Predictive Validity of a Training Protocol Using a Robotic Surgery Simulator
Department
Statistics and Analytical Sciences
Document Type
Article
Publication Date
1-2014
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 ([alpha] = 0.05).
Results: Fourteen novice surgeons completed the study-spending 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, -1.7 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.
Journal Title
Female Pelvic Medicine & Reconstructive Surgery
Journal ISSN
2151-8378
Volume
20
Issue
1
First Page
48
Last Page
51
Digital Object Identifier (DOI)
10.1097/SPV.0000000000000045