Probing the mechanisms underpinning recovery in post‐surgical patients with cervical radiculopathy using Bayesian networks. (21st February 2020) - Liew, Bernard X. W.
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.