Comparing Expert and Trainee Levels of Surgical Technical Skills

Background:

The use of virtual reality surgical simulators has been explored as a means of providing objective assessments in surgery. The enormous data generated by simulators can provide an objective and novel insight into the technical composites of expertise.

The objective of this work to form a data repository from expert and trainee levels of surgical performance data using the NeuroVR surgical simulation platform with haptic feedback, which will be used to assess face, content and construct validity of the simulated surgical tasks and develop intelligent systems in future studies.

Specific Aims: 1) Construct a data repository including expert and trainee levels of surgical performance data on virtual reality simulation. 2) Outline the face and content validity of the simulated tasks.

Design: 50 participants from a single university were recruited in this consecutive retrospective case series study.

Setting: Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University Participants: Neurosurgeons (14), neurosurgical fellows and neurosurgical residents (24) from Montreal Neurological Institute and Hospital along with medical studies (12).

Task: Complete removal of a simulated tumor with minimal bleeding and damage to surrounding healthy brain using two surgical instruments of the NeuroVR (CAE Healthcare) surgical simulator.

Intervention: A single 28-minute simulation session, including 5 practice scenarios (3 minutes per task) and one complex realistic scenario (13 minutes). Pre- and post-questionnaires to obtain participant demographics and assess face and content validity of the simulated tasks.

Main Outcomes and Measures:

Primary outcome are surgical performance metrics recorded during the simulated tasks including.

1. Simulated bipolar and simulated ultrasonic aspirator separation distance Mean instrument tip separation distance in mm
2. Simulated bipolar force application Mean bipolar force application in Newtons
3. Simulated ultrasonic aspirator force application Mean ultrasonic aspirator force application in Newtons

Secondary outcomes include:

1.Rating of face validity of simulation

Questionnaire using 5 point Likert scale will assess face validity

2 Rating of content validity of simulation

Questionnaire using 5 point Likert scale will assess content validity

.

Detailed Description:

Background Technological means are being increasingly used in education; however surgical technical skills training is still heavily dependent on expert-trainee relationship. This apprenticeship model faces limitations with the availability of expert surgeons, patient-safety concerns, lack of objectivity, standardization, and quantitation. Surgical interventions carry substantive risks to patients and high costs to health care systems. Consequently, surgical education transforms into more competency-based frameworks by implementing technological means to increase data collection, enable quantification, and objective analysis. Virtual reality simulators have been developed to provide standardized realistic simulated operative environments with the detailed performance data recorded, enabling to explore the composites of technical expertise, and offering new methodologies for surgical training.

Rationale A technically challenging operation in neurosurgery involves the subpial resection of brain tumors adjacent to critical structures. Two simulated tumor resection tasks were developed previously to simulate this procedure using the NeuroVR platform with haptic feedback. The face, content and construct validity of these simulations and methodologies to assess surgical performance in these tasks qualitatively and quantitatively are yet to be explored.

Research Objectives To form a data repository of simulated surgical performance across different expertise levels to allow future studies and to explore face, content and construct validity to develop expert benchmarks and intelligent systems to provide feedback to learners.

Participants do not know about the performance metrics recorded, only that they will be practicing technical skills used in neurosurgery for subpial tumor resection procedures.

Primary Purpose:

Official Title: Comparing Expert and Trainee Levels of Surgical Technical Skills in Virtual Reality Surgical Simulation - A Case Series Study Study Start Date: March 1, 2015

Study Completion Date:

May 31, 2016

Arms and Interventions

Arm Intervention/treatment Single arm, consecutive case series study

All participants performed two simulated tasks: a practice subpial tumor resection five times and a complex brain tumor resection once. Participants were instructed to remove the tumor completely while minimizing injury to the adjacent critical tissues and bleeding. Practice and complex tumor resection tasks were to be completed within three- and 13-minutes time limits, respectively. Participants received no feedback during or after the trials. Performance data was recorded at 0.02 second intervals by the NeuroVR platform.

Contacts and Locations Locations Canada, Quebec Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4 Quebec, Canada Investigators Principal Investigator: Rolando F. Del Maestro, MD PhD McGill University

More Information Responsible Party: Rolando F. Del Maestro, William Feindel Professor Emeritus in Neuro-Oncology, McGill University

ClinicalTrials.gov Identifier:

Human Subjects Ethics Review Board Status: Approved Ethics Review Board Project Number: 2010-270 NEU-09-042 Studies a U.S. FDA-regulated Drug Product: No Studies a U.S. FDA-regulated Device Product: No

Data Analysis Plan This study aims to form a data repository to enable future studies to explore the composites of surgical expertise in virtual reality surgical simulation performance by both qualitative and quantitative analyses. Participant rating of the simulated tasks will be used to assess face and content validity of the simulated tasks. A median score of \>=3 is deemed sufficient for validation.

References:

1. Stulberg JJ, Huang R, Kreutzer L, Ban K, Champagne BJ, Steele SE, et al. Association Between Surgeon Technical Skills and Patient Outcomes. JAMA Surgery. 2020.DOI: 10.1001/jamasurg.2020.3007.
2. Delorme S, Laroche D, DiRaddo R, Del Maestro RF. NeuroTouch: A Physics-Based Virtual Simulator for Cranial Microneurosurgery Training. Operative Neurosurgery. 2012;71(suppl_1):ons32-ons42.DOI: 10.1227/NEU.0b013e318249c744.
3. Gélinas-Phaneuf N, Del Maestro RF. Surgical Expertise in Neurosurgery: Integrating Theory Into Practice. Neurosurgery. 2013;73(suppl_1):S30-S8.DOI: 10.1227/NEU.0000000000000115.
4. Anderson O, Davis R, Hanna GB, Vincent CA. Surgical adverse events: a systematic review. The American Journal of Surgery. 2013;206(2):253-62.DOI: 10.1016/j.amjsurg.2012.11.009.
5. Brightwell A, Grant J. Competency-based training: who benefits? Postgraduate Medical Journal. 2013;89(1048):107.DOI: 10.1136/postgradmedj-2012-130881.
6. Sabbagh AJ, Bajunaid KM, Alarifi N, Winkler-Schwartz A, Alsideiri G, Al-Zhrani G, et al. Roadmap for Developing Complex Virtual Reality Simulation Scenarios: The Subpial Neurosurgical Tumor Resection Model. World Neurosurgery. 2020.DOI: https://doi.org/10.1016/j.wneu.2020.03.187.

Eligibility Criteria:
Inclusion Criteria:

Neurosurgeons

Neurosurgical fellows

Neurosurgical residents

Medical students

Exclusion Criteria:

Experience with previous similar brain tumor simulation