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Computational Modeling of 60 Hz Subthalamic Nucleus Deep Brain Stimulation for Gait Disorder in Parkinson's Disease

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04184791
Recruitment Status : Recruiting
First Posted : December 4, 2019
Last Update Posted : August 20, 2020
Sponsor:
Collaborators:
National Institute of Neurological Disorders and Stroke (NINDS)
The University of Tennessee, Knoxville
Information provided by (Responsible Party):
Ritesh Ramdhani, MD, Northwell Health

Brief Summary:

The objective of this study is to further the understanding and application of 60Hz subthalamic deep brain stimulation (STN-DBS) in Parkinson's patients with gait disorder. The investigators will achieve this through 2 study aims:

  1. Determine the impact of 60Hz subthalamic deep brain stimulation on gait kinematics using wearable sensors
  2. Develop machine learning models to predict optimal subthalamic deep brain stimulation frequency based on wearable sensors

Condition or disease Intervention/treatment Phase
Parkinson Disease Device: Deep Brain Stimulation Not Applicable

Detailed Description:
Gait disorder, which manifests as shuffling, reduction in speed, multistep turning, and/or freezing of gait (FOG), can arise later in the Parkinson's disease (PD) course and cause significant disability. Ultimately, patients are at risk for falls and can become socially isolated due to their mobility limitations. These symptoms tend not to respond to high frequency STN-DBS. However, lower frequency stimulation (60-80Hz) of the STN in treating gait disorder and/or freezing of gait has demonstrated benefit. This study potentially can expand knowledge of 60hz DBS while improving its utilization in combination with PD medications-enabling sustainable and possibly predictable therapeutic benefit.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 30 participants
Allocation: Randomized
Intervention Model: Single Group Assignment
Masking: Double (Participant, Outcomes Assessor)
Primary Purpose: Treatment
Official Title: Computational Modeling of 60 Hz Subthalamic Nucleus Deep Brain Stimulation for Gait Disorder in Parkinson's Disease
Actual Study Start Date : January 15, 2020
Estimated Primary Completion Date : August 31, 2021
Estimated Study Completion Date : August 31, 2021

Resource links provided by the National Library of Medicine


Arm Intervention/treatment
Experimental: Deep Brain Stimulation(DBS) OFF Medication
Subthalamic-DBS in the Levodopa OFF state.
Device: Deep Brain Stimulation
Each DBS electrode contact will be reprogrammed in 60hz and High Frequency Stimulation (180hz) in the Levodopa ON (medicated) and OFF (unmedicated) conditions.

Experimental: Deep Brain Stimulation(DBS) ON Medication
Subthalamic-DBS in the Levodopa ON state.
Device: Deep Brain Stimulation
Each DBS electrode contact will be reprogrammed in 60hz and High Frequency Stimulation (180hz) in the Levodopa ON (medicated) and OFF (unmedicated) conditions.




Primary Outcome Measures :
  1. Change in Gait and Balance using Wearable Sensors [ Time Frame: 1-2 minute period with each stimulation condition ]
    Inertial Sensors will be used to quantify gait metrics (postural sway, gait cycle, circumduction) as participants conduct two 7meter walking trials for each stimulation condition ( 60Hz frequency or High Frequency) across DBS electrode pairs in both the medicated and unmedicated states.

  2. Accuracy of Predicting Gait Response to 60hz with Machine Learning [ Time Frame: 2 years ]
    Regression models will be created using non-linear regression analysis based on random forest (RF) classifier on the raw gait sensor data acquired from the medicated and unmedicated states.

  3. Accuracy of Predicting Best Stimulation Frequency (60hz vs. High Frequency) with Machine Learning [ Time Frame: 2 years ]
    Regression models will be created using non-linear regression analysis based on random forest (RF) classifier on all raw sensor data (gait and balance, tremor, and speed of limb movements) acquired in the medicated and unmedicated states.


Secondary Outcome Measures :
  1. Change in Hand Tremor Severity [ Time Frame: 1 minute test session for each stimulation condition ]
    The difference in tremor (e.g. rest, postural, kinetic) severity will be measured with an Inertial sensor for each DBS electrode stimulation pair (60hz or High Frequency) in both the medicated and unmedicated states.

  2. Change in Speed of Limb Movements [ Time Frame: 1 minute test session for each stimulation condition ]
    The difference in the speed of limb movements (e.g. finger taps, hand grasps, wrist rotation, leg lifts, toe taps) will be measured with an Inertial sensor for each DBS electrode stimulation pair (60hz or High Frequency) in both the medicated and unmedicated states.



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Ages Eligible for Study:   21 Years to 80 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  1. Male or female, aged 21-80
  2. Patients diagnosed with Parkinson's disease (PD)
  3. PD subjects who have bilateral STN-DBS (greater than 6 months) or in the preoperative stage of being implanted with bilateral STN-DBS
  4. Have underlying gait disorder
  5. Currently treated with oral levodopa therapy
  6. Willingness to comply with all study procedures

Exclusion Criteria:

  1. Non-English speaking
  2. Cognitive deficits based on historical record that limit participant compliance with study protocol
  3. Vestibular disorder or musculoskeletal problems affecting gait or balance

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04184791


Contacts
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Contact: Toni Fitzpatrick 516-562-2685 tfitzpatrick@northwell.edu

Locations
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United States, New York
Northwell Health Recruiting
Great Neck, New York, United States, 11021
Contact: Toni Fitzpatrick         
Principal Investigator: Ritesh A Ramdhani, MD         
Sponsors and Collaborators
Northwell Health
National Institute of Neurological Disorders and Stroke (NINDS)
The University of Tennessee, Knoxville
Investigators
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Principal Investigator: Ritesh Ramdhani, MD Northwell Health
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Responsible Party: Ritesh Ramdhani, MD, Assistant Professor of Neurology, Northwell Health
ClinicalTrials.gov Identifier: NCT04184791    
Other Study ID Numbers: 19-0217
R21NS111301 ( U.S. NIH Grant/Contract )
First Posted: December 4, 2019    Key Record Dates
Last Update Posted: August 20, 2020
Last Verified: August 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: Yes
Product Manufactured in and Exported from the U.S.: Yes
Keywords provided by Ritesh Ramdhani, MD, Northwell Health:
Deep Brain Stimulation
Parkinson's Disease
Wearable Sensors
Additional relevant MeSH terms:
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Parkinson Disease
Parkinsonian Disorders
Basal Ganglia Diseases
Brain Diseases
Central Nervous System Diseases
Nervous System Diseases
Movement Disorders
Neurodegenerative Diseases