US Ignite: Track 1: Remote Management of Deep Brain Stimulation (DBS) Patients Using Utah Telehealth Network (UTN)

US Ignite：轨道 1：使用犹他州远程医疗网络 (UTN) 远程管理深部脑刺激 (DBS) 患者

• 批准号: 1531003
• 负责人: Christopher Butson
• 金额: $ 59.54万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1531003&HistoricalAwards=false
• 关键词: US; Ignite; Track; Remote; Management

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.

深脑刺激（DBS）是一种疗法，已被证明可有效治疗帕金森氏病和本质震颤，现在正在评估各种其他疾病，例如阿尔茨海默氏病，抑郁症和创伤性脑损伤。因此，患有广泛神经系统疾病的患者可以从DBS中受益。但是，这些患者面临访问问题，因为DBS设备几乎完全在学术医疗中心的主要城市植入和管理。虽然患者可以旅行一两次手术是合理的，但在手术后的几个月和几年中，他们的DBS设备的术后管理长距离可能是不可行的。我们设想了一种新的模型，其中患者一次或两次进行手术旅行，然后由社区神经科医生或家庭实践医生在其家中进行管理，他们使用专家决策支持工具选择DBS设备设置。该赠款的目的是测试在iOS设备上运行的基于应用程序的决策支持平台的使用，并通过用于医疗保健应用程序开发的高带宽网络提供预测性，特定于患者的计算模型。我们认为，该系统可以大大减少DBS编程所需的时间，并且将来可能使患者可以在术后进行管理，而无需前往DBS手术中心。我们预计，如果这项研究成功，那么它将通过提供在移动设备上运行的系统来实现关键的一步，并且可用于管理各种神经系统疾病的DBS患者。因此，我们认为在此应用中开发和测试的技术可能会对大量患者产生变革性的影响。近年来，使用特异性的计算模型的使用已大幅增长，以预测和可视化神经调节疗法的影响，例如深脑刺激（DBS），例如治疗包括帕金森氏病（PD）和Essential Threvor（Ectential Temeror（Etor）（ET））。这些模型已经在临床上进行了验证，并且在多项研究中证明了它们在DBS编程中的实用性。但是，将这些模型从研究环境转换为日常临床工作流程一直是一个重大挑战，这主要是由于模型的复杂性和专业可视化软件所需的专业知识。在此应用程序中，我们建议部署交互式可视化系统，ImageVIS3D Mobile（IV3DM），该系统是为移动iOS计算设备（例如iPhone或iPad）设计的，以可视化接受DBS治疗的帕金森氏病（PD）患者特异性患者。 DBS设置的选择是一个重大的临床挑战，需要大量的专业知识才能实现最佳的治疗反应，并且通常在患者中没有任何视觉刺激系统的视觉表示。在这些患者的管理中，Catch-22使这个问题更加复杂：学术医疗中心以外的临床医生很少会管理DBS患者，因为他们缺乏这样做的工具和专业知识；没有人开发出远程移动工具，因为有一种看法，即在学术医疗中心以外的提供者不会使用它们。本申请的目的是通过提供一个决策支持系统来打破这一僵局，该决策支持系统可以为临床医生提供在农村地区管理DBS患者所需的工具。我们以前已经在受控的临床环境中测试了IV3DM对编程DBS患者的实用性，并表明它可以大大减少选择良好的治疗环境所需的时间。在此应用程序中，我们建议添加几种关键促进技术，并测试犹他州偏远地区PD患者的IV3DM的使用。其中包括：集成先前开发的基于GPU的求解器；将远程音量渲染功能添加到IV3DM中，以启用广泛的DBS设置；在犹他州远程医疗网络（UTN）上测试IV3DM，这是犹他州的宽带网络，专门用于医疗保健。我们预计，如果这项研究成功，我们将表明PD患者可以接受与主要医疗中心专家提供的护理相当的护理，但患者负担的负担要小得多（即旅行时间）。该应用的智力优点在于在宽带远程医疗网络上传递了DBS患者特定于患者的计算模型，以改善PD患者的护理。