CLOVER-DBS: Algorithm-Guided Deep Brain Stimulation-Programming Based on External Sensor Feedback Evaluated in a Prospective, Randomized, Crossover, Double-Blind, Two-Center Study

Article type: Research Article

Authors: Wenzel, Gregor R.^{a; *} | Roediger, Jan^{a; b} | Brücke, Christof^{a; 1} | Marcelino, Ana Luísa de A.^a | Gülke, Eileen^c | Pötter-Nerger, Monika^c | Scholtes, Heleen^d | Wynants, Kenny^{d; 2} | Juárez Paz, León M.^d | Kühn, Andrea A.^a

Affiliations: [a] Department of Neurology, Movement Disorders & Neuromodulation Section, Charité –University Medicine Berlin, Berlin, Germany | [b] Einstein Center for Neurosciences Berlin, Charité –University Medicine Berlin, Berlin, Germany | [c] Department of Neurology, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany | [d] Boston Scientific, Valencia, CA, USA

Correspondence: [*] Correspondence to: Gregor R. Wenzel, Department of Neurology, Movement Disorders & Neuromodulation Section, Charité – University Medicine Berlin, Charitéplatz 1, 10117 Berlin, Germany. Tel.: +49 30 450 660 569; E-mail: [email protected].

Note: [1] Present address: Department of Neurology, Medizinische Universität Wien, Spitalgasse 23, 1090 Vienna, Austria. E-mail: [email protected].

Note: [2] Present address: Nyxoah SA, Mont-Saint-Guibert, Belgium.

Abstract: Background:Recent technological advances in deep brain stimulation (DBS) (e.g., directional leads, multiple independent current sources) lead to increasing DBS-optimization burden. Techniques to streamline and facilitate programming could leverage these innovations. Objective:We evaluated clinical effectiveness of algorithm-guided DBS-programming based on wearable-sensor-feedback compared to standard-of-care DBS-settings in a prospective, randomized, crossover, double-blind study in two German DBS centers. Methods:For 23 Parkinson’s disease patients with clinically effective DBS, new algorithm-guided DBS-settings were determined and compared to previously established standard-of-care DBS-settings using UPDRS-III and motion-sensor-assessment. Clinical and imaging data with lead-localizations were analyzed to evaluate characteristics of algorithm-derived programming compared to standard-of-care. Six different versions of the algorithm were evaluated during the study and 10 subjects programmed with uniform algorithm-version were analyzed as a subgroup. Results:Algorithm-guided and standard-of-care DBS-settings effectively reduced motor symptoms compared to off-stimulation-state. UPDRS-III scores were reduced significantly more with standard-of-care settings as compared to algorithm-guided programming with heterogenous algorithm versions in the entire cohort. A subgroup with the latest algorithm version showed no significant differences in UPDRS-III achieved by the two programming-methods. Comparing active contacts in standard-of-care and algorithm-guided DBS-settings, contacts in the latter had larger location variability and were farther away from a literature-based optimal stimulation target. Conclusion:Algorithm-guided programming may be a reasonable approach to replace monopolar review, enable less trained health-professionals to achieve satisfactory DBS-programming results, or potentially reduce time needed for programming. Larger studies and further improvements of algorithm-guided programming are needed to confirm these results.

Keywords: Deep brain stimulation, Parkinson’s disease, algorithm, wearable device feedback, double-blind, subthalamic nucleus

DOI: 10.3233/JPD-202480

Journal: Journal of Parkinson's Disease, vol. 11, no. 4, pp. 1887-1899, 2021