Imaging Biomarkers for Myofascial Pain
- Conditions
- Myofascial Pain SyndromeHealthy
- Interventions
- Diagnostic Test: Ultrasound imagingDiagnostic Test: Bioimpedance spectroscopyDiagnostic Test: ElectromyographyDiagnostic Test: Physical examination
- Registration Number
- NCT06060925
- Lead Sponsor
- George Mason University
- Brief Summary
Myofascial pain syndrome (MPS) is highly prevalent in the community. It is primarily diagnosed using patient self reports and physical examination, which lack reliability, sensitivity and specificity and does not provide insights into the abnormal biological and physiological processes in soft tissues. While a number of treatment methods are available to patients, there are currently no criteria to determine which treatments might be best for each patient's unique myofascial pain phenotype. To improve evidence-based management of myofascial pain, there is a critical need to develop quantitative measures that advance the understanding of the physiological processes in the underlying the soft tissues across the clinical continuum of MPS. The objective of this project is to develop a quantitative biomarker informed by the current understanding of underlying tissue-level mechanisms at the level of the "myofascial unit" (muscle, nerve, fascia, vasculature, lymphatics) that are likely to be involved in MPS.
- Detailed Description
Definition of proposed composite multimodal biomarker-The investigators propose to develop a quantitative tissue-level classifier based on quantitative metrics (features) derived from ultrasound elastography, Doppler, bioimpedance spectroscopy and high-density electromyography, as an indicator of the normal biological process in myofascial tissues, and pathogenic process in active and latent phases of myofascial pain.
Overall approach and scientific rigor: In Aim 1, the investigators will develop methods to generate reproducible metrics (features) from the raw tissue-level measures and determine the minimum detectable change in these features in a pilot study. In Aim 2, the investigators will conduct a longitudinal observational study with two groups of subjects (control and myofascial pain). The investigators will develop a classification algorithm that optimally differentiates between active and latent phase of myofascial pain and normal myofascial tissue.
Study population and anatomical site. The chosen pain condition is chronic neck and shoulder pain. The investigators will recruit two groups of subjects: Group 1: Chronic myofascial pain as determined by baseline clinical examination using Travell and Simon's criteria7 and Group 2: pain free controls. The investigators will focus on two standardized anatomical locations (Figure 4). This will enable imaging the medial upper trapezius and the infraspinatus muscles, which are common locations for MTrPs55 as well as the levator scapulae. These three muscles have quite different morphology and fasciae45. The levator is a fusiform muscle with well-defined fascia that includes the muscle while the trapezius has thinner fascia from where perimysium septae cross the muscle belly. The infraspinatus has multiple fascial layers on its surface and has clear segmental linkages to the C5-6 segment56 Eligibility criteria: The investigators will recruit adults 18-65 years of age. Exclusion criteria: (1) diagnosis of fibromyalgia, chronic fatigue syndrome or chronic Lyme disease; (2) Diagnosis of cervical radiculopathy, neuropathy, or neuritis; (3) History of head, neck, cervical spine, or shoulder girdle surgery; (4) Atypical facial neuralgia; (5) New medication or change in medication in past 6 weeks; (6) Current throat or ear infection.
Masking and Matching: This is a single-blind longitudinal observational study. The team performing the data collection and analysis will not know the group allocation of the subjects and will be blinded to the results of the clinical evaluations. The two groups will be age and gender matched using a paired recruitment strategy57. The investigators will identify a pool of eligible control subjects with no history of pain and divide them into gender and age brackets (18-30; 31-50; and \>50). For each Group 1 subject recruited in a bracket, the investigators will recruit a matched Group 2 subject from the pool.
Sex as a biological variable: Myofascial pain is widely prevalent in the community and affects both men and women. Trapezius myalgia is more prevalent in women58. The investigators will utilize age and gender-matched groups, and will test the classifier performance for both the pooled population as well as separately by gender to identify any gender-specific differences in the biomarker measures.
Outcome Measures: The primary outcome measure will be the composite classifier based on the tissue-level quantitative biomarkers. The investigators will perform repeated data collections every month for 3 months. The clinical phenotype of the subjects (normal, latent, episodic active, and persistent active) will be determined by a comprehensive physical examination protocol12. The investigators will utilize the NIH HEAL Common Data Elements for adult chronic pain to collect self reports. To further characterize the clinical phenotype, as a secondary outcome measure, the investigators will utilize an ecological momentary assessment (EMA) application (Metricwire) on a smartphone to obtain a daily pain rating triggered at random points during the day and collect automated activity monitoring from the smartphone sensors. The investigators will also collect weekly 3-item pain intensity and interference59.
Data collection procedures Data management: This is a single site study. All study procedures will be performed at Mason. The study biostatistician (Rosenberger) will set up the appropriate masking controls and electronic case report forms (eCRFs) in the electronic data capture system (REDCap). All study data will be entered into REDCap using eCRFs. Study personnel will have appropriate role-based access controls in REDCap. Source validation will be performed using REDCap's built-in checks.
Masking: A single clinician (Gerber) will obtain each subject's consent and conduct history and the physical examination. An additional clinician (DeStefano) may be present to assist, and a research assistant will be present to take notes and enter data. The engineering team, supervised by the PI (Sikdar) and co-I Chitnis, will collect the outcome measures in a separate room and will be masked to the patient's history and results of the physical examination. A manual of operating procedures will be developed for the study.
Data analysis procedures. All data analysis will be performed by a biostatistics graduate research assistant under the supervision of the data scientist (Lee) and study biostatistician (Rosenberger).
Primary analysis: The investigators will construct and rigorously validate a multi-class classification algorithm based on functional time series and statistical learning methods. Here, the biomarker time series can be represented as combination of unique temporal patterns/signals, or basis functions. These functions include time-invariant eigenbasis functions80, smoothing splines81, wavelets82, or functional principal components83. Using functional data analysis, a composite predictor variable will be constructed that summarizes the pertinent information contained in the biomarker time series. Then, a multi-class classification method will be constructed using supervised learning approaches, such as support vector machines84, discriminant analysis85,86, neural networks87,88, regression trees89. The classifying algorithm will use the composite predictor to codify subjects into the four relevant categories (pain - episodic, pain - active, control-episodic, and control-active). K-fold cross-validation will be used to assess the classifier's accuracy based on sensitivity, specificity, F1 score, and the area under the ROC curve for multi-class scenarios90,91.
Secondary analysis: Several secondary analyses will be performed including: (1) Determine normative values of biomarkers in control group (Group 2); (2) Evaluate convergent validity of primary and secondary biomarkers. Since the underlying ground truth cannot be measured directly, the primary and secondary biomarkers will be utilized to evaluate convergent validity; (3) Correlation with corresponding clinical measure (range of motion, pressure pain threshold.
Recruitment & Eligibility
- Status
- ENROLLING_BY_INVITATION
- Sex
- All
- Target Recruitment
- 129
- Age 18 and older
- Diagnosis of fibromyalgia, chronic fatigue syndrome or chronic Lyme disease confirmed by physical exam
- Diagnosis of cervical radiculopathy, neuropathy or neuriitis
- History of head, neck, or shoulder girdle surgery
- Atypical facial neuralgia
- New medication or change in medication in past 6 months
- Current throat or ear infection
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Subjects without pain Ultrasound imaging No symptoms of chronic pain. Active myofascial pain syndrome Electromyography Subjects that experience spontaneous pain Active myofascial pain syndrome Physical examination Subjects that experience spontaneous pain Latent myofascial pain syndrome Bioimpedance spectroscopy Subjects that elicit pain only when palpated and disturbed. Latent myofascial pain syndrome Electromyography Subjects that elicit pain only when palpated and disturbed. Subjects without pain Electromyography No symptoms of chronic pain. Active myofascial pain syndrome Ultrasound imaging Subjects that experience spontaneous pain Active myofascial pain syndrome Bioimpedance spectroscopy Subjects that experience spontaneous pain Latent myofascial pain syndrome Ultrasound imaging Subjects that elicit pain only when palpated and disturbed. Latent myofascial pain syndrome Physical examination Subjects that elicit pain only when palpated and disturbed. Subjects without pain Bioimpedance spectroscopy No symptoms of chronic pain. Subjects without pain Physical examination No symptoms of chronic pain.
- Primary Outcome Measures
Name Time Method Ultrasound Doppler Baseline, month 3 Ultrasound Doppler estimates the flow velocity in blood vessels. We will extract end-diastolic velocity as the outcome measure.
Bioimpedance spectroscopy Baseline, month 3 Bioimpedance spectroscopy involves sending a small current into tissue at different frequencies and estimating the resistance and reactance. It can be used to measure fluid content in the extracellular space.
High density electromyography Baseline, month 3 High density electromyography involves the placement of a 64-channel electrode array on the skin surface and measuring the electrical activity of muscles. It can be used to measure motor unit excitability. We will extract the Force/EMG ratio as the outcome measure.
Ultrasound shear wave elastography Baseline, month 3 Shear wave elastography utilizes the radiation force of ultrasound to induce shear waves in tissue and measure the propagation speed. It provides information about the mechanical properties of tissue. We will extract the shear anisotropy ratio as the outcome measure.
- Secondary Outcome Measures
Name Time Method Pressure pain threshold Baseline, month 3 The pressure pain threshold will be determined by an algometer with a 1cm2 probe area, with a series of three ascending stimulus intensities, each applied as a slowly increasing ramp of 50 kPa/s.
Cervical and shoulder range of motion Baseline, month 3 We will quantify the asymmetry in range of motion (flexion/extension; side bending;rotation; abduction/adduction)using a marker less motion capture system.
NIH HEAL Common data elements for adult chronic pain Baseline, month 3 The physical examination will include the NIH HEAL Initiative recommended core data elements for adult chronic pain: • Pain intensity (PEG) • Pain interference (PEG) • Physical functioning/quality of life (PROMIS Physical Functioning Short Form 6b • Sleep (PROMIS Sleep Disturbance 6a + Sleep Duration Question) • Pain catastrophizing (Pain Catastrophizing Scale - Short Form 6) • Depression (PHQ-2) • Anxiety (GAD-2) • Global satisfaction with treatment (PGIC) • Substance use screener (TAPS 1)
Ecological Momentary Assessment Month 1-3 We will utilize the MetricWire app to ask subjects to record their pain and activity rating during the day and during the evening every day for 3 months
Windup ratio Baseline, month 3 We will quantify the degree of windup (temporal summation) by measuring the amplification of pain to a train of nociceptive stimuli (a logarithmically scaled set of weighted pinpricks) applied over dermatomes that are segmentally linked to the target trapezius and infraspinatus muscles. Subjects will provide a numeric pain rating score (NPRS, 11-point scale) after each stimulus. The windup ratio (WUR) will be calculated as the ratio of the mean NPRS to baseline.
Trial Locations
- Locations (1)
George Mason University
🇺🇸Fairfax, Virginia, United States