Introduction: Deep brain stimulation (DBS) is an established treatment for Parkinson's disease (PD). Clinicians face various challenges in adjusting stimulation parameters and configurations in clinical DBS settin...
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Introduction: Deep brain stimulation (DBS) is an established treatment for Parkinson's disease (PD). Clinicians face various challenges in adjusting stimulation parameters and configurations in clinical DBS settings owing to inexperience, time constraints, and recent advances in DBS technology that have expanded the number of possible contact configurations. We aimed to assess the efficacy of a closed-loop algorithm (CLA) for the DBS-programming method using external motion sensor-based motor assessments in patients with PD. Methods: In this randomized, double-blind, crossover study, we enrolled 12 patients who underwent eight-ring-contact DBS lead implantations bilaterally in the subthalamic nucleus. The DBS settings of the participants were programmed using a standard of care (SOC) and CLA method. The clinical effects of both programming methods were assessed in a randomized crossover fashion. The outcomes were evaluated using the Unified Parkinson's Disease Scale part III (UPDRS-III) and sensor-based scores for baseline (medication-off/stimulation-off) and both programming methods. The number of programming steps required for each programming method was also recorded. Results: The UPDRS-III scores and sensor-based scores were significantly improved by SOC and CLA settings compared to the baseline. No statistical difference was observed between SOC and CLA. The programming steps were significantly reduced in the CLA settings compared to those in the SOC. No serious adverse events were observed. Conclusion: CLA can optimize DBS settings prospectively with similar therapeutic benefits as that of the SOC and reduce the number of programming steps. Automated optimization of DBS settings would reduce the burden of programming for both clinicians and patients.
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