Papers of the Week

Pain neuroscience education (PNE) is an approach used in the management of chronic musculoskeletal pain (CMP). Previous reviews on PNE and other pain interventions, have focussed on mean treatment effects, but in the context of "precision medicine", any inter-individual differences in treatment response are also important to quantify. If inter-individual differences are present, and predictors identified, PNE could be tailored to certain people for optimising effectiveness. Such heterogeneity can be quantified using recently-formulated approaches for comparing the response variance between the treatment and control groups. Therefore, we conducted a systematic review and meta-analysis on the extracted standard deviations of baseline-to-follow up change to quantify the inter-individual variation in pain, disability and psychosocial outcomes in response to PNE. Electronic databases were searched between 01/01/2002 and 14/06/2018. The review included five randomised controlled trials (n=428) in which disability outcomes were reported. Using a random effects meta-analysis, the pooled SD (95% CI) for control group-adjusted response heterogeneity to PNE was 7.36 units /100 (95% CI: -3.93 to 11.12). The 95% prediction interval for this response heterogeneity SD was wide (-10.20 to 14.57 units /100). The control group-adjusted proportion of "responders" in the population who would be estimated to exceed a clinically important change of 10/100 ranged from 18-45%. Therefore, when baseline-to-follow up random variability in disability is taken into account (informed by the control arm), there is currently insufficient evidence for the notion of clinically important inter-individual differences in disability responses to PNE in people with CMP. The protocol was published on PROSPERO (CRD42017068436). PERSPECTIVE: We bring a novel method to pain science for calculating inter-individual differences in response to a treatment. This is conducted within the context of a systematic review and meta-analysis on PNE. We highlight how using erroneous methods for calculating inter-individual differences can drastically change conclusions when compared to appropriate methods.