Background:In the UK, 10% of men and 18% of women over the age of 60 suffer from symptomatic osteoarthritis (OA), and rising. OA knee pain can worsen without significant radiographic changes and pain remains a major problem for up to 20% of patients after total knee joint replacement. Chronic knee OA pain is augmented by central pain mechanisms, including central sensitisation. Measures of the level of central involvement in pain could inform clinical decision making. Self-report characteristics of depression, anxiety, cognitive difficulties, catastrophizing, sleep disturbance, fatigue, and widespread pain distribution together contribute to a Central Mechanisms Trait which is associated with central sensitisation and OA knee painObjectives:Using self-report questionnaire data from the Osteoarthritis Initiative Cohort Study (OAI) we aimed to evaluate the prognostic performance of baseline CMT for pain at 24-months.Methods:OAI participants with knee OA or at risk of knee OA with pain in the same knee at both index time point (48-months) and one year prior to that date were included (n=1984). Knee pain was measured using the Western Ontario and McMasters Universities Osteoarthritis Index (WOMAC) pain sub-scale, by reference to the index knee (the knee with the highest WOMAC pain sub-scale score at baseline). Questionnaire items were selected to assess the 7 available characteristics identified by Akin-Akinyosoye et al.[1], from which a single CMT factor was calculated by confirmatory factor analysis. Anxiety, fatigue and cognitive difficulties were assessed by single items, depression and sleep disturbance represented by multiple items, and catastrophising by using the Coping Strategies Questionnaire – Catastrophising sub-scale. Pain distribution was defined as a sum of other painful joints at or below the hip. A CMT factor was derived from the 7 characteristics using confirmatory factor analysis. The association between the CMT factor score and 24-month pain (adjusted for baseline pain, radiographic OA (Kellgren-Lawrence (KL) scale) and demographic confounders) was investigated using generalised linear regression with a negative binomial link function.Results:At baseline, participants had a mean (SD) age 65(9) years, a BMI 29.6(5.1) kg/m², 60% were female, 19.8% were African American, KL score was 1.92(1.35) indicating that the majority of the cohort had radiographic OA. Model diagnostics informed the CMT model, with the final model having an RMSEA of 0.073 (90%CI 0.070-0.076). Data were consistent with a single factor model for CMT. In the multivariable model, higher baseline CMT scores were significantly associated with 24-month WOMAC pain scores, with or without adjustment for baseline pain and other covariates, including KL score (multivariable model; std beta=0.173 (SE=0.027), p=0.004). Association of baseline CMT was of similar strength, and over and above association of KL score with 24-month pain (std beta=0.164 (SE=0.038), p=<0.001). Adjusted regression coefficients and associated p-values are shown in Table 1.Table 1.Adjusted regression coefficients for analysed variables against WOMAC pain at 24-monthsVariablesStd beta (SE)PSex-0.096 (0.101)0.344Age, y-0.001 (0.006)0.881BMI, kg/m²0.017 (0.010)0.088Index Knee Kellgren-Lawrence Score0.164 (0.038)<0.001CMT Factor Score0.173 (0.060)0.004Baseline Pain0.857 (0.035)<0.001n=1421, rows in bold indicate significant association (p<0.05), associations adjusted for race and ethnicityConclusion:CMT predicts worse pain prognosis with a similar magnitude to radiographic OA even after adjustment for other factors. A self-report tool which included items relevant to the characteristics included in the CMT may help to select people with OA knee pain with unfavourable pain prognosis. Poor outcomes related to central pain mechanisms or to joint structural damage might be amenable to treatments addressing central or peripheral pain mechanisms respectively.References:[1]Akin-Akinyosoye et al., PAIN, 2018. 159(6): p. 1035-1044.Acknowledgements:This abstract was prepared using an Osteoarthritis Initiative (OAI) public use data set and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners. The authors wish to thank the participants, principal investigators, co-investigators and staff of all the hospitals who have contributed data to the OAI. The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health.Disclosure of Interests:Samuel Holden: None declared, Daniel McWilliams Grant/research support from: Pfizer and Eli Lilly, Stephanie Smith: None declared, David Walsh Consultant of: Pfizer, Eli Lilly, AbbVie and GlaxoSmithKline, Grant/research support from: Pfizer and Eli Lilly