You have accessJournal of UrologyTechnology & Instruments: Surgical Education & Skills Assessment II1 Apr 2014MP14-06 SUTURING SIMULATION VERSUS ROBOTIC PRACTICE: AN EVALUATION OF PERFORMANCE IMPROVEMENT, CONTENT, AND FACE VALIDITY IN NOVICE OPERATORS. Samuel M. Lindner, Michael J. Amirian, Edouard J. Trabulsi, and Costas D. Lallas Samuel M. LindnerSamuel M. Lindner More articles by this author , Michael J. AmirianMichael J. Amirian More articles by this author , Edouard J. TrabulsiEdouard J. Trabulsi More articles by this author , and Costas D. LallasCostas D. Lallas More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.634AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Virtual reality (VR) simulation has been advocated for improving robotic surgery skills for novice trainees. In the absence of dedicated suturing simulation, however, VR simulation has not translated to increases in real-world suturing performance, and the incremental benefit of VR simulation over dry lab practice is unclear. The aim of this study is to evaluate the effectiveness of VR simulation versus dry lab suturing practice at improving suturing performance in robotic surgery. METHODS Nineteen novice participants with no prior robotic suturing experience were randomized to two groups (VR simulation and dry lab). Each group underwent baseline suturing evaluation using a validated, objective suture score method. Groups were next assigned to train on the Simbionix™ Suturing Module (SSM) or undertake suturing practice using the da Vinci Surgical System in a dry lab. At the conclusion of training or practice, final suturing performance was evaluated using the objective suture scoring method. Participants in the VR simulation group were surveyed at the conclusion of the final suturing evaluation to assess the face and content validity of the SSM. RESULTS Both groups experienced significant improvement after training (VR simulation group p=0.0078; dry lab group p=0.0039). There was no significant difference in improvement between the two groups after undergoing training with either SSM use or suturing practice using the robotic surgical system in a dry lab. Improvement in composite timing scores were 123 and 172 in the VR simulation and dry lab test groups, respectively (p=0.36). In the validity assessment, participants rejected the face validity of the SSM with regard to simulated tissue behavior, and confirmed face validity for clutching, needle driving, depth/spatial relationship, and visual appearance. The participants confirmed content validity of the SSM in all categories, finding simulated tissue behavior, clutching, needle driving, depth/spatial relationship, and visual appearance useful and relevant for training. CONCLUSIONS In this sample of novice operators, there was no significant advantage to training with VR simulation using the SSM over dry lab training in improving suturing performance. While users of the SSM did not find all aspects of the simulator realistic in their face validity assessment, they found it useful and relevant as a training tool for improving suturing performance. Performance Data Summary Dry Lab (n=9) VR Simulation (n=10) Mean (SD) Median [Min, Max] Mean (SD) Median [Min, Max] p-value Timing Score Initial 235.22 (119.01) 265 [0, 353] 254.3 (144.75) 318.5 [0, 401] 0.5928 Final 407.67 (74.22) 440 [263, 476] 377.5 (51.52) 375.5 [302, 453] Difference 172.44 (114.38) 154 [18, 360] 123.20 (115.44) 98 [-34, 302] 0.3602 Time (Sec) Initial 355.67 (120.96) 313 [247, 600] 337.7 (146.6) 270 [195, 600] Final 185.22 (70.1) 159 [123, 327] 212.6 (53.13) 214.5 [143, 291] Difference -170.44 (117.30) -142 [-350, -15] -125.10 (117.38) -101.50 [-309, 27] 0.4023 Accuracy (mm) Initial 1.38 (1.3) 1 [0, 4] 2.11 (1.27) 2 [0, 4] Final 0.75 (1.16) 0 [0, 3] 0.78 (1.09) 0 [0, 3] Difference -0.63 (1.06) -0.50 [-2, 1] -1.33 (1.32) -1 [-3, 1] 0.2726 Gap (mm) Initial 0.13 (0.35) 0 [0, 1] 0.11 (0.33) 0 [0, 1] Final 0.50 (0.76) 0 [0, 2] 0.56 (1.13) 0 [0, 3] Difference 0.38 (0.92) 0 [-1, 2] 0.44 (1.24) 0 [-1, 3] 0.8734 Integrity Secure 3 5 3 3 Loose 3 3 6 6 Coming Apart 2 1 0 1 Incomplete 1 0 1 0 Total 9 9 10 10 0.0945 Square Knots Square 0 3 0 2 Not Square 8 6 9 8 Incomplete 1 0 1 0 Total 9 9 10 10 0.6825 © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e169 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Samuel M. Lindner More articles by this author Michael J. Amirian More articles by this author Edouard J. Trabulsi More articles by this author Costas D. Lallas More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...