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CompletedNCT04088747LEDFUpdated Sep 17, 2019

Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia

An observational study in Fibromyalgia, sponsored by Toronto Rehabilitation Institute. Completed at 1 site in Canada. Open to participants aged 20 Years to 65 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2019-09-17.

Sponsored by Toronto Rehabilitation Institute · Observational

Study type
Observational
Model
Case-control
Time perspective
Retrospective
Enrollment
81
Ages
20 Years to 65 Years
Sex
All
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Study 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.

Read the detailed description

Fibromyalgia (FM) diagnosis remains a challenge for clinicians due to a lack of objective diagnostic tools. One proposed solution is the use of quantitative ultrasound (US) techniques, such as image texture analysis, which has demonstrated discriminatory capabilities with other chronic pain conditions. The investigators propose the use of US image texture variables to construct an elastic net regularized, logistic regression model, for differentiating between the trapezius muscle in the healthy and FM patients. 162 Ultrasound videos of the right and left trapezius muscle were acquired from healthy participants and participants with FM. The videos will then be put through a mutli-step processing pipe including converting them into skeletal muscle regions of interest (ROI). The ROI's will be then filtered by an algorithm utilizing the complex wavelet structural similarity index (CW-SSIM), which removes ROI's that are too similar to one another. Eighty-eight texture variables will be extracted from the ROI's, which will be used in nested cross-validation to construct a logistic regression model with and without elastic net regularization. The generalized performance accuracy of both models will be estimated and confirmed with a final validation on a holdout test set. Depending on the predicted, generalized performance accuracy it will be validated or not by the final, holdout test set (confirming the model construction is accurate). These models should then confirm or deny the hypothesis that a regularized logistic regression model built on ultrasound texture features can accurately differentiate between healthy trapezius muscle and that of patients with FM.

02

Conditions studied

  • Fibromyalgia

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Keywords

  • Fibromyalgia
  • Ultrasound
  • Regularization
  • Logistic Regression
  • Imaging
03

In context

Fibromyalgia

1,369 studies on the registry are indexed under Fibromyalgia; 272 are open to participants now.

This study's enrollment of 81 is below the median of 100 across 282 observational studies indexed under Fibromyalgia.

Browse Fibromyalgia studies →

Lead sponsor

Toronto Rehabilitation Institute is the lead sponsor of 40 studies on the registry; 5 are open to participants now.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
20 Years to 65 Years
Sexes eligible
All
Accepts healthy volunteers
Yes
Sampling method
Non-probability sample

Study population

Patients diagnosed with Fibromyalgia and healthy age-matched controls.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • 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.

Exclusion criteria

Exclusion Criteria:

  • 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.
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Study design

Observational model
Case-control
Time perspective
Retrospective
Enrollment
81 participants (actual)
Patient registry
No

Groups and cohorts

  • 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

  • Diagnostic testUltrasound Imaging

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

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What researchers measure

Primary outcomes

  1. 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.

    Time frame: 1 hour

  2. 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.

    Time frame: 10 minutes

  3. 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.

    Time frame: 10 minutes

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Study locations

1 site
  • Toronto Rehabilitation Institute
    Toronto, Ontario M5G2A2, Canada
08

References and documents

Publications

  • 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. PubMed 7818567 ↗
  • 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. PubMed 29016895 ↗
  • 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. PubMed 25980433 ↗
  • 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. PubMed 27916278 ↗
  • 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. PubMed 28572464 ↗
  • 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
  • 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. PubMed 8347962 ↗
  • 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. PubMed 22734970 ↗
  • 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. PubMed 9008605 ↗
  • 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. PubMed 29717675 ↗
  • MathWorks. Image Processing Toolbox., Release 2018a, The MathWorks Inc.,Natick, Massachusetts, United States
  • 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. PubMed 19556195 ↗
  • 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. PubMed 30614553 ↗
  • Haralick, R. M., & Shanmugam, K. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics. 1973;SMC-3(6):610-621.
  • Galloway, M. M. Texture classification using gray level run length. Computer graphics and image processing. 1975;4(2):172-179.
  • 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.
  • MathWorks. Statistics and Machine Learning Toolbox., Release 2018a, The MathWorks Inc.,Natick, Massachusetts, United States
  • 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. PubMed 23153689 ↗
  • 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.
  • 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.
  • Bishop, C. M. Pattern recognition and machine learning. New York, NY: Springer-Verlag: 2006. p. 205-207.
  • Sarle, W. S. Stopped training and other remedies for overfitting. Computing science and statistics, 1996:352-360.

Study documents

  • Study protocol · Sep 9, 2019

Documents are hosted by the registry — open the source record to download them.

Individual participant data

Plan to share: No

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Ran from Sep 2018 to primary completion in Sep 2019, 12 months.

How these figures are worked out, and which studies are left out of them.

Registered
Sep 11, 2019
First posted
Sep 13, 2019
StartActual
Sep 1, 2018
Primary completionActual
Sep 6, 2019
CompletionActual
Sep 6, 2019
Last update
Sep 17, 2019
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Sep 17, 2019, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT04088747
Lead sponsor
Toronto Rehabilitation Institute
Responsible party
Dinesh Kumbhare (Affiliate Scientist, Toronto Rehabilitation Institute) — Principal investigator
First posted
Sep 13, 2019
Start date
Sep 1, 2018
Primary completion
Sep 6, 2019
Completion
Sep 6, 2019
Last update
Sep 17, 2019

Study contacts

Dinesh Kumbhare, MD,PhD
principal investigator · Toronto Rehabilitation Institute

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is completed, as verified in Sep 2019. You cannot join it, but the record below documents what was studied.

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