Multiple input algorithm-guided Deep Brain stimulation-programming for Parkinson's disease patients

Area of Science:

• Neuromodulation
• Neuroscience
• Biomedical Engineering

Background:

• Deep Brain Stimulation (DBS) for Parkinson's disease (PD) has advanced, offering more programming options but increasing complexity.
• Optimizing DBS settings is crucial for patient benefit but can be time-consuming and burdensome for clinicians.

Purpose of the Study:

• To evaluate the feasibility of a semi-automatic algorithm-guided-programming (AgP) approach for optimizing DBS settings in PD patients.
• To compare the acute clinical effectiveness of AgP-guided DBS settings with standard of care (SoC) programming.

Main Methods:

• A randomized, crossover, double-blind study in 10 PD subjects with directional DBS systems.
• AgP iteratively assessed weighted combinations of sensor and clinician-reported symptom responses to suggested DBS settings.
• AgP settings were compared to SoC settings and a no-therapy condition.

Main Results:

• Both AgP and SoC DBS settings significantly improved total Unified Parkinson's Disease Rating Scale III scores compared to therapy absence (p=0.002).
• AgP tested an average of 37 settings per subject, converging in approximately 1.7 hours.
• Despite similar clinical outcomes, AgP and SoC programming resulted in substantially different stimulation settings.

Conclusions:

• The algorithm-guided-programming (AgP) approach is a feasible alternative for DBS programming in Parkinson's disease.
• AgP demonstrates comparable acute clinical effectiveness to standard programming methods.
• This approach represents a significant step towards developing future closed-loop DBS optimization systems.