School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, Essex, United Kingdom.
Division of Physiotherapy, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Eur J Pain. 2020 May;24(5):909-920. doi: 10.1002/ejp.1537. Epub 2020 Feb 21.
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.
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.
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.
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.
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.
康复方法应基于对功能恢复背后机制的理解。然而，导致颈椎神经根病（CR）患者术后疼痛相关残疾改善的介质尚不清楚。本研究旨在使用贝叶斯网络（BN）来学习 CR 患者的身体和心理因素与疼痛相关残疾之间的概率关系。
我们分析了 201 名接受过手术的 CR 患者的前瞻性队列数据集。总共使用了 15 个变量来构建 BN 模型：年龄、性别、颈部肌肉耐力、颈部活动范围、颈部本体感觉、手握力、自我效能感、灾难化、抑郁、躯体知觉、手臂疼痛强度、颈部疼痛强度和残疾。
6 个月时自我效能感的变化增加 1 分与 12 个月时残疾的变化减少 0.09 分相关（t=-64.09，p<0.001）。两种途径导致残疾的变化：一条直接的途径，从 6 个月时自我效能感的变化到残疾；另一条间接途径，通过 6 个月和 12 个月时颈部和手臂疼痛强度的变化来介导。
这是第一项应用 BN 模型来理解 CR 术后患者康复机制的研究。疼痛相关残疾的改善是由自我效能水平的变化直接和间接驱动的。本研究提供了潜在可调节的中介因素，这些因素可能成为未来干预试验的目标。BN 模型可以提高 CR 患者治疗和结局评估的精确性。
使用贝叶斯网络建模，我们发现术后 6 个月时自我效能感的变化直接和间接影响了 CR 患者残疾的变化。对恢复机制的理解提供了潜在可调节的中介因素，这些因素可能成为未来干预试验的目标。
