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Probing the mechanisms underpinning recovery in post‐surgical patients with cervical radiculopathy using Bayesian networks

Liew, Bernard XW and Peolsson, Anneli and Scutari, Marco and Löfgren, Hakan and Wibault, Johanna and Dedering, Åsa and Öberg, Birgitta and Zsigmond, Peter and Falla, Deborah (2020) 'Probing the mechanisms underpinning recovery in post‐surgical patients with cervical radiculopathy using Bayesian networks.' European Journal of Pain, 24 (5). 909 - 920. ISSN 1090-3801

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Abstract

Background Rehabilitation approaches should be based on an understanding of the mechanisms underpinning functional recovery. Yet, the mediators that drive an improvement in post‐surgical pain‐related disability in individuals with cervical radiculopathy (CR) are unknown. The aim of the present study is to use Bayesian networks (BN) to learn the probabilistic relationships between physical and psychological factors, and pain-related disability in CR. Methods We analysed a prospective cohort dataset of 201 post‐surgical individuals with CR. In all, 15 variables were used to build a BN model: age, sex, neck muscle endurance, neck range of motion, neck proprioception, hand grip strength, self-efficacy, catastrophizing, depression, somatic perception, arm pain intensity, neck pain intensity and disability. Results A one point increase in a change of self‐efficacy at 6 months was associated with a 0.09 point decrease in a change in disability at 12 months (t = −64.09, p < .001). Two pathways led to a change in disability: a direct path leading from a change in self-efficacy at 6 months to disability, and an indirect path which was mediated by neck and arm pain intensity changes at 6 and 12 months. Conclusions This is the first study to apply BN modelling to understand the mechanisms of recovery in post‐surgical individuals with CR. Improvements in pain‐related disability was directly and indirectly driven by changes in self‐efficacy levels. The present study provides potentially modifiable mediators that could be the target of future intervention trials. BN models could increase the precision of treatment and outcome assessment of individuals with CR. Significance Using Bayesian Network modelling, we found that changes in self-efficacy levels at 6-month post-surgery directly and indirectly influenced the change in disability in individuals with CR. A mechanistic understanding of recovery provides potentially modifiable mediators that could be the target of future intervention trials.

Item Type: Article
Divisions: Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of
Depositing User: Elements
Date Deposited: 04 Dec 2020 17:24
Last Modified: 27 Jan 2021 02:00
URI: http://repository.essex.ac.uk/id/eprint/28950

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