Baseline self-report ‘central mechanisms’ trait predicts persistent knee pain in the Knee Pain in the Community (KPIC) cohort
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. 1471 participants reported knee pain at baseline and responded to a 1-year follow-up questionnaire, of whom 204 underwent pressure pain...
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Published in: | Osteoarthritis and cartilage Vol. 28; no. 2; pp. 173 - 181 |
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Main Authors: | , , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
England
Elsevier Ltd
01-02-2020
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Subjects: | |
Online Access: | Get full text |
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Summary: | 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.
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 (β) between self-report traits, PPTs and pain outcomes. Discriminative performance for each predictor was compared using receiver-operator characteristics (ROC) curves.
Baseline Central Mechanisms trait scores predicted pain persistence (Relative Risk, RR = 2.10, P = 0.001) and persistent pain severity (β = 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 (β = −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).
A simple summary self-report Central Mechanisms trait score may indicate a contribution of central mechanisms to poor knee pain prognosis. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1063-4584 1522-9653 |
DOI: | 10.1016/j.joca.2019.11.004 |