Test–retest reliability of Bayesian estimations of the effects of stimulation, prior information and individual traits on pain perception

Abstract

AbstractBackgroundThere is inter‐individual variability in the influence of different components (e.g. nociception and expectations) on pain perception. Identifying the individual effect of these components could serve for patient stratification, but only if these influences are stable in time.MethodsIn this study, 30 healthy participants underwent a cognitive pain paradigm in which they rated pain after viewing a probabilistic cue informing of forthcoming pain intensity and then receiving electrical stimulation. The trial information was then used in a Bayesian probability model to compute the relative weight each participant put on stimulation, cue, cue uncertainty and trait‐like bias. The same procedure was repeated 2 weeks later. Relative and absolute test–retest reliability of all measures was assessed.ResultsIntraclass correlation results showed good reliability for the effect of the stimulation (0.83), the effect of the cue (0.75) and the trait‐like bias (0.75 and 0.75), and a moderate reliability for the effect of the cue uncertainty (0.55). Absolute reliability measures also supported the temporal stability of the results and indicated that a change in parameters corresponding to a difference in pain ratings ranging between 0.47 and 1.45 (depending on the parameters) would be needed to consider differences in outcomes significant. The comparison of these measures with the closest clinical data we possess supports the reliability of our results.ConclusionsThese findings support the hypothesis that inter‐individual differences in the weight placed on different pain factors are stable in time and could therefore be a possible target for patient stratification.SignificanceOur results demonstrate the temporal stability of the weight healthy individuals place on the different factors leading to the pain response. These findings give validity to the idea of using Bayesian estimations of the influence of different factors on pain as a way to stratify patients for treatment personalization.