Korean J Pain 2019; 32(2): 120-128
Published online April 1, 2019 https://doi.org/10.3344/kjp.2019.32.2.120
Copyright © The Korean Pain Society.
Banafsheh Ghavidel-Parsa1, Ali Bidari2, Asghar Hajiabbasi1, Irandokht Shenavar1, Babak Ghalehbaghi3, and Omid Sanaei4
1Rheumatology Research Center, Razi Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, 2Department of Rheumatology, Iran University of Medical Sciences, Tehran, 3ENT and Head and Neck Research Center and Department, Iran University of Medical Sciences, Tehran, Iran, 4Division of Gastroenterology and Hepatology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
Correspondence to:Ali Bidari, Department of Rheumatology, Iran University of Medical Sciences, Hazarat Rasoul Medical Complex, Sattarkhan Ave, Tehran 41448-95655, Iran, Tel: ＋98-91-2327-7847, Fax: ＋98-21-6652-5327, E-mail: firstname.lastname@example.org, ORCID: https://orcid.org/0000-0002-3583-1838
Received: November 7, 2018; Revised: March 2, 2019; Accepted: March 4, 2019
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background: We aimed to explore the American College of Rheumatology (ACR) 1990 and 2011 fibromyalgia (FM) classification criteria’s items and the components of Fibromyalgia Impact Questionnaire (FIQ) to identify features best discriminating FM features. Finally, we developed a combined FM diagnostic (C-FM) model using the FM’s key features.
Methods: The means and frequency on tender points (TPs), ACR 2011 components and FIQ items were calculated in the FM and non-FM (osteoarthritis [OA] and non-OA) patients. Then, two-step multiple logistic regression analysis was performed to order these variables according to their maximal statistical contribution in predicting group membership. Partial correlations assessed their unique contribution, and two-group discriminant analysis provided a classification table. Using receiver operator characteristic analyses, we determined the sensitivity and specificity of the final model.
Results: A total of 172 patients with FM, 75 with OA and 21 with periarthritis or regional pain syndromes were enrolled. Two steps multiple logistic regression analysis identified 8 key features of FM which accounted for 64.8% of variance associated with FM group membership: lateral epicondyle TP with variance percentages (36.9%), neck pain (14.5%), fatigue (4.7%), insomnia (3%), upper back pain (2.2%), shoulder pain (1.5%), gluteal TP (1.2%), and FIQ fatigue (0.9%). The C-FM model demonstrated a 91.4% correct classification rate, 91.9% for sensitivity and 91.7% for specificity.
Conclusions: The C-FM model can accurately detect FM patients among other pain disorders. Re-inclusion of TPs along with saving of FM main symptoms in the C-FM model is a unique feature of this model.
Keywords: Chronic pain, Discriminant analysis, Fatigue, Fibromyalgia, Logistic models, Neck pain, Osteoarthritis, Periarthritis, Sensitivity and specificity, Shoulder pain, Sleep initiation and maintenance disorders, Survey and questionnaires.