator

Patrick Culligan, Emil Gurshumov, Christa Lewis, Jennifer Priestley, Jodie Komar, Charbel Salamon

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

Background: Robotic surgery simulation may provide a way for surgeons to acquire specific robotic surgical skills without practicing on live patients. Methods: Five robotic surgery experts performed 10 simulator skills to the best of their ability, and thus, established expert benchmarks for all parameters of these skills. A group of credentialed gynecologic surgeons naive to robotics practiced the simulator skills until they were able to perform each one as well as our experts. Within a week of doing so, they completed robotic pig laboratory training, after which they performed supracervical hysterectomies as their first-ever live human robotic surgery. Time, blood loss, and blinded assessments of surgical skill were compared among the experts, novices, and a group of control surgeons who had robotic privileges but no simulator exposure. Sample size estimates called for 11 robotic novices to achieve 90% power to detect a 1 SD difference between operative times of experts and novices (α = 0.05). Results: Fourteen novice surgeons completed the studyVspending an average of 20 hours (range, 9.7-38.2 hours) in the simulation laboratory to pass the expert protocol. The mean operative times for the expert and novices were 20.2 (2.3) and 21.7 (3.3) minutes, respectively (P = 0.12; 95% confidence interval, j1.7 to 4.7), whereas the mean time for control surgeons was 30.9 (0.6) minutes (P < 0.0001; 95% confidence interval, 6.3-12.3). Comparisons of estimated blood loss (EBL) and blinded video assessment of skill yielded similar differences between groups. Conclusions: Completing this protocol of robotic simulator skills translated to expert-level surgical times during live human surgery. As such, we have established predictive validity of this protocol.

Original languageEnglish (US)
Pages (from-to)48-51
Number of pages4
JournalFemale Pelvic Medicine and Reconstructive Surgery
Volume20
Issue number1
DOIs
StatePublished - Jan 1 2014
Externally publishedYes

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Robotics
Operative Time
Confidence Intervals
Benchmarking
Hysterectomy
Sample Size
Swine
Surgeons
Control Groups

All Science Journal Classification (ASJC) codes

  • Surgery
  • Obstetrics and Gynecology
  • Urology

Cite this

Culligan, Patrick ; Gurshumov, Emil ; Lewis, Christa ; Priestley, Jennifer ; Komar, Jodie ; Salamon, Charbel. / Predictive validity of a training protocol using a robotic surgery simulator. In: Female Pelvic Medicine and Reconstructive Surgery. 2014 ; Vol. 20, No. 1. pp. 48-51.
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Predictive validity of a training protocol using a robotic surgery simulator. / Culligan, Patrick; Gurshumov, Emil; Lewis, Christa; Priestley, Jennifer; Komar, Jodie; Salamon, Charbel.

In: Female Pelvic Medicine and Reconstructive Surgery, Vol. 20, No. 1, 01.01.2014, p. 48-51.

Research output: Contribution to journalArticle

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