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Niznik T, Grossen A, Shi H, Stephens M, Herren C, Desai VR. Learning Curve in Robotic Stereoelectroencephalography: Single Platform Experience. World Neurosurg 2024; 182:e442-e452. [PMID: 38030071 DOI: 10.1016/j.wneu.2023.11.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND Learning curve, training, and cost impede widespread implementation of new technology. Neurosurgical robotic technology introduces challenges to visuospatial reasoning and requires the acquisition of new fine motor skills. Studies detailing operative workflow, learning curve, and patient outcomes are needed to describe the utility and cost-effectiveness of new robotic technology. METHODS A retrospective analysis was performed of pediatric patients who underwent robotic stereoelectroencephalography (sEEG) with the Medtronic Stealth Autoguide. Workflow, total operative time, and time per electrode were evaluated alongside target accuracy assessed via error measurements and root sum square. Patient demographics and clinical outcomes related to sEEG were also assessed. RESULTS Robot-assisted sEEG was performed in 12 pediatric patients. Comparison of cases over time demonstrated a mean operative time of 363.3 ± 109.5 minutes for the first 6 cases and 256.3 ± 59.1 minutes for the second 6 cases, with reduced operative time per electrode (P = 0.037). Mean entry point error, target point error, and depth point error were 1.82 ± 0.77 mm, 2.26 ± 0.71 mm, and 1.27 ± 0.53 mm, respectively, with mean root sum square of 3.23 ± 0.97 mm. Error measurements between magnetic resonance imaging and computed tomography angiography found computed tomography angiography to be more accurate with significant differences in mean entry point error (P = 0.043) and mean target point error (P = 0.035). The epileptogenic zone was identified in 11 patients, with therapeutic surgeries following in 9 patients, of whom 78% achieved an Engel class I. CONCLUSIONS This study demonstrated institutional workflow evolution and learning curve for the Autoguide in pediatric sEEG, resulting in reduced operative times and increased accuracy over a small number of cases. The platform may seamlessly and quickly be incorporated into clinical practice, and the provided workflow can facilitate a smooth transition.
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Affiliation(s)
- Taylor Niznik
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA; Department of Neurosurgery, Section of Pediatric Neurosurgery, Oklahoma Children's Hospital, University of Oklahoma School of Medicine, Oklahoma City, Oklahoma, USA
| | - Audrey Grossen
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA; Department of Neurosurgery, Section of Pediatric Neurosurgery, Oklahoma Children's Hospital, University of Oklahoma School of Medicine, Oklahoma City, Oklahoma, USA
| | - Helen Shi
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA; Department of Neurosurgery, Section of Pediatric Neurosurgery, Oklahoma Children's Hospital, University of Oklahoma School of Medicine, Oklahoma City, Oklahoma, USA
| | - Mark Stephens
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA; Department of Neurosurgery, Section of Pediatric Neurosurgery, Oklahoma Children's Hospital, University of Oklahoma School of Medicine, Oklahoma City, Oklahoma, USA
| | - Cherie Herren
- Department of Neurology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Virendra R Desai
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA; Department of Neurosurgery, Section of Pediatric Neurosurgery, Oklahoma Children's Hospital, University of Oklahoma School of Medicine, Oklahoma City, Oklahoma, USA.
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