RESUMO
INTRODUCTION: We sought to explore whether electrode visualization tools (EVT) can accurately predict the selection of optimal Deep Brain Stimulation (DBS) electrode contacts. METHODS: Twelve patients with Parkinson's disease (PD) undergoing STN-DBS at The Ohio State University were enrolled in a prospective analysis to evaluate the accuracy of EVT-based vs. standard DBS programming. EVTs were generated by the Surgical Information Sciences (SIS) system to develop a 3D model showing the implanted lead location relative to the STN. Then, imaging-based data were compared to the results of a standard monopolar review to evaluate concordance with clinical data and time spent selecting useable, non-useable, and borderline electrode contacts. RESULTS: A total of 18 DBS leads (n = 68 electrode contacts) were analyzed. The concordance between EVT and standard clinical programming expressed as the kappa coefficient was 0.65 (82.35% raw agreement) for non-useable, 0.52 for useable (64.71% raw agreement), and 0.52 for borderline (58.82% raw agreement). The average time spent determining whether an electrode contact was useable, non-useable, or borderline was 1.46 ± 0.76 min with EVT vs. 61.25 ± 17.47 with standard monopolar review. Eight different categories of side effects were identified, with facial pulling and speech difficulties being observed with the most frequency. The type of side effect observed was accurately predicted using EVT 90% of the time. CONCLUSIONS: This study demonstrates that next-generation EVT-based programming can be implemented into STN-DBS programming workflows with a considerable saving of time and effort spent in testing combinations of stimulation settings, particularly for the identification of non-useable electrode contacts.