• Kehinde Akin-Akinyosoye, Nadia Frowd, Laura Marshall, Joanne Stocks, Gwen S Fernandes, Ana Valdes, Daniel F McWilliams, Weiya Zhang, Michael Doherty, Eamonn Ferguson, and David A Walsh.
• Arthritis Research UK Pain Centre, Nottingham, United Kingdom.
• Pain. 2018 Jun 1; 159 (6): 103510441035-1044.

AbstractThis study aimed to identify self-report correlates of central pain augmentation in individuals with knee pain. A subset of participants (n = 420) in the Knee Pain and related health In the Community (KPIC) baseline survey undertook pressure pain detection threshold (PPT) assessments. Items measuring specific traits related to central pain mechanisms were selected from the survey based on expert consensus, face validity, item association with underlying constructs measured by originating host questionnaires, adequate targeting, and PPT correlations. Pain distribution was reported on a body manikin. A "central pain mechanisms" factor was sought by factor analysis. Associations of items, the derived factor, and originating questionnaires with PPTs were compared. Eight self-report items measuring traits of anxiety, depression, catastrophizing, neuropathic-like pain, fatigue, sleep disturbance, pain distribution, and cognitive impact were identified as likely indices of central pain mechanisms. Pressure pain detection thresholds were associated with items representing each trait and with their originating scales. Pain distribution classified as "pain below the waist additional to knee pain" was more strongly associated with low PPT than were alternative classifications of pain distribution. A single factor, interpreted as "central pain mechanisms," was identified across the 8 selected items and explained variation in PPT (R = 0.17) better than did any originating scale (R = 0.10-0.13). In conclusion, including representative items within a composite self-report tool might help identify people with centrally augmented knee pain.

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