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Scand J Rheumatol ; 45(6): 461-469, 2016 Nov.
Article in English | MEDLINE | ID: mdl-26987470

ABSTRACT

BACKGROUND: The painDETECT questionnaire (PDQ) is a mechanism-based pain classification tool assigning patients to one of three categories depending on the quality of the experienced pain. Patients with non-nociceptive pain score high on the PDQ. The objective was to assess the proportions of the three PDQ classification groups in patients with rheumatoid arthritis (RA) and to explore differences in clinical characteristics. METHOD: RA patients initiating or escalating their RA therapy were included prospectively and underwent a thorough examination programme. Low (PDQ score < 13), medium (PDQ score 13-18), and high (PDQ score > 18) scores indicate nociceptive, unclear/possible neuropathic, or neuropathic pain mechanisms, respectively. RESULTS: The 102 included patients were classified into the following PDQ classification groups: low = 65%, medium = 23%, and high = 12%. Patients in the medium and high PDQ groups scored worse on indicators of anxiety, depression, disability, mental health-related quality of life, pain, and fatigue. They also had more tender points and an RA disease activity score based on 28 joints (DAS28) where a higher fraction of the composite score pertained to non-inflammatory factors compared to patients in the low PDQ classification group. There were no differences in objective inflammatory indices across groups. Multiple regression analysis demonstrated that the tender joint count (TJC) and the 36-item Short Form Health Survey (SF36) mental component summary (MCS) score were independently associated with the PDQ score. CONCLUSIONS: In patients initiating or intensifying medical treatment for their RA, non-nociceptive pain (PDQ score ≥ 13) is common. In these patients, the pain mechanisms result in increased disease activity scores on a non-inflammatory basis.


Subject(s)
Arthritis, Rheumatoid , Nociception , Pain Measurement , Pain/classification , Severity of Illness Index , Adult , Aged , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Regression Analysis
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