Arendt‐Nielsen, Lars

Abstract Pain treatments have modest effects. Health outcomes might be improved if treatments are matched to mechanisms underlying the persistence and biopsychosocial impact of an individual's pain. The International Association for the Study of Pain (IASP) defines 3 mechanistic pain descriptors presumed to involve different mechanisms—nociceptive, neuropathic, and nociplastic. Although treatments to address each descriptor have been proposed, there is no consensus on how to assign descriptors to clinical presentations. A recent consensus identified candidate clinical features to discriminate between descriptors in clinical practice and research. These need refinement to progress towards a tool. This study aimed to determine the rank and relative weight of identified clinical features to aid discrimination between mechanistic pain descriptors. Candidate clinical features (n = 196) were refined by the IASP Terminology Task Force to converge similar and remove redundant features. The Task Force (n = 24) and an expert panel (n = 39) ranked features using 1000minds conjoint analysis software and assigned weights based on discrete pairwise choices. Participants nominated from pairs of scenarios, which most likely indicated that pain aligned predominantly to a descriptor. Highest ranked features for neuropathic and nociplastic pain were aligned with IASP Clinical Criteria. Criteria for nociceptive pain have not been established. A ranked list of features shared by 2 mechanisms (indicating mixed mechanisms) was also identified. This study identified expert consensus on the highest ranked clinical features with potential to discriminate between pain descriptors, reflective of different underlying mechanisms. This study extends current frameworks by identifying and refining key discriminators for future operationalisation and validation.

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.