The Korean Journal of Pain

The Korean Pain Society (대한통증학회)
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Fibromyalgia diagnostic model derived from combination of American College of Rheumatology 1990 and 2011 criteria

  • Ghavidel-Parsa, Banafsheh (Rheumatology Research Center, Razi Hospital, School of Medicine, Guilan University of Medical Sciences) ;
  • Bidari, Ali (Department of Rheumatology, Iran University of Medical Sciences) ;
  • Hajiabbasi, Asghar (Rheumatology Research Center, Razi Hospital, School of Medicine, Guilan University of Medical Sciences) ;
  • Shenavar, Irandokht (Rheumatology Research Center, Razi Hospital, School of Medicine, Guilan University of Medical Sciences) ;
  • Ghalehbaghi, Babak (ENT and Head and Neck Research Center and Department, Iran University of Medical Sciences) ;
  • Sanaei, Omid (Division of Gastroenterology and Hepatology, Johns Hopkins Medical Institutions)
  • Received : 2018.11.07
  • Accepted : 2019.03.04
  • Published : 2019.04.01
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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

References

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