Background: Behçet's syndrome (BS) is a rare multi-systemic vasculitis of unknown aetiology. Fibromyalgia syndrome (FMS) is more prevalent in rheumatological conditions such-as BS, than the general population. However, there is limited research into the aetiology and characteristics of pain in BS.
Objectives: To describe the pain characteristics and incidence of FMS in people with BS and investigate their relationship with BS disease activity.
Methods: A cohort study of BS patients attending the Liverpool Behçet's Centre between February 2017 and March 2019. BS was defined using the International Study Group Criteria. BS severity was assessed using the Behçet's Disease Current Activity Form. FMS was determined from consultant diagnosis. Assessments of pain included: Pain Visual Analogue Scale (PVAS), Pain Mannequin, Brief Pain Inventory, EQ-5D-3L and Short Form McGill. Pain and FMS prevalence were compared between high and low disease activity.
Results: 90% reported moderate-severe pain with a median PVAS score of 68/100 [38, 81]. 35.6% of participants had FMS and 46.5% experienced generalized pain. 76% of participants with high disease activity reported severe pain, compared to 39.1% with low disease activity ( = .003). Pain was more generalised in high disease activity (72%) compared to low disease activity (37.7%) ( = .003). FMS was more prevalent in the high disease activity group (52%) than the low disease activity group (29%) ( = .04).
Conclusions: This is the first study to explore pain in participants with BS in the United Kingdom. The majority of BS patients experience moderate-severe widespread pain. Severe widespread pain is more prevalent in those with high disease activity. We have demonstrated a relationship between high disease activity, worse pain intensity, and FMS. This paper contributes to the understanding of two conditions which remain to be fully understood, FMS and BS, and generates new hypotheses to describe the interplay between.
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http://dx.doi.org/10.1177/20494637231198200 | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFACS Appl Mater Interfaces
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Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou, Guangdong 510060, P. R. China.
The clinical application of tyrosine kinase inhibitors (TKIs) is rapidly growing and has emerged as a cornerstone in the treatment of both solid tumors and hematologic malignancies. However, resistance to TKI targets and disease progression remain inevitable. Nanocarrier-mediated delivery has emerged as a promising strategy to overcome the limitations of the TKI application.
View Article and Find Full Text PDFJ Med Internet Res
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Bioorganic Chemistry Laboratory, New Chemistry Unit, Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Jakkur P.O., Bengaluru 560064, Karnataka, India.
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View Article and Find Full Text PDFMenopause
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Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
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