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Original Article

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.

Fibromyalgia diagnostic model derived from combination of American College of Rheumatology 1990 and 2011 criteria

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: bidari.a@iums.ac.ir
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.

Abstract

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