Predictive Validity of a Training Protocol Using a Robotic Surgery Simulator
originally published in Female Pelvic Medicine and Reconstructive Surgery
Patrick Culligan, MD; Emil Gurshumov, MD; Christa Lewis, DO; Jennifer Priestley, PhD; Jodie Koma, BSN; Charbel Salamon, MD
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 gynecologicsurgeons naive to robotics practiced the simulator skills until theywere able to perform each one as well as our experts. Within a weekof doing so, they completed robotic pig laboratory training, after whichthey performed supracervical hysterectomies as their first-ever livehuman robotic surgery. Time, blood loss, and blinded assessments ofsurgical skill were compared among the experts, novices, and a groupof control surgeons who had robotic privileges but no simulator expo-sure. Sample size estimates called for 11 robotic novices to achieve90% power to detect a 1 SD difference between operative times ofexperts and novices (> = 0.05).
Results: Fourteen novice surgeons completed the studyVspendingan average of 20 hours (range, 9.7Y38.2 hours) in the simulation lab-oratory to pass the expert protocol. The mean operative times for the ex-pert 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 timefor control surgeons was 30.9 (0.6) minutes (P G 0.0001; 95% confi-dence interval, 6.3Y12.3). Comparisons of estimated blood loss (EBL) andblinded video assessment of skill yielded similar differences betweengroups.
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.