FibromyalgiaLEDFNCT04088747
Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia
An observational study of Fibromyalgia, run by Toronto Rehabilitation Institute at 1 site in Canada.
Trial 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. Read more about this study
- Who is it for?
- Ages 20 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Fibromyalgia
- What is being tested?
- Ultrasound Imaging
- How many people
- 81 participants
- Where
- 1 siteCanada
- Study dates
- 1 Sep 2018to 6 Sep 2019
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients diagnosed with Fibromyalgia and healthy age-matched controls.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia: A Quantitative Approach Using B-Mode Ultrasound
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.
- Conditions
- Fibromyalgia
- Being tested
- Ultrasound Imaging
- Keywords
- Fibromyalgia, Ultrasound, Regularization, Logistic Regression, Imaging
- Medical subject headings
- Fibromyalgia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ultrasound ImagingDiagnostic test
B-mode ultrasound pictures of the upper Trapezius were collected from both left and right sides.
| Study group | Role | What they receive |
|---|---|---|
| Fibromyalgia | Patients who display symptoms and have a history of Fibromyalgia, between 20-65 years of age. Receives: Diagnostic Test: Ultrasound Imaging | |
| Healthy Controls | Age-matched, healthy controls, between 20-65 years of age who present no signs of chronic pain. Receives: Diagnostic Test: Ultrasound Imaging |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 10 minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Sep 2019
- Study start1 Sep 2018
- Primary completion6 Sep 2019
- Study completion6 Sep 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (22)Papers the sponsor has linked to this study.
- Other trials for FibromyalgiaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 81 participants (actual)
Sponsor and investigators
- Lead sponsor
- Toronto Rehabilitation Institute
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Dinesh Kumbhare, Toronto Rehabilitation Institute
- Investigators
- Dinesh Kumbhare, MD,PhD, Principal investigator, Toronto Rehabilitation Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04088747
- Sponsor's protocol ID
- FibromyalgiaDiagnosis
- Acronym
- LEDF
- Status last verified
- Sep 2019
- First submitted
- 11 Sep 2019
- Last updated
- 17 Sep 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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 7818567Gittins 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 29016895Schaefer 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 25980433Wolfe 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 27916278Ablin 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 28572464U.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 8347962Meenagh 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 22734970Bendtsen 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 9008605Kumbhare 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 29717675MathWorks. 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 19556195Behr 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 30614553Haralick, 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.
Study at a glance
From the registry record, last updated by the sponsor on 17 Sep 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia: A Quantitative Approach Using B-Mode Ultrasound
- ClinicalTrials.gov ID
- NCT04088747
- Acronym / sponsor ID
- LEDF / FibromyalgiaDiagnosis
- Conditions
- Fibromyalgia
- Keywords
- Fibromyalgia, Ultrasound, Regularization, Logistic Regression, Imaging
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 81 participants (actual)
- Lead sponsor
- Toronto Rehabilitation InstituteUniversity, hospital or other
- Study start
- 1 Sep 2018 (actual)
- Primary completion
- 6 Sep 2019 (actual)
- Study completion
- 6 Sep 2019 (actual)
- First posted
- 13 Sep 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Fibromyalgia | – | Diagnostic Test: Ultrasound ImagingPatients who display symptoms and have a history of Fibromyalgia, between 20-65 years of age. |
| Healthy Controls | – | Diagnostic Test: Ultrasound ImagingAge-matched, healthy controls, between 20-65 years of age who present no signs of chronic pain. |
Who may qualify
- Ages
- 20 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients diagnosed with Fibromyalgia and healthy age-matched controls.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (1 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Ultrasound ImagingB-mode ultrasound pictures of the upper Trapezius were collected from both left and right sides. | Diagnostic test | Fibromyalgia, Healthy Controls |
Primary outcome
Ultrasound Image Texture Variables
Time frame: 1 hour
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.
Fibromyalgia Diagnostic Criteria
Time frame: 10 minutes
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.
Central Sensitization Inventory
Time frame: 10 minutes
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Sep 2019.
- Background: PubMed 7818567Wolfe 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.
- Background: PubMed 29016895Gittins 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.
- Background: PubMed 25980433Schaefer 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.
- Background: PubMed 27916278Wolfe 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.
- Background: PubMed 28572464Ablin 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.
- BackgroundU.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: PubMed 8347962Kravis 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.
- Background: PubMed 22734970Meenagh 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.
- Background: PubMed 9008605Bendtsen 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.
- Background: PubMed 29717675Kumbhare 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.
- BackgroundMathWorks. Image Processing Toolbox., Release 2018a, The MathWorks Inc.,Natick, Massachusetts, United States
- Background: PubMed 19556195Sampat 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Dinesh Kumbhare, MD,PhD principal investigator
Toronto Rehabilitation Institute
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Fibromyalgia, run by Toronto Rehabilitation Institute at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 Sep 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Ultrasound Image Texture Variables (time frame: 1 hour). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04088747, which also lists 22 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Sep 2019.