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.
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.
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.
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.
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.