Development and Validation of a Multimodal Ultrasound- Based Biomarker for Myofascial Pain

基于多模态超声的肌筋膜疼痛生物标志物的开发和验证

• 批准号: 10579668
• 负责人: KANG KIM
• 金额: $ 223.25万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579668
• 关键词: 3-Dimensional; 3D ultrasound; Address; Affect; Aftercare; Age; Algorithms; Anatomy; Area Under Curve; Arthritis; Biological Markers; Body Regions; Chronic low back pain; Clinical; Clinical Trials; Computer software; Development; Educational workshop; Elasticity; Fascia; Functional disorder; Image; Injections; Institutes; Intervention; Machine Learning; Measurement; Measures; Mechanics; Methods; Modeling; Motion; Movement; Muscle; Musculoskeletal; Musculoskeletal Pain; Myofascial Pain Syndromes; Needles; Outcome; Outcome Measure; Pain; Pathology; Patient Self-Report; Patients; Performance; Phase; Phenotype; Physicians; Prediction of Response to Therapy; Predictive Value; Process; Property; Randomized Controlled Trials; Receiver Operating Characteristics; Reporting; Scanning; Sensitivity and Specificity; Single-Blind Study; Standardization; Stenosis; Structure; Syndrome; Techniques; Technology; Testing; Therapeutic; Time; Tissues; Treatment outcome; Ultrasonography; United States National Institutes of Health; Validation; Work; arm; base; biomarker development; clinical diagnosis; clinical diagnostics; clinical examination; clinical pain; clinical phenotype; cohort; convolutional neural network; deep learning; deep learning model; diagnostic biomarker; elastography; feature extraction; follow-up; imaging modality; improved; interest; machine learning model; multimodality; novel; pain patient; placebo group; predicting response; predictive marker; pressure; programs; relating to nervous system; response; success; tool; treatment response; trigger point; ultrasound; viscoelasticity

Abstract: “Development and Validation of a Multimodal Ultrasound-Based Biomarker for Myofascial Pain” This proposal responds to RFA-AT-22-003 to address the need for a biomarker indicative of myofascial pain. Myofascial pain can affect many regions of the body and it is a key component of chronic low back pain in particular. Patients with chronic low back pain have a range of musculoskeletal pathologies perpetuating their pain syndrome in addition to the myofascial components, such as facet arthritis or stenosis. Hence, there is a significant clinical need to identify the components of chronic low back pain related to myofascial pain beyond use of the physical exam only. Such a biomarker would have immediate clinical diagnostic uses, as well as being important as a phenotyping tool and outcome measure in clinical trials. Advances in ultrasound technology have resulted in identification of several abnormalities in myofascial tissues related to myofascial pain, beyond identifying trigger points. In addition to echogenicity changes, these include shear wave elastography of muscles and fascia, and dynamic fascia tissue deformation capturing abnormalities in movement/ glide of fascia tissue during lumbar flexion. Despite the clinical need and the available technology, no comprehensive study has integrated these ultrasound measures to validate a biomarker for the myofascial component of chronic low back pain. First, we propose to perform two detailed ultrasound assessments and standardized physical exams (including pressure algometry for painful trigger points) on 160 subjects each with and without chronic low back pain, divided into 4 phenotypic groups with and without painful trigger points. We will correlate the ultrasound measures to the clinical phenotype. Second, we will then use deep learning approaches to construct explainable machine learning models which integrate these measures to classify and predict the myofascial components of chronic low back pain, with latent and/or active trigger points. As performance metrics, we will report on area under the curve (AUC), sensitivity, and specificity. Third, in the R33 phase will perform a single blinded, randomized controlled trial of dry needling versus sham needling in 80 patients with chronic low back pain and active trigger points. We will collect the ultrasound measures and perform a standardized examination for myofascial pain prior to the intervention and at a one-week follow-up. We will test the ability and performance metrics of the deep learning models to predict the intensity of myofascial pain prior to injection and changes in myofascial pain post-needling. We anticipate that this work will lead to a software module which can be incorporated into existing clinical ultrasound machines for assessment of the myofascial components of musculoskeletal pain.

摘要：“基于多模式超声的生物标志物的肌筋膜的开发和验证 疼痛” 该提案对RFA-AT-22-003做出了反应，以满足对肌筋膜疼痛的生物标志物的需求。 肌筋膜疼痛会影响身体的许多区域，这是慢性下背痛的关键组成部分 特别的。慢性下背部疼痛的患者具有多种肌肉骨骼病理学，使其永存 除了肌筋膜成分（例如小平性关节炎或狭窄）外，疼痛综合征。因此，有一个 识别与肌筋膜疼痛有关 仅使用身体检查。这样的生物标志物将立即具有临床诊断用途，以及 在临床试验中作为表型工具和结果测量很重要。超声波进步 技术导致识别与肌筋膜相关的肌筋膜组织中的几种异常 疼痛，除了识别触发点。除了回声变化外，这些还包括剪切波 肌肉和筋膜的弹性图，以及动态筋膜组织变形，可捕获异常 腰部屈曲期间筋膜组织的运动/滑行。尽管临床需要和可用技术， 没有综合研究整合了这些超声措施，以验证肌筋膜的生物标志物 慢性下背痛的成分。首先，我们建议进行两项详细的超声评估，并 160名受试者的标准化身体检查（包括疼痛触发点的压力算法） 并且没有慢性下腰痛，分为有或没有疼痛触发点的4个表型组。我们 将将超声测量与临床表型相关联。其次，我们将使用深度学习 构建可解释的机器学习模型的方法，这些模型整合了这些措施以进行分类和 预测具有潜在和/或活动触发点的慢性下背痛的肌筋膜成分。作为 性能指标，我们将报告曲线（AUC），灵敏度和特异性下的面积。第三，在 R33阶段将执行单个盲人，随机对照试验的干针与80中的假针刺 患有慢性下背部疼痛和主动触发点的患者。我们将收集超声测量和 在干预之前和一周的随访之前，对肌膜疼痛进行标准化检查。 我们将测试深度学习模型的能力和性能指标，以预测 注射前的肌筋膜疼痛和核心疼痛发生后的肌筋膜疼痛。我们预计这项工作 将导致一个软件模块，该模块可以将其纳入现有的临床超声计算机中 评估肌肉骨骼疼痛的肌筋膜成分。