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

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.

Linking profiles of pathway activation with clinical motor improvements – A retrospective computational study

“…As a result, 15 patients for the training (Berlin) and 19 for the test (Würzburg) cohort were admitted. Although the first cohort contained fewer patients, it provided a total of 30 datapoints: two stimulation protocols were documented on the same day for each patient as a part of another study that compared the standard of care with algorithm-guided DBS-programming, see ( Wenzel et al, 2021 ) for further details. Later, this aspect was used to derive the correlation model.…”

Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury

“…This study was performed in patients with octopolar leads, but this novel DBS programming method may enable automatic programming even in more complex DBS leads, such as directional leads. Indeed, AI may upgrade algorithms that enable automatic programming even in more complex DBS leads, such as in a directional lead (Wenzel et al, 2021).…”

Directional Deep Brain Stimulation in the Treatment of Parkinson's Disease

“…42 Nonetheless, the long-term effects of dDBS compared with conventional leads remain unknown; its therapeutic agency in treating axial symptoms of PD warrants further study; and its applicability with other programming approaches, such as image-guided DBS and artificial intelligence-based programming paradigms, has not been fully investigated. 43,44…”