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- 저자 : Ghalehbaghi, Babak (검색결과 3 건)
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Challenges in fibromyalgia diagnosis: from meaning of symptoms to fibromyalgia labeling
( Ali Bidari ),( Banafsheh Ghavidel Parsa ),( Babak Ghalehbaghi ) 대한통증학회 2018 The Korean Journal of Pain Vol.31 No.3
Fibromyalgia (FM) is a contested illness with ill-defined boundaries. There is no clearly defined cut-point that separates FM from non-FM. Diagnosis of FM has been faced with several challenges that occur, including patients’ health care-seeking behavior, symptoms recognition, and FM labeling by physicians. This review focuses on important but less visible factors that have a profound influence on under- or over-diagnosis of FM. FM shows different phenotypes and disease expression in patients and even in one patient over time. Psychosocial and cultural factors seem to be a contemporary ferment in FM which play a major role in physician diagnosis even more than having severe symptom levels in FM patients. Although the FM criteria are the only current methods which can be used for classification of FM patients in surveys, research, and clinical settings, there are several key pieces missing in the fibromyalgia diagnostic puzzle, such as invalidation, psychosocial factors, and heterogeneous disease expression. Regarding the complex nature of FM, as well as the arbitrary and illusory constructs of the existing FM criteria, FM diagnosis frequently fails to provide a clinical diagnosis fit to reality. A physicians’ judgment, obtained in real communicative environments with patients, beyond the existing constructional scores, seems the only reliable way for more valid diagnoses. It plays a pivotal role in the meaning and conceptualization of symptoms and psychosocial factors, making diagnoses and labeling of FM. It is better to see FM as a whole, not as a medical specialty or constructional scores. (Korean J Pain 2018; 31: 147-54)
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Banafsheh Ghavidel-Parsa,Ali Bidari,Asghar Hajiabbasi,Irandokht Shenavar,Babak Ghalehbaghi,Omid Sanaei 대한통증학회 2019 The Korean Journal of Pain Vol.32 No.2
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 dis-criminant 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.
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Ghavidel-Parsa, Banafsheh,Bidari, Ali,Hajiabbasi, Asghar,Shenavar, Irandokht,Ghalehbaghi, Babak,Sanaei, Omid The Korean Pain Society 2019 The Korean Journal of Pain Vol.32 No.2
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.
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