Can quantitative sensory testing predict treatment outcomes in hip and knee osteoarthritis? A systematic review and meta-analysis of individual participant data

Abstract

An individual participant data (IPD) meta-analysis can assess the predictive value of data on outcomes at the individual level, offering a potential tool for developing personalized pain management. Pretreatment quantitative sensory testing (QST) may stratify patient groups, which are then linked to treatment outcomes. Our objective was to determine if measures of QST at baseline are related to treatment outcomes (at any time point) for pain and disability in lower-limb osteoarthritis. We performed a systematic review with an IPD meta-analysis. Searches were conducted in 9 databases until May 5, 2023 for intervention studies that measured baseline QST and longitudinal measures of participant-reported pain and disability. We performed a 2-stage approach to analyse longitudinal data. Individual models were fitted to each study and combined using random effects multivariate meta-analytic models. Study quality was assessed using the Joanna Briggs Institute checklist, and certainty of the evidence was assessed using GRADE. We identified 3082 records and included 1 hip and 28 knee datasets consisting of 2522 participants from 40 studies. Local warm detection thresholds (P = 0.024) predicted knee osteoarthritis pain outcomes (very-low certainty). Local warm detection thresholds (P = 0.030), remote cold detection thresholds (P = 0.05), and remote pressure tolerance thresholds (P = 0.007) predicted knee osteoarthritis disability outcomes (very-low certainty). Other QST variables were associated with hip and knee osteoarthritis pain and disability levels (eg, pressure pain thresholds), but this relationship did not change over time. This review finds that mechanism-based, QST methodologies do not consistently predict pain or disability on an individual level in hip or knee osteoarthritis.