Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia

NCT04088747 | Completed

• 1 other identifier: FibromyalgiaDiagnosis
• Study Type: observational
• Target: 81
• Locations: 1 country
• Sites: 1

Brief Summary

This study will utilize ultrasound image texture variables to construct an elastic net regularized, logistic regression model to differentiate between healthy and Fibromyalgia patients. The collected ultrasound data will be from participants who are healthy, and from participants who have Fibromyalgia. The predicted performance accuracy of the diagnostic model will be validated and this will confirm or deny the hypothesis that differentiation between the two cohorts is possible.

Conditions

Fibromyalgia

Keywords

Fibromyalgia; Ultrasound; Regularization; Logistic Regression; Imaging

Outcome Measures

Primary Outcomes (3)

• Ultrasound Image Texture Variables

  91 statistical image texture variables are extracted from the B mode ultrasound images from both cohorts in order to construct a diagnostic model. The texture variables will be extracted using MATLAB.

  1 hour

• Fibromyalgia Diagnostic Criteria

  This evaluates symptoms related to Fibromyalgia and determines a score to assess the severity. This score is comprised of the Widespread Pain Index(WPI), which quantifies the regions of pain, and the Symptom Severity Scale(SSS), which measures qualitative aspects of pain such as fatigue and cognitive symptoms. The WPI scale ranges from 0-19 (0- no areas of body pain, 19- all body regions have pain), whereas the SSS ranges from 0-12 (0-no qualitative aspects of pain, 12-many qualitative aspects of pain). This criteria was evaluated on each patient to determine which cohort they belong to. According to the Fibromyalgia Diagnostic Criteria, one is diagnosed with Fibromyalgia if they have a WPI score of 7 or higher, and a SSS score of 5 or higher. Fibromyalgia is also diagnosed with a score of 3-6 on the WPI score, and a score of 9 or higher on the SSS score.

  10 minutes

• Central Sensitization Inventory

  This is a self reported outcome measure designed to identify patients that experience central sensitization. It involves 25 questions which include symptomatic experiences. The subject must answer on a scale of 0(never) to 5(always) corresponding to how often they experience these. The maximum score is 100 and a score of more than 40 indicates the presence of Central Sensitization. This criteria was evaluated on each patient to determine which cohort they belong to.

  10 minutes

Study Arms (2)

Fibromyalgia

Patients who display symptoms and have a history of Fibromyalgia, between 20-65 years of age.

Diagnostic Test: Ultrasound Imaging

Healthy Controls

Age-matched, healthy controls, between 20-65 years of age who present no signs of chronic pain.

Diagnostic Test: Ultrasound Imaging

Interventions

Ultrasound Imaging DIAGNOSTIC_TEST

B-mode ultrasound pictures of the upper Trapezius were collected from both left and right sides.

Fibromyalgia; Healthy Controls

Eligibility Criteria

• Age: 20 Years - 65 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients diagnosed with Fibromyalgia and healthy age-matched controls.

You may qualify if:

• gender independent; chronic widespread pain, fitting the 2016 FM criteria, absence of myofascial pain syndrome trigger points and between the ages of 20 and 65 years (44.3 ± 13.9 years).
• Healthy asymptomatic volunteers who were age matched (n = 17) with no physical complaints or abnormality on physical examination also participated.

You may not qualify if:

• Participants were excluded if they demonstrated clinical evidence of another cause for widespread pain, such as polymyositis, dermatomyositis, endocrine disorders, etc. None of the participants had performed any physical exercise during the two to three days prior to entry into the study.

Sponsors & Collaborators

• Toronto Rehabilitation Institute: lead

Study Sites (1)

Toronto Rehabilitation Institute

Toronto, Ontario, M5G2A2, Canada

Location

Related Publications (22)

• Wolfe F, Ross K, Anderson J, Russell IJ, Hebert L. The prevalence and characteristics of fibromyalgia in the general population. Arthritis Rheum. 1995 Jan;38(1):19-28. doi: 10.1002/art.1780380104.

  PMID: 7818567 BACKGROUND

• Gittins R, Howard M, Ghodke A, Ives TJ, Chelminski P. The Accuracy of a Fibromyalgia Diagnosis in General Practice. Pain Med. 2018 Mar 1;19(3):491-498. doi: 10.1093/pm/pnx155.

  PMID: 29016895 BACKGROUND

• Schaefer C, Mann R, Masters ET, Cappelleri JC, Daniel SR, Zlateva G, McElroy HJ, Chandran AB, Adams EH, Assaf AR, McNett M, Mease P, Silverman S, Staud R. The Comparative Burden of Chronic Widespread Pain and Fibromyalgia in the United States. Pain Pract. 2016 Jun;16(5):565-79. doi: 10.1111/papr.12302. Epub 2015 May 16.

  PMID: 25980433 BACKGROUND

• Wolfe F, Clauw DJ, Fitzcharles MA, Goldenberg DL, Hauser W, Katz RL, Mease PJ, Russell AS, Russell IJ, Walitt B. 2016 Revisions to the 2010/2011 fibromyalgia diagnostic criteria. Semin Arthritis Rheum. 2016 Dec;46(3):319-329. doi: 10.1016/j.semarthrit.2016.08.012. Epub 2016 Aug 30.

  PMID: 27916278 BACKGROUND

• Ablin JN, Wolfe F. A Comparative Evaluation of the 2011 and 2016 Criteria for Fibromyalgia. J Rheumatol. 2017 Aug;44(8):1271-1276. doi: 10.3899/jrheum.170095. Epub 2017 Jun 1.

  PMID: 28572464 BACKGROUND

• U.S. Department of Health and Human Services Food and Drug Administration/Centre for Drug Evaluation and Research. Guidance for Industry and FDA Staff Qualification Process for Drug Development Tools. Silver Spring, MD: Author; 2014

  BACKGROUND

• Kravis MM, Munk PL, McCain GA, Vellet AD, Levin MF. MR imaging of muscle and tender points in fibromyalgia. J Magn Reson Imaging. 1993 Jul-Aug;3(4):669-70. doi: 10.1002/jmri.1880030418.

  PMID: 8347962 BACKGROUND

• Meenagh G, Sakellariou G, Iagnocco A, Delle Sedie A, Riente L, Filippucci E, Di Geso L, Grassi W, Bombardieri S, Valesini G, Montecucco C. Ultrasound imaging for the rheumatologist XXXIX. Sonographic assessment of the hip in fibromyalgia patients. Clin Exp Rheumatol. 2012 May-Jun;30(3):319-21. Epub 2012 Jun 26.

  PMID: 22734970 BACKGROUND

• Bendtsen L, Norregaard J, Jensen R, Olesen J. Evidence of qualitatively altered nociception in patients with fibromyalgia. Arthritis Rheum. 1997 Jan;40(1):98-102. doi: 10.1002/art.1780400114.

  PMID: 9008605 BACKGROUND

• 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.

  PMID: 29717675 BACKGROUND

• MathWorks. Image Processing Toolbox., Release 2018a, The MathWorks Inc.,Natick, Massachusetts, United States

  BACKGROUND

• Sampat MP, Wang Z, Gupta S, Bovik AC, Markey MK. Complex wavelet structural similarity: a new image similarity index. IEEE Trans Image Process. 2009 Nov;18(11):2385-401. doi: 10.1109/TIP.2009.2025923. Epub 2009 Jun 23.

  PMID: 19556195 BACKGROUND

• Behr M, Noseworthy M, Kumbhare D. Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case-Control Study. J Ultrasound Med. 2019 Aug;38(8):2119-2132. doi: 10.1002/jum.14909. Epub 2019 Jan 7.

  PMID: 30614553 BACKGROUND

• Haralick, R. M., & Shanmugam, K. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics. 1973;SMC-3(6):610-621.

  BACKGROUND

• Galloway, M. M. Texture classification using gray level run length. Computer graphics and image processing. 1975;4(2):172-179.

  BACKGROUND

• Zou, H., & Hastie, T. Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology) 2005;67(2):301-320.

  BACKGROUND

• MathWorks. Statistics and Machine Learning Toolbox., Release 2018a, The MathWorks Inc.,Natick, Massachusetts, United States

  BACKGROUND

• Jalalian A, Mashohor SB, Mahmud HR, Saripan MI, Ramli AR, Karasfi B. Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging. 2013 May-Jun;37(3):420-6. doi: 10.1016/j.clinimag.2012.09.024. Epub 2012 Nov 13.

  PMID: 23153689 BACKGROUND

• Virmani, J., Kumar, V., Kalra, N., & Khandelwal, N. Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound. International Journal of Convergence Computing 2013;1(1):19-37.

  BACKGROUND

• Xian, G. M. An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Systems with Applications 2010;37(10):6737-6741.

  BACKGROUND

• Bishop, C. M. Pattern recognition and machine learning. New York, NY: Springer-Verlag: 2006. p. 205-207.

  BACKGROUND

• Sarle, W. S. Stopped training and other remedies for overfitting. Computing science and statistics, 1996:352-360.

  BACKGROUND

MeSH Terms

Conditions

Fibromyalgia

Interventions

Ultrasonography

Condition Hierarchy (Ancestors)

Muscular Diseases; Musculoskeletal Diseases; Rheumatic Diseases; Neuromuscular Diseases; Nervous System Diseases

Intervention Hierarchy (Ancestors)

Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Dinesh Kumbhare, MD,PhD

  Toronto Rehabilitation Institute

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Affiliate Scientist

Study Record Dates

• First Submitted: September 11, 2019
• First Posted: September 13, 2019
• Study Start: September 1, 2018
• Primary Completion: September 6, 2019
• Study Completion: September 6, 2019
• Last Updated: September 17, 2019

Record last verified: 2019-09

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)