Training Performance Assessment for Intracranial Aneurysm Clipping Surgery Using a Patient-Specific Mixed-Reality Simulator: A Learning Curve Study

Abstract

BACKGROUND AND OBJECTIVES:The value of simulation-based training in medicine and surgery has been widely demonstrated. This study investigates the introduction and use of a new mixed-reality neurosurgical simulator in aneurysm clipping surgery, focusing on the learning curve and performance improvement.METHODS:Five true-scale craniotomy head models replicating patient-specific neuroanatomy, along with a mixed-reality simulator, a neurosurgical microscope, and a set of microsurgical instruments and clips, were used in the operation theater to simulate aneurysm microsurgery. Six neurosurgical residents participated in five video-recorded simulation sessions over 4 months. Complementary learning modalities were implemented between sessions. Thereafter, three blinded analysts reported on residents' use of the microscope, quality of manipulation, aneurysm occlusion, clipping techniques, and aneurysm rupture. Data were also captured regarding training time and clipping attempts.RESULTS:Over the course of training, clipping time and number of clipping attempts decreased significantly (P =.018, P =.032) and the microscopic skills improved (P =.027). Quality of manipulation and aneurysm occlusion scoring improved initially although the trend was interrupted because the spacing between sessions increased. Significant differences in clipping time and attempts were observed between the most and least challenging patient models (P =.005, P =.0125). The least challenging models presented higher rates of occlusion based on indocyanine green angiography evaluation from the simulator.CONCLUSION:The intracranial aneurysm clipping learning curve can be improved by implementing a new mixed-reality simulator in dedicated training programs. The simulator and the models enable comprehensive training under the guidance of a mentor.