High fidelity simulation of the endoscopic transsphenoidal approach: Validation of the UpSurgeOn TNS Box

高保真内镜经蝶入路模拟:UpSurgeOn TNS Box 的验证

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Abstract

OBJECTIVE: Endoscopic endonasal transsphenoidal surgery is an established technique for the resection of sellar and suprasellar lesions. The approach is technically challenging and has a steep learning curve. Simulation is a growing training tool, allowing the acquisition of technical skills pre-clinically and potentially resulting in a shorter clinical learning curve. We sought validation of the UpSurgeOn Transsphenoidal (TNS) Box for the endoscopic endonasal transsphenoidal approach to the pituitary fossa. METHODS: Novice, intermediate and expert neurosurgeons were recruited from multiple centres. Participants were asked to perform a sphenoidotomy using the TNS model. Face and content validity were evaluated using a post-task questionnaire. Construct validity was assessed through post-hoc blinded scoring of operative videos using a Modified Objective Structured Assessment of Technical Skills (mOSAT) and a Task-Specific Technical Skill scoring system. RESULTS: Fifteen participants were recruited of which n = 10 (66.6%) were novices and n = 5 (33.3%) were intermediate and expert neurosurgeons. Three intermediate and experts (60%) agreed that the model was realistic. All intermediate and experts (n = 5) strongly agreed or agreed that the TNS model was useful for teaching the endonasal transsphenoidal approach to the pituitary fossa. The consensus-derived mOSAT score was 16/30 (IQR 14-16.75) for novices and 29/30 (IQR 27-29) for intermediate and experts (p < 0.001, Mann-Whitney U). The median Task-Specific Technical Skill score was 10/20 (IQR 8.25-13) for novices and 18/20 (IQR 17.75-19) for intermediate and experts (p < 0.001, Mann-Whitney U). Interrater reliability was 0.949 (CI 0.983-0.853) for OSATS and 0.945 (CI 0.981-0.842) for Task-Specific Technical Skills. Suggested improvements for the model included the addition of neuro-vascular anatomy and arachnoid mater to simulate bleeding vessels and CSF leak, respectively, as well as improvement in materials to reproduce the consistency closer to that of human tissue and bone. CONCLUSION: The TNS Box simulation model has demonstrated face, content, and construct validity as a simulator for the endoscopic endonasal transsphenoidal approach. With the steep learning curve associated with endoscopic approaches, this simulation model has the potential as a valuable training tool in neurosurgery with further improvements including advancing simulation materials, dynamic models (e.g., with blood flow) and synergy with complementary technologies (e.g., artificial intelligence and augmented reality).

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