Clinical trial NCT04088747: Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia
Official title: Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia: A Quantitative Approach Using B-Mode Ultrasound
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Fibromyalgia
Keywords: Fibromyalgia
Ultrasound
Regularization
Logistic Regression
Imaging
Lead sponsor: Toronto Rehabilitation Institute
Sponsor class: OTHER
First posted: 2019-09-13
Enrollment: 81
Sex: ALL
Minimum age (years): 20
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients diagnosed with Fibromyalgia and healthy age-matched controls.

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.

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.

Eligibility criteria: 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:

* 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.

Primary outcomes:
measure: Ultrasound Image Texture Variables
timeFrame: 1 hour
measure: Fibromyalgia Diagnostic Criteria
timeFrame: 10 minutes
measure: Central Sensitization Inventory
timeFrame: 10 minutes
Secondary outcomes:
Other outcomes:
Locations:
city: Toronto
country: Canada
facility: Toronto Rehabilitation Institute
state: Ontario
Study officials:
affiliation: Toronto Rehabilitation Institute
name: Dinesh Kumbhare, MD,PhD
role: PRINCIPAL_INVESTIGATOR
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