Development and Validation of a Noninvasive Multimodal Ultrasound-Based Imaging Biomarker for Myofascial Pain
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Chronic Low-back Pain
- Sponsor
- University of Pittsburgh
- Enrollment
- 124
- Locations
- 1
- Primary Endpoint
- Diagnosis of one of four MP-related categories
- Status
- Completed
- Last Updated
- 8 months ago
Overview
Brief Summary
The goal of this observational study is to develop and validate a biomarker for lumbar myofascial pain (MP) based on ultrasound obtained measurements of the lumbar muscles and fascia. The investigators will use advanced machine learning approaches and validation in a randomized controlled trial. The main questions it aims to answer are:
- Will the deep learning-based marker reliably identify subjects from the 4 different groups: healthy, MP without trigger points, MP with latent trigger points, and MP with active trigger points?
- Will the deep learning-based marker accurately classify/predict the severity of MP in subjects with cLBP?
Participants in the healthy group will be asked to do the following tasks:
- Consent/Enrollment
- Measure Height/Weight
- Complete Questionnaires on REDCap
- Participate in Ultrasound Imaging Experiment Sessions
Participants in the chronic low back pain group will be asked to do the following tasks:
- Consent/Enrollment
- Complete Questionnaires on REDCap
- Measure Height/Weight
- Undergo a Standardized Clinical Exam
- Participate in Ultrasound Imaging Experiment Sessions
Detailed Description
The investigators propose to use multimodal ultrasound imaging to develop and validate a practical and inexpensive biomarker for lumbar myofascial pain, which shows sensitivity to change in relation to treatment. Myofascial pain (MP) is a frequent contributing factor to chronic low back pain (cLBP). It is associated with a range of tissue abnormalities, such as taught muscle bands, trigger points (TPs), and thoracolumbar fascia motion dysfunction, along with poor tissue elasticity. As a result, a composite biomarker for MP related to components of the syndrome is more likely to be plausible biologically, robust, and useful clinically for diagnosis and treatment. The investigators propose to study: 1. The echogenicity of latent and active trigger points, 2. The dynamic spatial-temporal tissue deformation quantified by strain tensors (compression, extension, and shear) in the thoracolumbar fascia and multifidus muscle, 3. The viscoelastic properties of the fascia and muscles measured by ultrasound shear wave elastography. In the R61 Phase (year 1 to 3) the investigators will use deep learning to integrate these measurements into a predictive biomarker and use established validation methods to test its ability to predict MP. The investigators will determine the sensitivity and specificity of the biomarker to classify the myofascial components of pain, as well as the response to treatment (a diagnostic and predictive marker).
Investigators
Ajay Wasan, MD, Msc
Professor
University of Pittsburgh
Eligibility Criteria
Inclusion Criteria
- Not provided
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Diagnosis of one of four MP-related categories
Time Frame: Study Visit 1 (week 1)
Participants to be diagnosed as normal, MP without TPs, MP with latent TPs, and MP with active TPs as determined by standardized clinical examinations.
Secondary Outcomes
- Presence of Substantial MP(Study Visit 1 (week 1) - Study Visit 2 (week 2))