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：“开发和验证基于多模态超声的肌筋膜生物标志物 痛苦” 该提案响应了RFA-AT-22-003，以解决对指示肌筋膜疼痛的生物标志物的需求。 肌筋膜疼痛可以影响身体的许多区域，它是慢性腰痛的关键组成部分， 特别的。慢性下腰痛患者有一系列的肌肉骨骼病变， 除了肌筋膜成分之外的疼痛综合征，例如小关节炎或狭窄。因此有 重要的临床需要，以确定与肌筋膜疼痛相关的慢性腰痛的组成部分， 仅使用体检。这样的生物标志物将具有直接的临床诊断用途，以及 作为临床试验中的表型分析工具和结果测量是重要的。超声的进展 技术已经鉴定了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，还包括剪切波 肌肉和筋膜的弹性成像，以及动态筋膜组织变形， 腰椎屈曲期间筋膜组织的运动/滑动。尽管有临床需求和可用的技术， 没有综合的研究整合这些超声测量来验证肌筋膜的生物标志物 慢性下背痛的一种。首先，我们建议进行两次详细的超声评估， 对160名受试者进行标准化体格检查（包括疼痛触发点的压力痛觉测量）， 和无慢性腰痛，分为4个表型组，有和无疼痛触发点。我们 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，这些模型集成了这些措施来分类和 预测慢性腰痛的肌筋膜成分，具有潜在和/或活动触发点。作为 性能指标，我们将报告曲线下面积（AUC），灵敏度和特异性。三是在 R33期将在80例患者中进行干针与假针的单盲、随机对照试验， 患有慢性腰痛和活动触发点的患者。我们将收集超声波测量结果， 在干预前和一周随访时对肌筋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 注射前肌筋膜疼痛和针刺后肌筋膜疼痛的变化。我们预计这项工作 将导致一个软件模块，可以纳入现有的临床超声机器， 评估肌肉骨骼疼痛的肌筋膜成分。