A Multivariate Approach to Quantifying Risk Factors Impacting Stereotactic Robotic-Guided Stereoelectroencephalography.

Abstract

Background and objectives: Stereoelectroencephalography (SEEG) is an important method for invasive monitoring to establish surgical candidacy in approximately half of refractory epilepsy patients. Identifying factors affecting lead placement can mitigate potential surgical risks. This study applies multivariate analyses to identify perioperative factors affecting stereotactic electrode placement.

Methods: We collected registration and accuracy data for consecutive patients undergoing SEEG implantation between May 2022 and November 2023. Stereotactic robotic guidance, using intraoperative imaging and a novel frame-based fiducial, was used for planning and SEEG implantation. Entry-point (EE), target-point (TE), and angular errors were measured, and statistical univariate and multivariate linear regression analyses were performed.

Results: Twenty-seven refractory epilepsy patients (aged 15-57 years) undergoing SEEG were reviewed. Sixteen patients had unilateral implantation (10 left-sided, 6 right-sided); 11 patients underwent bilateral implantation. The mean number of electrodes per patient was 18 (SD = 3) with an average registration mean error of 0.768 mm (SD = 0.108). Overall, 486 electrodes were reviewed. Univariate analysis showed significant correlations of lead error with skull thickness (EE: P = .003; TE: P = .012); entry angle (EE: P < .001; TE: P < .001; angular error: P = .030); lead length (TE: P = .020); and order of electrode implantation (EE: P = .003; TE: P = .001). Three multiple linear regression models were used. All models featured predictors of implantation region (157 temporal, 241 frontal, 79 parietal, 9 occipital); skull thickness (mean = 5.80 mm, SD = 2.97 mm); order (range: 1-23); and entry angle in degrees (mean = 75.47, SD = 11.66). EE and TE error models additionally incorporated lead length (mean = 44.08 mm, SD = 13.90 mm) as a predictor. Implantation region and entry angle were significant predictors of error (P ≤ .05).

Conclusion: Our study identified 2 primary predictors of SEEG lead error, region of implantation and entry angle, with nonsignificant contributions from lead length or order of electrode placement. Future considerations for SEEG may consider varying regional approaches and angles for more optimal accuracy in lead placement.