title: Pain phenotype in patients with knee osteoarthritis: classification and measurement properties of painDETECT and self-report Leeds assessment of neuropathic symptoms and signs scale in a cross-sectional study.
abstract: OBJECTIVE
  Multiple mechanisms are involved in pain associated with osteoarthritis (OA). The painDETECT and Self-Report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) questionnaires screen for neuropathic pain and may also identify individuals with musculoskeletal pain who exhibit abnormal central pain processing. The aim of this cross-sectional study was to evaluate painDETECT and S-LANSS for classification agreement and fit to the Rasch model, and to explore their relationship to pain severity and pain mechanisms in OA.
  
  
  METHODS
  A total of 192 patients with knee OA completed questionnaires covering different aspects of pain. Another group of 77 patients with knee OA completed questionnaires and underwent quantitative sensory testing for pressure-pain thresholds (PPTs). Agreement between painDETECT and S-LANSS was evaluated using kappa coefficients and receiver operator characteristic (ROC) curves. Rasch analysis of both questionnaires was conducted. Relationships between screening questionnaires and measures of pain severity or PPTs were calculated using correlations.
  
  
  RESULTS
  PainDETECT and S-LANSS shared a stronger correlation with each other than with measures of pain severity. ROC curves identified optimal cutoff scores for painDETECT and S-LANSS to maximize agreement, but the kappa coefficient was low (κ = 0.33-0.46). Rasch analysis supported the measurement properties of painDETECT but not those of S-LANSS. Higher painDETECT scores were associated with widespread reductions in PPTs.
  
  
  CONCLUSIONS
  The data suggest that painDETECT assesses pain quality associated with augmented central pain processing in patients with OA. Although developed as a screening questionnaire, painDETECT may also function as a measure of characteristics that indicate augmented central pain processing. Agreement between painDETECT and S-LANSS for pain classification was low, and it is currently unknown which tool may best predict treatment outcome.
authors.affiliation: University of Nottingham, Nottingham, UK.
authors.forename: Bryan J
authors.initials: BJ
authors.lastname: Moreton
authors.forename: Victoria
authors.initials: V
authors.lastname: Tew
authors.forename: Roshan
authors.initials: R
authors.lastname: das Nair
authors.forename: Maggie
authors.initials: M
authors.lastname: Wheeler
authors.forename: David A
authors.initials: DA
authors.lastname: Walsh
authors.forename: Nadina B
authors.initials: NB
authors.lastname: Lincoln
mesh_terms: D000368:Aged; D003430:Cross-Sectional Studies; D005260:Female; D006306:Health Surveys / Q000145:classification* / Q000379:methods; D006801:Humans; D008297:Male; D008875:Middle Aged; D009437:Neuralgia / Q000145:classification* / Q000175:diagnosis; D020370:Osteoarthritis, Knee / Q000145:classification* / Q000175:diagnosis; D010147:Pain Measurement / Q000145:classification* / Q000379:methods; D010641:Phenotype; D057566:Self Report / Q000145:classification*