Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia (LEDF)
Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia: A Quantitative Approach Using B-Mode Ultrasound
Studienübersicht
Status
Status
Bedingungen
Bedingungen
Intervention / Behandlung
Intervention / Behandlung
Detaillierte Beschreibung
Studientyp
Studientyp
Einschreibung (Tatsächlich)
Einschreibung
Kontakte und Standorte
Studienorte
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Ontario
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Toronto, Ontario, Kanada, M5G2A2
- Toronto Rehabilitation Institute
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Teilnahmekriterien
Zulassungskriterien
Zulassungskriterien
Studienberechtigtes Alter
Akzeptiert gesunde Freiwillige
Studienberechtigte Geschlechter
Probenahmeverfahren
Studienpopulation
Beschreibung
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.
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
Anzahl der Gruppen / Kohorten
Kohorten und Interventionen
Gruppe / KohorteGruppe / Kohorte |
Intervention / BehandlungIntervention / Behandlung |
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Fibromyalgia
Patients who display symptoms and have a history of Fibromyalgia, between 20-65 years of age.
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B-mode ultrasound pictures of the upper Trapezius were collected from both left and right sides.
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Healthy Controls
Age-matched, healthy controls, between 20-65 years of age who present no signs of chronic pain.
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B-mode ultrasound pictures of the upper Trapezius were collected from both left and right sides.
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Was misst die Studie?
Primäre Ergebnismessungen
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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Ultrasound Image Texture Variables
Zeitfenster: 1 hour
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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.
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1 hour
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Fibromyalgia Diagnostic Criteria
Zeitfenster: 10 minutes
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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.
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10 minutes
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Central Sensitization Inventory
Zeitfenster: 10 minutes
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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.
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10 minutes
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Mitarbeiter und Ermittler
Sponsor
Sponsor
Ermittler
Ermittler
- Hauptermittler: Dinesh Kumbhare, MD,PhD, Toronto Rehabilitation Institute
Publikationen und hilfreiche Links
Allgemeine Veröffentlichungen
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Tatsächlich)
Studienbeginn
Primärer Abschluss (Tatsächlich)
Primärer Abschluss
Studienabschluss (Tatsächlich)
Studienabschluss
Studienanmeldedaten
Zuerst eingereicht
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Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Zuerst gepostet
Studienaufzeichnungsaktualisierungen
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Letztes Update gepostet
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Schlüsselwörter
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
Andere Studien-ID-Nummern
- FibromyalgiaDiagnosis
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