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

Akin-Akinyosoye, Kehinde; Sarmanova, Aliya; Fernandes, Gwen; Frowd, Nadia; Swaithes, Laura; Stocks, Joanne; Valdes, Ana; Mcwilliams, Daniel F; Zhang, Weiya; Doherty, Michael; Ferguson, Eamonn; Walsh, David A.

Abstract

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 (?) 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 (? = 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 (? = ?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.

Citation

Akin-Akinyosoye, K., Sarmanova, A., Fernandes, G., Frowd, N., Swaithes, L., Stocks, J., …Walsh, D. A. (2020). Baseline self-report ‘central mechanisms’ trait predicts persistent knee pain in the Knee Pain in the Community (KPIC) cohort. Osteoarthritis and Cartilage, 28(2), 173-181. https://doi.org/10.1016/j.joca.2019.11.004

• Journal Article Type: Article
• Acceptance Date: Nov 18, 2019
• Online Publication Date: Dec 10, 2019
• Publication Date: 2020-02
• Deposit Date: Jan 27, 2020
• Publicly Available Date: Feb 5, 2020
• Journal: Osteoarthritis and Cartilage
• Print ISSN: 1063-4584
• Electronic ISSN: 1522-9653
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 28
• Issue: 2
• Pages: 173-181
• DOI: https://doi.org/10.1016/j.joca.2019.11.004
• Keywords: Rheumatology; Orthopedics and Sports Medicine; Biomedical Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/3812567
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1063458419312701?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: Baseline self-report ‘central mechanisms’ trait predicts persistent knee pain in the Knee Pain in the Community (KPIC) cohort; Journal Title: Osteoarthritis and Cartilage; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.joca.2019.11.004; Content Type: article; Copyright: © 2020 The Authors. Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International.