Abstract
BACKGROUND
The pursuit of improved accuracy for localization and electrode implantation in deep brain stimulation (DBS) and stereoelectroencephalography (sEEG) has fostered an abundance of disparate surgical/stereotactic practices. Specific practices/technologies directly modify implantation accuracy; however, no study has described their respective influence in multivariable context.
OBJECTIVE
To synthesize the known literature to statistically quantify factors affecting implantation accuracy.
METHODS
A systematic review and meta-analysis was conducted to determine the inverse-variance weighted pooled mean target error (MTE) of implanted electrodes among patients undergoing DBS or sEEG. MTE was defined as Euclidean distance between planned and final electrode tip. Meta-regression identified moderators of MTE in a multivariable-adjusted model.
RESULTS
A total of 37 eligible studies were identified from a search return of 2,901 potential articles (2002-2018) – 27 DBS and 10 sEEG. Random-effects pooled MTE = 1.91 mm (95% CI: 1.7-2.1) for DBS and 2.34 mm (95% CI: 2.1-2.6) for sEEG. Meta-regression identified study year, robot use, frame/frameless technique, and intraoperative electrophysiologic testing (iEPT) as significant multivariable-adjusted moderators of MTE (P < .0001, R2 = 0.63). Study year was associated with a 0.92-mm MTE reduction over the 16-yr study period (P = .0035), and robot use with a 0.79-mm decrease (P = .0019). Frameless technique was associated with a mean 0.50-mm (95% CI: 0.17-0.84) increase, and iEPT use with a 0.45-mm (95% CI: 0.10-0.80) increase in MTE. Registration method, imaging type, intraoperative imaging, target, and demographics were not significantly associated with MTE on multivariable analysis.
CONCLUSION
Robot assistance for stereotactic electrode implantation is independently associated with improved accuracy and reduced target error. This remains true regardless of other procedural factors, including frame-based vs frameless technique.
Commentary: Robot-Assisted Stereotaxy Reduces Target Error: A Meta-Analysis and Meta-Regression of 6056 Trajectories