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、普遍和昂贵的病症，难以治疗。 这一领域的创新大大提高了战斗人员的生存率，但也增加了慢性疼痛幸存者的人数。现役军人和退伍军人的疼痛问题有许多独特的特点。除了战场受伤外，比普通公众更大的创伤和心理压力也会增加患慢性疼痛的可能性。损伤相关慢性疼痛的管理可能需要多种方法的组合，包括局部和全身镇痛药物，疼痛导向的物理治疗，行为矫正，生物反馈和其他干预措施。脊髓电刺激（SCS）是治疗退伍军人慢性疼痛的一种越来越多的治疗方法。临床研究表明，它对适当选择的患者是安全有效的，并被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手术相比，SCS的利用率仍然显著不足。VHA已通过常规筛查和疼痛评估以及疼痛减轻计划作为整个VHA医疗保健系统的国家政策。SCS涉及连接到小型内部起搏器的刺激电极的硬膜外植入。与疼痛相关的皮区中的背柱（DC）纤维的激活通常受到与附近背根（DR）的激活相关的不适的发作的限制。成功的SCS被认为依赖于DC纤维的激活而没有DR纤维的激活，这被称为选择性。预测选择性，以及哪些患者将对治疗产生反应，目前只能通过进行一项研究来实现。 侵入性"试验"程序，其中暂时插入电极，允许患者体验神经刺激并说明其是否有帮助。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程控可能耗时、昂贵并导致异常刺激。本提案的目的是通过基于患者解剖结构定制动态范围，验证可靠预测% DC/DR选择性激活的患者特定模型。该项目是与退伍军人事务部神经外科和杜克大学生物工程和神经外科部门的专家合作。其重点是确认患者特定SCS模型，该模型利用针对患者解剖结构定制的设置可靠地预测负责疼痛缓解的神经元件的激活。已经完成了一项试点临床研究，证明了概念的可行性。这项CSR & D优异奖将在现有SCS治疗的退伍军人中进行更大规模的机制研究。在目标1中，将结合每例患者现有的术前MRI和术后X射线图像，以构建基于患者特定解剖结构的个性化计算模型。目标2将进一步证明模型在最大化选择性方面的可行性，同时通过提供> 50%的基线疼痛减轻来减少SCS程控时间并改善临床结局。根据团队的实力和广泛的初步数据，预计该提案将成功完成。该项目将提供数据，以证明更广泛的临床研究和定制SCS软件平台的开发是合理的，用于评价和治疗慢性疼痛。该项目的长期目标是开发一个新的平台，这将导致全球退伍军人慢性疼痛治疗的重大进展。

 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.