Simulation training approaches in intracranial aneurysm surgery-a systematic review

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

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.

Enhancing microsurgical skills in neurosurgery residents of low-income countries: A comprehensive guide

Background

The main objectives of this paper are to outline the essential tools, instruments, and equipment needed to set up a functional microsurgery laboratory that is affordable for low-income hospitals and to identify cost-effective alternatives for acquiring microsurgical equipment, such as refurbished or donated instruments, collaborating with medical device manufacturers for discounted rates, or exploring local suppliers.

Methods

Step-by-step instructions were provided on setting up the microsurgery laboratory, including recommendations for the layout, ergonomic considerations, lighting, and sterilization processes while ensuring cost-effectiveness, as well as comprehensive training protocols and a curriculum specifically tailored to enhance microsurgical skills in neurosurgery residents.

Results

We explored cost-effective options for obtaining microsurgery simulators and utilizing open-source or low-cost virtual training platforms. We also included guidelines for regular equipment maintenance, instrument sterilization, and establishing protocols for infection control to ensure a safe and hygienic learning environment. To foster collaboration between low-income hospitals and external organizations or institutions that can provide support, resources, or mentorship, this paper shows strategies for networking, knowledge exchange, and establishing partnerships to enhance microsurgical training opportunities further. We evaluated the impact and effectiveness of the low-cost microsurgery laboratory by assessing the impact and effectiveness of the established microsurgery laboratory in improving the microsurgical skills of neurosurgery residents. About microsutures and microanastomosis, after three weeks of training, residents showed improvement in "surgical time" for ten separate simple stitches (30.06 vs. 8.65 min) and ten continuous single stitches (19.84 vs. 6.51 min). Similarly, there was an increase in the "good quality" of the stitches and the suture pattern from 36.36% to 63.63%.

Conclusion

By achieving these objectives, this guide aims to empower low-income hospitals and neurosurgery residents with the necessary resources and knowledge to establish and operate an affordable microsurgery laboratory, ultimately enhancing the quality of microsurgical training and patient care in low-income countries.

Dynamic Mixed-Reality Patient-Specific Aneurysm Clipping Simulation for Two Cases-A Feasibility Study

BACKGROUND AND OBJECTIVE

Intracranial aneurysm (IA) clipping is a complex neurosurgical procedure which demands advanced technology to minimize risks and maximize patient outcomes. This study aims to evaluate the feasibility of training patient-specific microsurgical clipping procedures using a mixed-reality physical neurosurgical simulator for unruptured IA.

METHODS

Two board-certified neurosurgeons were asked to simulate surgery in 2 patient-specific left-side unruptured middle cerebral artery-bifurcation IA models. The study was conducted in the operation theater under realistic conditions using a mixed-reality physical neurosurgical simulator. Time, procedural, and outcome-related information was collected. The participating neurosurgeons were encouraged to attempt all possible clipping strategies, even those deemed suboptimal, reporting the outcome of each strategy. Finally, to evaluate the feasibility and added value of integrating indocyanine green fluorescence angiography (ICG-FA) with the simulator, the ICG-FA videos for each clipping strategy were analyzed and compared with the reported clipping outcomes.

RESULTS

Between 4 and 8, different clipping strategies were applied per aneurysm model; the number of strategies was higher in Patient Model 1 (6.5 ± 1.5) (more complex aneurysm) than in Patient Model 2 (5.0 ± 1.0). The clipping strategies differed between surgeons. At most, 53.5 minutes were necessary to complete each training session, but more than double the time was spent on the more complex aneurysm. Up to 53.8% (Patient Model 1) and 50% (Patient Model 2) of the attempted strategies were discarded by the neurosurgeons during the simulation. Evaluation of aneurysm occlusion through ICG-FA was specific, although sensitivity was poor.

CONCLUSION

The present mixed-reality patient-specific simulator allows testing, anticipating, and discarding different aneurysm microsurgical clipping strategies regardless of the pathology complexity. Specific limitations should be considered regarding ICG-FA aneurysm inspection after clipping.