Stratifying Parkinson's Patients With STN-DBS Into High-Frequency or 60 Hz-Frequency Modulation Using a Computational Model.

Objective: High frequency stimulation (HFS) of the subthalamic nucleus (STN) is a well-established therapy for Parkinson's disease (PD), particularly the cardinal motor symptoms and levodopa induced motor complications. Recent studies have suggested the possible role of 60 Hz stimulation in STN-deep brain stimulation (DBS) for patients with gait disorder. The objective of this study was to develop a computational model, which stratifies patients a priori based on symptomatology into different frequency settings (i.e., high frequency or 60 Hz).

Methods: We retrospectively analyzed preoperative MDS-Unified Parkinson's Disease Rating Scale III scores (32 indicators) collected from 20 PD patients implanted with STN-DBS at Mount Sinai Medical Center on either 60 Hz stimulation (ten patients) or HFS (130-185 Hz) (ten patients) for an average of 12 months. Predictive models using the Random Forest classification algorithm were built to associate patient/disease characteristics at surgery to the stimulation frequency. These models were evaluated objectively using leave-one-out cross-validation approach.

Results: The computational models produced, stratified patients into 60 Hz or HFS (130-185 Hz) with 95% accuracy. The best models relied on two or three predictors out of the 32 analyzed for classification. Across all predictors, gait and rest tremor of the right hand were consistently the most important.

Conclusions: Computational models were developed using preoperative clinical indicators in PD patients treated with STN-DBS. These models were able to accurately stratify PD patients into 60 Hz stimulation or HFS (130-185 Hz) groups a priori, offering a unique potential to enhance the utilization of this therapy based on clinical subtypes.