Evaluation of Efficiency and Selectivity of a Novel Computational Model of Spinal Cord Stimulation for Chronic Pain

 描述(由申请人提供)： 慢性疼痛是一种重要的、普遍的、昂贵的疾病，很难治疗。现代 实地的创新极大地提高了战斗人员的存活率，但也增加了长期痛苦的存活者的人数。现役军人和退伍军人的疼痛问题有许多独特的特点。除了战场上的伤害外，比普通公众更大的创伤和心理压力会加剧慢性疼痛的可能性。治疗与损伤相关的慢性疼痛可能需要多种方法的结合，包括局部和全身止痛药、疼痛定向物理治疗、行为矫正、生物反馈和其他干预措施。在治疗慢性疼痛的退伍军人中，一种越来越常用的治疗方法是脊髓刺激(SCS)。临床研究表明，它对经过适当选择的患者是安全有效的，并已被FDA批准用于治疗躯干和四肢的慢性疼痛。与慢性阿片类药物或外科再次手术相比，SCS的利用率仍然很低。VHA通过了对疼痛的常规筛查和评估以及规划减轻疼痛的计划，作为整个VHA卫生保健系统的一项国家政策。SCS包括在硬膜外植入一个连接到小型内部起搏器的刺激电极。与疼痛相关的皮肤体中的背柱(DC)纤维的激活通常受到与附近的背根(DR)激活相关的不适的开始的限制。成功的SCS被认为依赖于DC纤维的激活而不是DR纤维的激活，这被称为选择性。预测选择性，以及哪些患者会对治疗有反应，目前只能通过做一个 侵入性“试验”程序，临时插入电极，允许患者体验神经刺激并说明它是否有帮助。此外，由于数百万种可能的刺激参数和不同的患者解剖结构，SCS编程可能会耗时、昂贵，并导致异常刺激。这项建议的目的是验证一个患者特有的模型，通过根据患者解剖调整动态范围，可靠地预测DC/DR选择性激活。这个项目是与退伍军人神经外科以及杜克大学生物工程和神经外科的专家合作完成的。它将专注于验证特定于患者的SCS模型，该模型利用根据患者解剖学量身定做的设置，可靠地预测负责疼痛缓解的神经元件的激活。一项演示概念验证的试验性临床研究已经完成。本次CSR&D优秀奖将在现有SCS疗法的退伍军人中进行更大规模的机械性研究。在目标1中，每个患者现有的术前MRI和术后X光图像将结合在一起，以患者特定的解剖为基础构建个性化的计算模型。目标2将进一步证明模型的可行性，在最大限度地提高选择性，同时减少SCS编程时间和改善临床结果，提供&gt；50%的疼痛从基线减少。根据小组的实力和广泛的初步数据，预计这项提案将成功完成。该项目将提供数据，以证明更广泛的临床研究和开发用于评估和治疗慢性疼痛的定制SCS软件平台的合理性。该项目的长期目标是开发一个新的平台，使世界各地退伍军人的慢性疼痛治疗取得重大进展。

 DESCRIPTION (provided by applicant): Chronic pain is an important, prevalent, and expensive condition that is difficult to treat. Modern innovations in the field have dramatically increased the survival of combatants, but also increased the number of those surviving with chronic pain. Pain issues among active-duty military and Veterans have many unique features. In addition to battlefield injuries, greater exposure to trauma and psychological stress than the general public compounds the likelihood of developing chronic pain. Management of injury-related chronic pain may require a combination of approaches including local and systemic analgesic drugs, pain-directed physical therapy, behavior modification, biofeedback, and other interventions. An increasingly employed therapy in the management of Veterans with chronic pain is spinal cord stimulation (SCS). It has been shown in clinical studies to be safe and effective for properly selected patients and approved by the FDA to treat chronic pain of the trunk and limbs. SCS remains significantly under-utilized compared to either chronic opiates or surgical re- operation. The VHA has adopted routine screening and assessment of pain as well as planning for pain reduction as a national policy throughout the VHA health care system. SCS involves epidural implantation of a stimulation electrode connected to a small internal pacemaker. Activation of dorsal column (DC) fibers in the dermatomes associated with pain is often limited by the onset of discomfort associated with activation of nearby dorsal roots (DR). Successful SCS is thought to depend on activation of DC fibers without activation of DR fibers, which is termed selectivity. Predicting selectivity, and which patients will respond to the therapy, is currently only possible by doing an invasive "trial" procedure, where an electrode is temporarily inserted and the patient is allowed to experience the neurostimulation and state whether it is helpful. Furthermore, due to the millions of possible stimulation parameters and varying patient anatomy, SCS programming can be time consuming, expensive, and result in aberrant stimulation. The objective of this proposal is to validate a patient-specific model that reliably predicts %DC/DR selective activation, by tailoring the dynamic range based on patient anatomy. This project is a collaboration with experts from VA Neurosurgery and the Bioengineering and Neurosurgery Departments at Duke University. It will focus on validating a patient-specific SCS model that reliably predicts activaton of neural elements responsible for pain relief, utilizing settings tailored to patient anatomy. A pilot clinical study demonstrating proof of concept has already been completed. This CSR&D Merit Award will expand upon this in a larger, mechanistic study in Veterans with existing SCS therapy. In Aim 1, each patient's existing preoperative MRI and postoperative X-ray images will be combined to construct a personalized, computational model based on patient-specific anatomy. Aim 2 will further demonstrate feasibility of models in maximizing selectivity, while reducing SCS programming times and improving clinical outcomes, by providing >50% reduction in pain from baseline. Successful completion of this proposal is expected based on the strength of the team and extensive preliminary data. This project will provide the data to justify broader clinical studies and development of a customized SCS software platform for the evaluation and treatment of chronic pain. The long-term goal of the project is to develop a novel platform that will lead to significant advances in the treatment of chronic pain for Veterans worldwide.