Imaging Biomarkers for Myofascial Pain

Healthy Clinical Trial

Official title:

Multimodal Imaging Biomarkers for Investigating Fascia, Muscle and Vasculature in Myofascial Pain

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT06060925
• Other study ID #: 1933416
• Status: Enrolling by invitation
• Start date: January 1, 2023
• Est. completion date: September 2024

Study information

• Verified date: September 2023
• Source: George Mason University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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 information / eligibility

• Status: Enrolling by invitation
• Enrollment: 129
• Est. completion date: September 2024
• Est. primary completion date: September 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Age 18 and older

Exclusion Criteria:

• 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

Related Conditions & MeSH terms

• Fibromyalgia
• Healthy
• Myofascial Pain Syndrome
• Myofascial Pain Syndromes

Intervention

Diagnostic Test:
• Ultrasound imaging
B-mode, color Doppler, shear wave elastography
• Bioimpedance spectroscopy
Multifrequency bioimpedance spectroscopy
• Electromyography
High density electromyography
• Physical examination
Comprehensive musculoskeletal physical examination, including range of motion, and quantitative sensory testing

Locations

Country	Name	City	State
United States	George Mason University	Fairfax	Virginia

Sponsors (1)

Lead Sponsor	Collaborator
George Mason University

Country where clinical trial is conducted

United States, 

References & Publications (104)

Adams MCB, Hurley RW, Siddons A, Topaloglu U, Wandner LD. NIH HEAL Clinical Data Elements (CDE) implementation: NIH HEAL Initiative IMPOWR network IDEA-CC. Pain Med. 2023 Jul 5;24(7):743-749. doi: 10.1093/pm/pnad018. — View Citation

Afsharipour B, Petracca F, Gasparini M, Merletti R. Spatial distribution of surface EMG on trapezius and lumbar muscles of violin and cello players in single note playing. J Electromyogr Kinesiol. 2016 Dec;31:144-153. doi: 10.1016/j.jelekin.2016.10.003. Epub 2016 Oct 31. — View Citation

Akamatsu FE, Ayres BR, Saleh SO, Hojaij F, Andrade M, Hsing WT, Jacomo AL. Trigger points: an anatomical substratum. Biomed Res Int. 2015;2015:623287. doi: 10.1155/2015/623287. Epub 2015 Feb 24. — View Citation

Allwein EL, Schapire RE, Singer Y. Reducing multiclass to binary: A unifying approach for margin classifiers. J Mach Learn Res. 2000;1(Dec):113-141.

Alwosheel A, van Cranenburgh S, Chorus CG. Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. J Choice Model. 2018;28:167-182.

Ballyns JJ, Shah JP, Hammond J, Gebreab T, Gerber LH, Sikdar S. Objective sonographic measures for characterizing myofascial trigger points associated with cervical pain. J Ultrasound Med. 2011 Oct;30(10):1331-40. doi: 10.7863/jum.2011.30.10.1331. — View Citation

Ballyns JJ, Turo D, Otto P, Shah JP, Hammond J, Gebreab T, Gerber LH, Sikdar S. Office-based elastographic technique for quantifying mechanical properties of skeletal muscle. J Ultrasound Med. 2012 Aug;31(8):1209-19. doi: 10.7863/jum.2012.31.8.1209. — View Citation

Bartels EM, Sorensen ER, Harrison AP. Multi-frequency bioimpedance in human muscle assessment. Physiol Rep. 2015 Apr;3(4):e12354. doi: 10.14814/phy2.12354. — View Citation

Beleites C, Neugebauer U, Bocklitz T, Krafft C, Popp J. Sample size planning for classification models. Anal Chim Acta. 2013 Jan 14;760:25-33. doi: 10.1016/j.aca.2012.11.007. Epub 2012 Nov 17. — View Citation

Bird M, Le D, Shah J, et al. Characterization of local muscle fiber anisotropy using shear wave elastography in patients with chronic myofascial pain. In: 2017 IEEE International Ultrasonics Symposium (IUS). ; 2017:1-4. doi:10.1109/ULTSYM.2017.8091631

Cowman MK, Schmidt TA, Raghavan P, Stecco A. Viscoelastic Properties of Hyaluronan in Physiological Conditions. F1000Res. 2015 Aug 25;4:622. doi: 10.12688/f1000research.6885.1. eCollection 2015. — View Citation

Cristianini N, Shawe-Taylor J, Department of Computer Science Royal Holloway John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press; 2000

Deyo RA, Dworkin SF, Amtmann D, Andersson G, Borenstein D, Carragee E, Carrino J, Chou R, Cook K, DeLitto A, Goertz C, Khalsa P, Loeser J, Mackey S, Panagis J, Rainville J, Tosteson T, Turk D, Von Korff M, Weiner DK. Report of the NIH Task Force on research standards for chronic low back pain. Pain Med. 2014 Aug;15(8):1249-67. doi: 10.1111/pme.12538. — View Citation

Dobbin KK, Zhao Y, Simon RM. How large a training set is needed to develop a classifier for microarray data? Clin Cancer Res. 2008 Jan 1;14(1):108-14. doi: 10.1158/1078-0432.CCR-07-0443. — View Citation

Doggweiler-Wiygul R. Urologic myofascial pain syndromes. Curr Pain Headache Rep. 2004 Dec;8(6):445-51. doi: 10.1007/s11916-004-0065-1. — View Citation

Duarte FCK, Hurtig M, Clark A, Brown S, Simpson J, Srbely J. Experimentally induced spine osteoarthritis in rats leads to neurogenic inflammation within neurosegmentally linked myotomes. Exp Gerontol. 2021 Jul 1;149:111311. doi: 10.1016/j.exger.2021.111311. Epub 2021 Mar 17. — View Citation

Duarte FCK, Hurtig M, Clark A, Simpson J, Srbely JZ. Association between naturally occurring spine osteoarthritis in geriatric rats and neurogenic inflammation within neurosegmentally linked skeletal muscle. Exp Gerontol. 2019 Apr;118:31-38. doi: 10.1016/j.exger.2019.01.002. Epub 2019 Jan 4. — View Citation

Duarte FCK, West DWD, Linde LD, Hassan S, Kumbhare DA. Re-Examining Myofascial Pain Syndrome: Toward Biomarker Development and Mechanism-Based Diagnostic Criteria. Curr Rheumatol Rep. 2021 Jul 8;23(8):69. doi: 10.1007/s11926-021-01024-8. — View Citation

Eby SF, Song P, Chen S, Chen Q, Greenleaf JF, An KN. Validation of shear wave elastography in skeletal muscle. J Biomech. 2013 Sep 27;46(14):2381-7. doi: 10.1016/j.jbiomech.2013.07.033. Epub 2013 Jul 30. — View Citation

egatheeswaran G. The Effects of Central Sensitization on Motoneurone Excitability in Osteoarthritis. Published online 2012.

Evans V, Koh RGL, Duarte FCK, Linde L, Amiri M, Kumbhare D. A randomized double blinded placebo controlled study to evaluate motor unit abnormalities after experimentally induced sensitization using capsaicin. Sci Rep. 2021 Jul 2;11(1):13793. doi: 10.1038/s41598-021-93188-7. — View Citation

Ezzati K, Ravarian B, Saberi A, Salari A, Reyhanian Z, Khakpour M, Yousefzadeh Chabok S. Prevalence of Cervical Myofascial Pain Syndrome and its Correlation with the Severity of Pain and Disability in Patients with Chronic Non-specific Neck Pain. Arch Bone Jt Surg. 2021 Mar;9(2):230-234. doi: 10.22038/abjs.2020.48697.2415. — View Citation

Fede C, Angelini A, Stern R, Macchi V, Porzionato A, Ruggieri P, De Caro R, Stecco C. Quantification of hyaluronan in human fasciae: variations with function and anatomical site. J Anat. 2018 Oct;233(4):552-556. doi: 10.1111/joa.12866. Epub 2018 Jul 24. — View Citation

Figueroa RL, Zeng-Treitler Q, Kandula S, Ngo LH. Predicting sample size required for classification performance. BMC Med Inform Decis Mak. 2012 Feb 15;12:8. doi: 10.1186/1472-6947-12-8. — View Citation

Fleckenstein J, Zaps D, Ruger LJ, Lehmeyer L, Freiberg F, Lang PM, Irnich D. Discrepancy between prevalence and perceived effectiveness of treatment methods in myofascial pain syndrome: results of a cross-sectional, nationwide survey. BMC Musculoskelet Disord. 2010 Feb 11;11:32. doi: 10.1186/1471-2474-11-32. — View Citation

Gaffney BM, Maluf KS, Curran-Everett D, Davidson BS. Associations between cervical and scapular posture and the spatial distribution of trapezius muscle activity. J Electromyogr Kinesiol. 2014 Aug;24(4):542-9. doi: 10.1016/j.jelekin.2014.04.008. Epub 2014 Apr 28. — View Citation

Gennisson JL, Catheline S, Chaffai S, Fink M. Transient elastography in anisotropic medium: application to the measurement of slow and fast shear wave speeds in muscles. J Acoust Soc Am. 2003 Jul;114(1):536-41. doi: 10.1121/1.1579008. — View Citation

George D, Lloyd H, Silverman RH, Chitnis PV. A frequency-domain non-contact photoacoustic microscope based on an adaptive interferometer. J Biophotonics. 2018 Jun;11(6):e201700278. doi: 10.1002/jbio.201700278. Epub 2018 Feb 26. — View Citation

Gerber LH, Sikdar S, Armstrong K, Diao G, Heimur J, Kopecky J, Turo D, Otto P, Gebreab T, Shah J. A systematic comparison between subjects with no pain and pain associated with active myofascial trigger points. PM R. 2013 Nov;5(11):931-8. doi: 10.1016/j.pmrj.2013.06.006. Epub 2013 Jun 28. — View Citation

Gerwin RD, Dommerholt J, Shah JP. An expansion of Simons' integrated hypothesis of trigger point formation. Curr Pain Headache Rep. 2004 Dec;8(6):468-75. doi: 10.1007/s11916-004-0069-x. — View Citation

Gerwin RD. Myofascial and Visceral Pain Syndromes: Visceral-Somatic Pain Representations. J Musculoskelet Pain. 2002;10(1-2):165-175. doi:10.1300/J094v10n01_13

Greven S, Crainiceanu C, Caffo B, Reich D. Longitudinal functional principal component analysis. Electron J Stat. 2010;4:1022-1054. doi: 10.1214/10-EJS575. — View Citation

Guan S, Khan AA, Sikdar S, Chitnis PV. Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal. IEEE J Biomed Health Inform. 2020 Feb;24(2):568-576. doi: 10.1109/JBHI.2019.2912935. Epub 2019 Apr 23. — View Citation

Guo W. Functional data analysis in longitudinal settings using smoothing splines. Stat Methods Med Res. 2004 Feb;13(1):49-62. doi: 10.1191/0962280204sm352ra. — View Citation

Guzman-Pavon MJ, Cavero-Redondo I, Martinez-Vizcaino V, Torres-Costoso AI, Reina-Gutierrez S, Alvarez-Bueno C. Effect of Manual Therapy Interventions on Range of Motion Among Individuals with Myofascial Trigger Points: A Systematic Review and Meta-Analysis. Pain Med. 2022 Jan 3;23(1):137-143. doi: 10.1093/pm/pnab224. — View Citation

Hand DJ, Till RJ. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems.

Herrero JF, Laird JM, Lopez-Garcia JA. Wind-up of spinal cord neurones and pain sensation: much ado about something? Prog Neurobiol. 2000 Jun;61(2):169-203. doi: 10.1016/s0301-0082(99)00051-9. — View Citation

Hoheisel U, Rosner J, Mense S. Innervation changes induced by inflammation of the rat thoracolumbar fascia. Neuroscience. 2015 Aug 6;300:351-9. doi: 10.1016/j.neuroscience.2015.05.034. Epub 2015 May 21. — View Citation

Hughes EJ, McDermott K, Funk MF. Evaluation of hyaluronan content in areas of densification compared to adjacent areas of fascia. J Bodyw Mov Ther. 2019 Apr;23(2):324-328. doi: 10.1016/j.jbmt.2019.01.017. Epub 2019 Feb 5. No abstract available. — View Citation

Kendall RT, Feghali-Bostwick CA. Fibroblasts in fibrosis: novel roles and mediators. Front Pharmacol. 2014 May 27;5:123. doi: 10.3389/fphar.2014.00123. eCollection 2014. — View Citation

Kim H, Drake BL, Park H. Multiclass classifiers based on dimension reduction with generalized LDA. Pattern Recognit. 2007;40(11):2939-2945.

Kong F, Silverman RH, Liu L, Chitnis PV, Lee KK, Chen YC. Photoacoustic-guided convergence of light through optically diffusive media. Opt Lett. 2011 Jun 1;36(11):2053-5. doi: 10.1364/OL.36.002053. — View Citation

Krebs EE, Lorenz KA, Bair MJ, Damush TM, Wu J, Sutherland JM, Asch SM, Kroenke K. Development and initial validation of the PEG, a three-item scale assessing pain intensity and interference. J Gen Intern Med. 2009 Jun;24(6):733-8. doi: 10.1007/s11606-009-0981-1. Epub 2009 May 6. — View Citation

Kuan TS, Hsieh YL, Chen SM, Chen JT, Yen WC, Hong CZ. The myofascial trigger point region: correlation between the degree of irritability and the prevalence of endplate noise. Am J Phys Med Rehabil. 2007 Mar;86(3):183-9. doi: 10.1097/PHM.0b013e3180320ea7. — View Citation

Kumbhare D, Shaw S, Ahmed S, Noseworthy MD. Quantitative ultrasound of trapezius muscle involvement in myofascial pain: comparison of clinical and healthy population using texture analysis. J Ultrasound. 2020 Mar;23(1):23-30. doi: 10.1007/s40477-018-0330-5. Epub 2018 Nov 9. — View Citation

Kumbhare DA, Ahmed S, Behr MG, Noseworthy MD. Quantitative Ultrasound Using Texture Analysis of Myofascial Pain Syndrome in the Trapezius. Crit Rev Biomed Eng. 2018;46(1):1-31. doi: 10.1615/CritRevBiomedEng.2017024947. — View Citation

Langevin HM, Fox JR, Koptiuch C, Badger GJ, Greenan-Naumann AC, Bouffard NA, Konofagou EE, Lee WN, Triano JJ, Henry SM. Reduced thoracolumbar fascia shear strain in human chronic low back pain. BMC Musculoskelet Disord. 2011 Sep 19;12:203. doi: 10.1186/1471-2474-12-203. — View Citation

Langevin HM. Fascia Mobility, Proprioception, and Myofascial Pain. Life (Basel). 2021 Jul 8;11(7):668. doi: 10.3390/life11070668. — View Citation

Larsson B, Sogaard K, Rosendal L. Work related neck-shoulder pain: a review on magnitude, risk factors, biochemical characteristics, clinical picture and preventive interventions. Best Pract Res Clin Rheumatol. 2007 Jun;21(3):447-63. doi: 10.1016/j.berh.2007.02.015. — View Citation

Li M, Tang Y, Yao J. Photoacoustic tomography of blood oxygenation: A mini review. Photoacoustics. 2018 May 31;10:65-73. doi: 10.1016/j.pacs.2018.05.001. eCollection 2018 Jun. — View Citation

Li T, Zhu S, Ogihara M. Using discriminant analysis for multi-class classification: an experimental investigation. Knowl Inf Syst. 2006;10(4):453-472

Lucas N, Macaskill P, Irwig L, Moran R, Bogduk N. Reliability of physical examination for diagnosis of myofascial trigger points: a systematic review of the literature. Clin J Pain. 2009 Jan;25(1):80-9. doi: 10.1097/AJP.0b013e31817e13b6. — View Citation

Luedtke K, Allers A, Schulte LH, May A. Efficacy of interventions used by physiotherapists for patients with headache and migraine-systematic review and meta-analysis. Cephalalgia. 2016 Apr;36(5):474-92. doi: 10.1177/0333102415597889. Epub 2015 Jul 30. Erratum In: Cephalalgia. 2016 Jul;36(8):819-20. — View Citation

Lui HW, Chow KL. Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices. Inform Med Unlocked. 2018;13:26-33.

Mayoral Del Moral O, Torres Lacomba M, Russell IJ, Sanchez Mendez O, Sanchez Sanchez B. Validity and Reliability of Clinical Examination in the Diagnosis of Myofascial Pain Syndrome and Myofascial Trigger Points in Upper Quarter Muscles. Pain Med. 2018 Oct 1;19(10):2039-2050. doi: 10.1093/pm/pnx315. Erratum In: Pain Med. 2019 Aug 1;20(8):1644. — View Citation

Mc Loughlin S, Mc Loughlin MJ, Mateu F. Pulsed Doppler in simulated compartment syndrome: a pilot study to record hemodynamic compromise. Ochsner J. 2013 Winter;13(4):500-6. — View Citation

Menon RG, Oswald SF, Raghavan P, Regatte RR, Stecco A. T1rho-Mapping for Musculoskeletal Pain Diagnosis: Case Series of Variation of Water Bound Glycosaminoglycans Quantification before and after Fascial Manipulation(R) in Subjects with Elbow Pain. Int J Environ Res Public Health. 2020 Jan 22;17(3):708. doi: 10.3390/ijerph17030708. — View Citation

Menon RG, Raghavan P, Regatte RR. Quantifying muscle glycosaminoglycan levels in patients with post-stroke muscle stiffness using T1rho MRI. Sci Rep. 2019 Oct 10;9(1):14513. doi: 10.1038/s41598-019-50715-x. — View Citation

Mense S. Innervation of the thoracolumbar fascia. Eur J Transl Myol. 2019 Sep 6;29(3):8297. doi: 10.4081/ejtm.2019.8297. eCollection 2019 Aug 2. — View Citation

Mukherjee S, Tamayo P, Rogers S, Rifkin R, Engle A, Campbell C, Golub TR, Mesirov JP. Estimating dataset size requirements for classifying DNA microarray data. J Comput Biol. 2003;10(2):119-42. doi: 10.1089/106652703321825928. — View Citation

Ng W, Minasny B, Mendes W de S, Demattê JAM. The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. SOIL.2020;6(2):565-578.

Ou G, Murphey YL. Multi-class pattern classification using neural networks. Pattern Recognit.2007;40(1):4-18.

Park SY, Staicu AM. Longitudinal Functional Data Analysis. Stat (Int Stat Inst). 2015;4(1):212-226. doi: 10.1002/sta4.89. Epub 2015 Aug 24. — View Citation

Piehl-Aulin K, Laurent C, Engstrom-Laurent A, Hellstrom S, Henriksson J. Hyaluronan in human skeletal muscle of lower extremity: concentration, distribution, and effect of exercise. J Appl Physiol (1985). 1991 Dec;71(6):2493-8. doi: 10.1152/jappl.1991.71.6.2493. — View Citation

R. Rosenbaum P. A Matched Observational Study. In: Rosenbaum PR, ed. Design of Observational Studies. Springer Series in Statistics. Springer International Publishing; 2020:191-199. doi:10.1007/978-3- 030-46405-9_8

Raghavan P, Lu Y, Mirchandani M, Stecco A. Human Recombinant Hyaluronidase Injections For Upper Limb Muscle Stiffness in Individuals With Cerebral Injury: A Case Series. EBioMedicine. 2016 Jul;9:306-313. doi: 10.1016/j.ebiom.2016.05.014. Epub 2016 May 13. — View Citation

Rathbone ATL, Grosman-Rimon L, Kumbhare DA. Interrater Agreement of Manual Palpation for Identification of Myofascial Trigger Points: A Systematic Review and Meta-Analysis. Clin J Pain. 2017 Aug;33(8):715-729. doi: 10.1097/AJP.0000000000000459. — View Citation

Rolke R, Magerl W, Campbell KA, Schalber C, Caspari S, Birklein F, Treede RD. Quantitative sensory testing: a comprehensive protocol for clinical trials. Eur J Pain. 2006 Jan;10(1):77-88. doi: 10.1016/j.ejpain.2005.02.003. — View Citation

Rosenberger WF, Lachin JM. Randomization in Clinical Trials. John Wiley & Sons, Inc; 2015.doi:10.1002/9781118742112

Sanchez B, Rutkove SB. Present Uses, Future Applications, and Technical Underpinnings of Electrical Impedance Myography. Curr Neurol Neurosci Rep. 2017 Sep 20;17(11):86. doi: 10.1007/s11910-017-0793-3. — View Citation

Schmidt JL, Tweten DJ, Benegal AN, Walker CH, Portnoi TE, Okamoto RJ, Garbow JR, Bayly PV. Magnetic resonance elastography of slow and fast shear waves illuminates differences in shear and tensile moduli in anisotropic tissue. J Biomech. 2016 May 3;49(7):1042-1049. doi: 10.1016/j.jbiomech.2016.02.018. Epub 2016 Feb 15. — View Citation

Shah JP, Danoff JV, Desai MJ, Parikh S, Nakamura LY, Phillips TM, Gerber LH. Biochemicals associated with pain and inflammation are elevated in sites near to and remote from active myofascial trigger points. Arch Phys Med Rehabil. 2008 Jan;89(1):16-23. doi: 10.1016/j.apmr.2007.10.018. — View Citation

Shah JP, Phillips TM, Danoff JV, Gerber LH. An in vivo microanalytical technique for measuring the local biochemical milieu of human skeletal muscle. J Appl Physiol (1985). 2005 Nov;99(5):1977-84. doi: 10.1152/japplphysiol.00419.2005. Epub 2005 Jul 21. — View Citation

Shah JP, Thaker N, Heimur J, Aredo JV, Sikdar S, Gerber L. Myofascial Trigger Points Then and Now: A Historical and Scientific Perspective. PM R. 2015 Jul;7(7):746-761. doi: 10.1016/j.pmrj.2015.01.024. Epub 2015 Feb 24. — View Citation

Shankar H, Reddy S. Two- and three-dimensional ultrasound imaging to facilitate detection and targeting of taut bands in myofascial pain syndrome. Pain Med. 2012 Jul;13(7):971-5. doi: 10.1111/j.1526-4637.2012.01411.x. Epub 2012 Jun 8. — View Citation

Shiose K, Tanabe Y, Ohnishi T, Takahashi H. Effect of regional muscle damage and inflammation following eccentric exercise on electrical resistance and the body composition assessment using bioimpedance spectroscopy. J Physiol Sci. 2019 Nov;69(6):895-901. doi: 10.1007/s12576-019-00702-8. Epub 2019 Aug 6. — View Citation

Sikdar S, Ortiz R, Gebreab T, Gerber LH, Shah JP. Understanding the vascular environment of myofascial trigger points using ultrasonic imaging and computational modeling. Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5302-5. doi: 10.1109/IEMBS.2010.5626326. — View Citation

Sikdar S, Shah JP, Gebreab T, Yen RH, Gilliams E, Danoff J, Gerber LH. Novel applications of ultrasound technology to visualize and characterize myofascial trigger points and surrounding soft tissue. Arch Phys Med Rehabil. 2009 Nov;90(11):1829-38. doi: 10.1016/j.apmr.2009.04.015. — View Citation

Skootsky SA, Jaeger B, Oye RK. Prevalence of myofascial pain in general internal medicine practice. West J Med. 1989 Aug;151(2):157-60. — View Citation

Skovron ML. Epidemiology of low back pain. Baillieres Clin Rheumatol. 1992 Oct;6(3):559-73. doi: 10.1016/s0950-3579(05)80127-x. — View Citation

Sollmann N, Mathonia N, Weidlich D, Bonfert M, Schroeder SA, Badura KA, Renner T, Trepte-Freisleder F, Ganter C, Krieg SM, Zimmer C, Rummeny EJ, Karampinos DC, Baum T, Landgraf MN, Heinen F. Quantitative magnetic resonance imaging of the upper trapezius muscles - assessment of myofascial trigger points in patients with migraine. J Headache Pain. 2019 Jan 18;20(1):8. doi: 10.1186/s10194-019-0960-9. — View Citation

Song P, Manduca A, Trzasko JD, Chen S. Noise Equalization for Ultrafast Plane Wave Microvessel Imaging. IEEE Trans Ultrason Ferroelectr Freq Control. 2017 Nov;64(11):1776-1781. doi: 10.1109/TUFFC.2017.2748387. Epub 2017 Sep 1. — View Citation

Srbely JZ, Dickey JP, Bent LR, Lee D, Lowerison M. Capsaicin-induced central sensitization evokes segmental increases in trigger point sensitivity in humans. J Pain. 2010 Jul;11(7):636-43. doi: 10.1016/j.jpain.2009.10.005. Epub 2009 Dec 16. — View Citation

Srbely JZ, Dickey JP, Lee D, Lowerison M. Dry needle stimulation of myofascial trigger points evokes segmental anti-nociceptive effects. J Rehabil Med. 2010 May;42(5):463-8. doi: 10.2340/16501977-0535. — View Citation

Srbely JZ, Dickey JP, Montaholi Y. Experimentally Induced Central Sensitization Evokes Segmental Autonomic Responses in Humans. Int Phys Med Rehabil J. 2017;1(3):1-7

Srbely JZ. New trends in the treatment and management of myofascial pain syndrome. Curr Pain Headache Rep. 2010 Oct;14(5):346-52. doi: 10.1007/s11916-010-0128-4. — View Citation

Stecco A, Gesi M, Stecco C, Stern R. Fascial components of the myofascial pain syndrome. Curr Pain Headache Rep. 2013 Aug;17(8):352. doi: 10.1007/s11916-013-0352-9. — View Citation

Stecco A, Meneghini A, Stern R, Stecco C, Imamura M. Ultrasonography in myofascial neck pain: randomized clinical trial for diagnosis and follow-up. Surg Radiol Anat. 2014 Apr;36(3):243-53. doi: 10.1007/s00276-013-1185-2. Epub 2013 Aug 23. — View Citation

Stecco C, Pirri C, Fede C, Fan C, Giordani F, Stecco L, Foti C, De Caro R. Dermatome and fasciatome. Clin Anat. 2019 Oct;32(7):896-902. doi: 10.1002/ca.23408. Epub 2019 May 28. — View Citation

Stecco C, Stern R, Porzionato A, Macchi V, Masiero S, Stecco A, De Caro R. Hyaluronan within fascia in the etiology of myofascial pain. Surg Radiol Anat. 2011 Dec;33(10):891-6. doi: 10.1007/s00276-011-0876-9. Epub 2011 Oct 2. — View Citation

Stohrer M, Boucher Y, Stangassinger M, Jain RK. Oncotic pressure in solid tumors is elevated. Cancer Res. 2000 Aug 1;60(15):4251-5. — View Citation

Stratton P, Khachikyan I, Sinaii N, Ortiz R, Shah J. Association of chronic pelvic pain and endometriosis with signs of sensitization and myofascial pain. Obstet Gynecol. 2015 Mar;125(3):719-728. doi: 10.1097/AOG.0000000000000663. — View Citation

Thabtah F, Cowling P, Peng Y. MCAR: multi-class classification based on association rule. In: The 3rd/IEEE International Conference OnComputer Systems and Applications, 2005. ; 2005:33-.

Tough EA, White AR, Cummings TM, Richards SH, Campbell JL. Acupuncture and dry needling in the management of myofascial trigger point pain: a systematic review and meta-analysis of randomised controlled trials. Eur J Pain. 2009 Jan;13(1):3-10. doi: 10.1016/j.ejpain.2008.02.006. Epub 2008 Apr 18. — View Citation

Tough EA, White AR, Richards S, Campbell J. Variability of criteria used to diagnose myofascial trigger point pain syndrome--evidence from a review of the literature. Clin J Pain. 2007 Mar-Apr;23(3):278-86. doi: 10.1097/AJP.0b013e31802fda7c. — View Citation

Travell JG, Simons DG. Myofascial Pain and Dysfunction: The Trigger Point Manual. Lippincott Williams & Wilkins; 1983.

Tuckey B, Srbely J, Rigney G, Vythilingam M, Shah J. Impaired Lymphatic Drainage and Interstitial Inflammatory Stasis in Chronic Musculoskeletal and Idiopathic Pain Syndromes: Exploring a Novel Mechanism. Front Pain Res (Lausanne). 2021 Aug 23;2:691740. doi: 10.3389/fpain.2021.691740. eCollection 2021. — View Citation

Turo D, Otto P, Hossain M, Gebreab T, Armstrong K, Rosenberger WF, Shao H, Shah JP, Gerber LH, Sikdar S. Novel Use of Ultrasound Elastography to Quantify Muscle Tissue Changes After Dry Needling of Myofascial Trigger Points in Patients With Chronic Myofascial Pain. J Ultrasound Med. 2015 Dec;34(12):2149-61. doi: 10.7863/ultra.14.08033. Epub 2015 Oct 21. — View Citation

Turo D, Otto P, Shah JP, Heimur J, Gebreab T, Zaazhoa M, Armstrong K, Gerber LH, Sikdar S. Ultrasonic characterization of the upper trapezius muscle in patients with chronic neck pain. Ultrason Imaging. 2013 Apr;35(2):173-87. doi: 10.1177/0161734612472408. — View Citation

Tweten DJ, Okamoto RJ, Schmidt JL, Garbow JR, Bayly PV. Estimation of material parameters from slow and fast shear waves in an incompressible, transversely isotropic material. J Biomech. 2015 Nov 26;48(15):4002-4009. doi: 10.1016/j.jbiomech.2015.09.009. Epub 2015 Oct 9. — View Citation

Vabalas A, Gowen E, Poliakoff E, Casson AJ. Machine learning algorithm validation with a limited sample size. PLoS One. 2019 Nov 7;14(11):e0224365. doi: 10.1371/journal.pone.0224365. eCollection 2019. — View Citation

Wang Y, Rosenberger WF, Uschner D. Randomization tests for multiarmed randomized clinical trials. Stat Med. 2020 Feb 20;39(4):494-509. doi: 10.1002/sim.8418. Epub 2019 Dec 17. — View Citation

Woolf CJ. Central sensitization: implications for the diagnosis and treatment of pain. Pain. 2011 Mar;152(3 Suppl):S2-S15. doi: 10.1016/j.pain.2010.09.030. Epub 2010 Oct 18. — View Citation

Zhao W, Wu R. Wavelet-Based Nonparametric Functional Mapping of Longitudinal Curves. J Am Stat Assoc. 2008;103(482):714-725.

* Note: There are 104 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Ultrasound shear wave elastography	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.	Baseline, month 3
Primary	Ultrasound Doppler	Ultrasound Doppler estimates the flow velocity in blood vessels. We will extract end-diastolic velocity as the outcome measure.	Baseline, month 3
Primary	Bioimpedance spectroscopy	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.	Baseline, month 3
Primary	High density electromyography	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.	Baseline, month 3
Secondary	NIH HEAL Common data elements for adult chronic pain	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)	Baseline, month 3
Secondary	Windup ratio	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.	Baseline, month 3
Secondary	Pressure pain threshold	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.	Baseline, month 3
Secondary	Cervical and shoulder range of motion	We will quantify the asymmetry in range of motion (flexion/extension; side bending;rotation; abduction/adduction)using a marker less motion capture system.	Baseline, month 3
Secondary	Ecological Momentary Assessment	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	Month 1-3