Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia

NCT ID: NCT04088747

Last Updated: 2019-09-17

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 81 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-09-01
• Study Completion Date: 2019-09-06

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.

Conditions

Fibromyalgia

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: RETROSPECTIVE

Study Groups

Fibromyalgia

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

Ultrasound Imaging

Intervention Type: DIAGNOSTIC_TEST

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

Healthy Controls

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

Ultrasound Imaging

Intervention Type: DIAGNOSTIC_TEST

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

Interventions

Ultrasound Imaging

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

Intervention Type: DIAGNOSTIC_TEST

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.

• Minimum Eligible Age: 20 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Toronto Rehabilitation Institute

OTHER

Sponsor Role: lead

Responsible Party

Dinesh Kumbhare

Affiliate Scientist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Dinesh Kumbhare, MD,PhD

Role: PRINCIPAL_INVESTIGATOR

Toronto Rehabilitation Institute

Locations

Toronto Rehabilitation Institute

Toronto, Ontario, Canada

Countries

Canada

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Provided Documents

Document Type: Study Protocol

View Document

Other Identifiers

FibromyalgiaDiagnosis

Identifier Type: -

Identifier Source: org_study_id