ABSTRACT

Deep brain stimulation (DBS) is a therapy that has been shown to be effective for the treatment of Parkinson's disease and essential tremor, and is now being assessed for a wide range of other disorders such as Alzheimer's disease, depression and traumatic brain injury. Hence, patients with a wide range of neurological disorders could benefit from DBS. However, these patients face an access problem because DBS devices are almost exclusively implanted and managed in major cities at academic medical centers. While it is reasonable for a patient to travel once or twice for surgery, it can be infeasible for them to travel long distances for post-operative management of their DBS devices in the months and years following surgery. We envision a new model in which patients travel once or twice for surgery and then are managed in their home area by community neurologists or family practice physicians who use expert decision support tools to choose DBS device settings. The purpose of this grant is to test the use of an app-based decision support platform that runs on iOS devices, and provides predictive, patient-specific computational models over a high-bandwidth network that was developed for healthcare applications. We believe that this system can drastically reduce the amount of time necessary for DBS programming, and in the future it may enable patients to be post-operatively managed without the need to travel to DBS surgical centers. We anticipate that if this study is successful then it will achieve a critical step by providing a system that runs on mobile devices, and can be used to manage DBS patients across a wide range of neurological disorders. Hence, we feel that the technology developed and tested in this application could have transformative effects on large numbers of patients.

In recent years there has been substantial growth in the use of patient-specific computational models to predict and visualize the effects of neuromodulation therapies such as deep brain stimulation (DBS) to treat movement disorders including Parkinson's disease (PD) and essential tremor (ET). These models have been clinically validated, and their utility in DBS programming has been demonstrated in several studies. However, translating these models from a research environment to the everyday clinical workflow has been a major challenge, primarily due to the complexity of the models and the expertise required in specialized visualization software. In this application we propose to deploy an interactive visualization system, ImageVis3D Mobile (IV3Dm), which has been designed for mobile iOS computing devices such as the iPhone or iPad, to visualize patients-specific models of Parkinson's disease (PD) patients who received DBS therapy. Selection of DBS settings is a significant clinical challenge that requires considerable expertise to achieve optimal therapeutic response, and is often performed without any visual representation of the stimulation system in the patient. This issue is compounded by a catch-22 in the management of these patients: very few clinicians outside academic medical centers will manage DBS patients because they lack the tools and expertise to do so; no one has developed remote, mobile tools because there is a perception that providers outside academic medical centers will not use them. The purpose of this application is to break this deadlock by providing a decision support system that can provide clinicians with the tools necessary to manage DBS patients in rural areas. We have previously tested the utility of IV3Dm for programming DBS patients in a controlled clinical setting and have shown that it can drastically reduce the amount of time necessary to choose good therapeutic settings. In this application we proposed to add several key enabling technologies and test the use of IV3Dm on PD patients in remote areas of Utah. These include: integrating a previously developed GPU-based solver; adding remote volume rendering capability to IV3Dm to enable a wide range of possible DBS settings; testing IV3Dm over the Utah Telehealth Network (UTN), a broadband network in the State of Utah that is dedicated for use in healthcare. We anticipate that if this study is successful we will show that PD patients can receive care that is comparable to that provided by specialists at major medical centers but with far less patient burden (i.e. travel time). The intellectual merit of this application lies in the delivery of patient-specific computational models of DBS patients over a broadband telehealth network to improve the care of PD patients.

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