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

Ariane Delgado-Sanchez; Christiana Charalambous; Nelson J Trujillo-Barreto; Hannah Safi; Anthony Jones; Manoj Sivan; Deborah Talmi; Christopher Brown

doi:10.1002/ejp.2193

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

Eur J Pain. 2024 Mar;28(3):434-453. doi: 10.1002/ejp.2193. Epub 2023 Nov 10.

Authors

Ariane Delgado-Sanchez¹, Christiana Charalambous¹, Nelson J Trujillo-Barreto¹, Hannah Safi^{2

3}, Anthony Jones¹, Manoj Sivan⁴, Deborah Talmi⁵, Christopher Brown⁶

Affiliations

¹ School of Health Sciences, University of Manchester, Manchester, UK.
² Department of Medical Physics, Salford Royal Foundation Trust, Northern Care Alliance, Salford, UK.
³ Department of Electrical and Electronic Engineering, School of Engineering, University of Manchester, Manchester, UK.
⁴ Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK.
⁵ Department of Psychology, University of Cambridge, Cambridge, UK.
⁶ Institute of Population Health, University of Liverpool and Human Pain Research Group, University of Liverpool, Liverpool, UK.

PMID: 37947114
DOI: 10.1002/ejp.2193

Abstract

Background: There 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.

Methods: In 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.

Results: Intraclass 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.

Conclusions: These 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.

Significance: Our 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.

MeSH terms

Bayes Theorem
Humans
Pain Measurement / methods
Pain Perception* / physiology
Pain* / diagnosis
Reproducibility of Results