Deep brain stimulation (DBS) has tremendous potential to improve the lives of patients with a wide range of chronic illnesses. Good outcomes from DBS for Parkinson's disease (PD) are strongly correlated to accurate electrode placement and to careful post-operative selection of stimulation parameters (voltage, pulse width, frequency, active electrode contact(s), among others). Although DBS is beneficial for a variety of disorders, a persistent problem has been extensive, costly programming time after the electrode leads are implanted. This is largely because there are very few tools available to assist clinicians in this process, and as a result DBS programming can require a significant degree of experience and expertise, as well as a substantial amount of time for the clinician to search for optimal device settings. Over the last few years computational models have been developed to predict and visualize the effects of DBS based on the neuroanatomy of individual patients. Recently these models have shown promise for improving the efficiency of DBS programming, and have been incorporated into a clinical decision support system. The long-term goal of this research is to improve the lives of patients with neurological disease that are treated with DBS. The objective of this application is to prospec- tively test the use of DBS clinical decision support tool in post-operative clinical care. The central hypothesis is that the use of a DBS clinical decision support system for individual patient management will enable consider- able time savings and reduced burden on patients and caregivers. This hypothesis has been formulated from pilot studies that have shown dramatic decreases in DBS programming time compared to standard care for clinicians who used an iPad-based decision support system (99% time savings from over 4 hours to 2 minutes). The rationale for the proposed research is that computational models, clinical informatics, and mobile computing devices can be used to enable DBS management in a way that has never before been possible. Guided by strong preliminary data, this hypothesis will be tested in two specific aims: 1) Measure the effective- ness of DBS decision support system in an established PD clinic; 2) Measure the effectiveness of DBS deci- sion support system by home health nurses. Under the first aim we will compare programming time and clinical outcomes for patients managed using the clinical decision support system compared to standard care. Under the second aim we will assess the effects of the system on patient and caregiver strain when used by home health nurses. This approach is innovative because it provides an iPad-based clinical decision support applica- tion (app) to enable nurses and physicians to quickly focus on stimulation settings that are likely to be most effective. The proposed research is significant because it will provide powerful tools to the health care provid- ers who will be able to provide the greatest benefit for DBS patients. The knowledge gained could enable a future model for DBS management where care is provided in both clinical and home settings by skilled nurses who use expert systems for guidance.

Public Health Relevance

The proposed research is relevant to NIH's mission because of its potential to improve patient outcomes, reduce caregiver burden, and reduce the cost of care for Parkinson's disease patients treated with Deep Brain Stimulation (DBS). The knowledge from this study is relevant to public health because it could improve treatment for a range of neurological disorders including other movement disorders such as dystonia or essential tremor, as well as conditions such as depression, Tourette syndrome, obsessive compulsive disorder or Alzheimer's disease.

National Institute of Health (NIH)
National Institute of Nursing Research (NINR)
Research Project (R01)
Project #
Application #
Study Section
Nursing and Related Clinical Sciences Study Section (NRCS)
Program Officer
Matocha, Martha F
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Utah
Organized Research Units
Salt Lake City
United States
Zip Code
Anderson, Daria Nesterovich; Duffley, Gordon; Vorwerk, Johannes et al. (2018) Anodic Stimulation Misunderstood: Preferential Activation of Fiber Orientations with Anodic Waveforms in Deep Brain Stimulation. J Neural Eng :
van Wouwe, N C; Pallavaram, S; Phibbs, F T et al. (2017) Focused stimulation of dorsal subthalamic nucleus improves reactive inhibitory control of action impulses. Neuropsychologia 99:37-47
Eisinger, Robert S; Hess, Christopher W; Martinez-Ramirez, Daniel et al. (2017) Motor subtype changes in early Parkinson's disease. Parkinsonism Relat Disord 43:67-72
Almeida, Leonardo; Martinez-Ramirez, Daniel; Ahmed, Bilal et al. (2017) A pilot trial of square biphasic pulse deep brain stimulation for dystonia: The BIP dystonia study. Mov Disord 32:615-618
Burciu, Roxana G; Hess, Christopher W; Coombes, Stephen A et al. (2017) Functional activity of the sensorimotor cortex and cerebellum relates to cervical dystonia symptoms. Hum Brain Mapp 38:4563-4573
Almeida, Leonardo; Deeb, Wissam; Spears, Chauncey et al. (2017) Current Practice and the Future of Deep Brain Stimulation Therapy in Parkinson's Disease. Semin Neurol 37:205-214
Malaty, Irene A; Okun, Michael S; Jaffee, Michael (2016) The Danger of Not Treating Parkinson Disease Psychosis. JAMA Neurol 73:1156
Hollister, Brad E; Duffley, Gordon; Butson, Chris et al. (2016) Visualization for Understanding Uncertainty in Activation Volumes for Deep Brain Stimulation. Eurograph IEEE VGTC Symp Vis 2016:37-41
Vedam-Mai, Vinata; Martinez-Ramirez, Daniel; Hilliard, Justin D et al. (2016) Post-mortem Findings in Huntington's Deep Brain Stimulation: A Moving Target Due to Atrophy. Tremor Other Hyperkinet Mov (N Y) 6:372
Ramirez-Zamora, Adolfo; Okun, Michael S (2016) Deep brain stimulation for the treatment of uncommon tremor syndromes. Expert Rev Neurother 16:983-97

Showing the most recent 10 out of 13 publications