The Central Aspects of Pain in the Knee (CAP-Knee) questionnaire; a mixed-methods study of a self-report instrument for assessing central mechanisms in people with knee pain.

OBJECTIVES: Pain is the prevailing symptom of knee osteoarthritis. Central sensitisation creates discordance between pain and joint pathology. We previously reported a Central Pain Mechanisms trait derived from eight discrete characteristics: Neuropathic-like pain, Fatigue, Cognitive-impact, Catastrophising, Anxiety, Sleep disturbance, Depression, and Pain distribution. We here validate and show that an 8-item questionnaire, Central Aspects of Pain in the Knee (CAP-Knee) is associated both with sensory- and affective- components of knee pain severity. METHODS: Participants with knee pain were recruited from the Investigating Musculoskeletal Health and Wellbeing study in the East Midlands, UK. CAP-Knee items were refined following cognitive interviews. Psychometric properties were assessed in 250 participants using Rasch-, and factor-analysis, and Cronbach's alpha. Intra-class correlation coefficients tested repeatability. Associations between CAP-Knee and McGill Pain questionnaire pain severity scores were assessed using linear regression. RESULTS: CAP-Knee targeted the knee pain sample well. Cognitive interviews indicated that participants interpreted CAP-Knee items in diverse ways, which aligned to their intended meanings. Fit to the Rasch model was optimised by rescoring each item, producing a summated score from 0 to 16. Internal consistency was acceptable (Cronbach's alpha = 0.74) and test-retest reliability was excellent (ICC2,1 = 0.91). Each CAP-Knee item contributed uniquely to one discrete 'Central Mechanisms trait' factor. High CAP-Knee scores associated with worse overall knee pain intensity, and with each of sensory- and affective- McGill Pain Questionnaire scores. CONCLUSION: CAP-Knee is a simple and valid self-report questionnaire, which measures a single 'Central Mechanisms' trait, and may help identify and target centrally-acting treatments aiming to reduce the burden of knee pain.

Baseline self-report 'central mechanisms' trait predicts persistent knee pain in the Knee Pain in the Community (KPIC) cohort.

OBJECTIVES: We investigated whether baseline scores for a self-report trait linked to central mechanisms predict 1 year pain outcomes in the Knee Pain in the Community cohort. METHOD: 1471 participants reported knee pain at baseline and responded to a 1-year follow-up questionnaire, of whom 204 underwent pressure pain detection thresholds (PPTs) and radiographic assessment at baseline. Logistic and linear regression models estimated the relative risks (RRs) and associations (ß) between self-report traits, PPTs and pain outcomes. Discriminative performance for each predictor was compared using receiver-operator characteristics (ROC) curves. RESULTS: Baseline Central Mechanisms trait scores predicted pain persistence (Relative Risk, RR = 2.10, P = 0.001) and persistent pain severity (ß = 0.47, P < 0.001), even after adjustment for age, sex, BMI, radiographic scores and symptom duration. Baseline joint-line PPTs also associated with pain persistence (RR range = 0.65 to 0.68, P < 0.02), but only in univariate models. Lower baseline medial joint-line PPT was associated with persistent pain severity (ß = -0.29, P = 0.013) in a fully adjusted model. The Central Mechanisms trait model showed good discrimination of pain persistence cases from resolved pain cases (Area Under the Curve, AUC = 0.70). The discrimination power of other predictors (PPTs (AUC range = 0.51 to 0.59), radiographic OA (AUC = 0.62), age, sex and BMI (AUC range = 0.51 to 0.64), improved significantly (P < 0.05) when the central mechanisms trait was included in each logistic regression model (AUC range = 0.69 to 0.74). CONCLUSION: A simple summary self-report Central Mechanisms trait score may indicate a contribution of central mechanisms to poor knee pain prognosis.

Knee pain and related health in the community study (KPIC): a cohort study protocol.

BACKGROUND: The incidence, progression and related risk factors for recent-onset knee pain (KP) remain uncertain. This study aims to examine the natural history of KP including incidence and progression and to identify possible phenotypes and their associated risk factors. METHODS: A prospective community-based cohort of men and women aged 40 years or over within the East Midlands region (UK) will be recruited via a postal questionnaire from their general practices. The questionnaire will enquire about: presence and onset of KP; pain severity (0­10 numerical rating scale (NRS)); pain catastrophizing and neuropathic-like pain (NP) using the painDETECT questionnaires (definite NP scores ≥19­38); risk factors for KP and/ or osteoarthritis (OA) (age, body mass index, constitutional knee alignment, nodal OA, index: ring finger length (2D4D) ratio); quality of life (SF12); and mental health (Hospital Anxiety and Depression Scale). Clinical assessments will be undertaken in a sample of 400 participants comprising three groups: early KP (≤3 year's duration), established KP (>3 years) and no KP. Assessments will include knee radiographs (standing semi-flexed and 30(0) skyline views); knee ultrasound (synovial effusion, hypertrophy, and Doppler activity); quantitative sensory testing; muscle strength (quadriceps, hip abductor, and hand-grip); balance; gait analysis (GAITrite); and biomarker sampling. A repeat questionnaire will be sent to responders at years 1 and 3. The baseline early KP group will undergo repeat assessments at year 1 (apart from radiographs) and year 3 (with radiographs). Any incident KP individuals identified at year 1 or 3 questionnaires will have clinical and radiographic assessments at the respective time points. DISCUSSION: Baseline data will be used to examine risk factors for early onset KP and to identify KP phenotypes. Subsequent prospective data, at least to Year 3, will allow examination of the natural history of KP and risk factors for incidence and progression. TRIAL REGISTRATION: The study was registered on the clinicaltrials.gov portal: NCT02098070) on the 14th of March 2014.