Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach

Prognostic models play an important role in the clinical management of cervical radiculopathy (CR). No study has compared the performance of modern machine learning techniques, against more traditional stepwise regression techniques, when developing prognostic models in individuals with CR. We analysed a prospective cohort dataset of 201 individuals with CR. Four modelling techniques (stepwise regression, least absolute shrinkage and selection operator [LASSO], boosting, and multivariate adaptive regression splines [MuARS]) were each used to form a prognostic model for each of four outcomes obtained at a 12 month follow-up (disability—neck disability index [NDI]), quality of life (EQ5D), present neck pain intensity, and present arm pain intensity). For all four outcomes, the differences in mean performance between all four models were small (difference of NDI < 1 point; EQ5D < 0.1 point; neck and arm pain < 2 points). Given that the predictive accuracy of all four modelling methods were clinically similar, the optimal modelling method may be selected based on the parsimony of predictors. Some of the most parsimonious models were achieved using MuARS, a non-linear technique. Modern machine learning methods may be used to probe relationships along different regions of the predictor space

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

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

Determining the level of cervical radiculopathy : Agreement between visual inspection of pain drawings and magnetic resonance imaging

Background and Aims Pain drawings are commonly used in the clinical assessment of people with cervical radiculopathy. This study aimed to assess (1) the agreement of clinical interpretation of pain drawings and MRI findings in identifying the affected level of cervical radiculopathy, (2) the agreement of these predictions based on the pain drawing among four clinicians from two different professions (i.e., physiotherapy and surgery) and (3) the topographical pain distribution of people presenting with cervical radiculopathy (C4-C7). Methods Ninety-eight pain drawings were obtained from a baseline assessment of participants in a randomized clinical trial, in which single-level cervical radiculopathy was determined using MRI. Four experienced clinicians independently rated each pain drawing, attributing the pain distribution to a single nerve root (C4-C7). A post hoc analysis was performed to assess agreement. Outcome measures Percentage agreement and kappa values were used to assess the level of agreement. Topographic pain frequency maps were created for each cervical radiculopathy level as determined by MRI. Results The radiculopathy level determined from the pain drawings showed poor overall agreement with MRI (mean = 35.7%, K = -0.007 to 0.139). The inter-clinician agreement ranged from fair to moderate (K = 0.212-0.446). Topographic frequency maps revealed that pain distributions were widespread and overlapped markedly between patients presenting with different levels cervical radiculopathy. Conclusion This study revealed a lack of agreement between the segmental level affected determined from the patients pain drawing and the affected level as identified on MRI. The large overlap of pain and non-dermatomal distribution of pain reported by patients likely contributed to this result