Doctor of Philosophy
Health and Rehabilitation Sciences
David Walton, Eldon Loh
Chronic neck pain can lead to long-term disability and socio-economic burden. Several demographic, clinical, and psychosocial factors have been implicated in the development of neck pain disability. These factors may also influence management of neck pain. Optimal treatment often requires targeting interventions based on specific diagnosis. One of the most common cause of neck pain is cervical facet joint injury. Currently, the gold standard for diagnosing facetogenic injury is through the medial branch block (MBB) procedure. Though the procedure is relatively safe, it is still invasive and may result in adverse effects. In Canada, access to this procedure is limited through referrals to a specialist pain clinic with wait times of over six months. It is important to help reduce wait times and provide access to the MBB procedure for those likely to respond. The objective of the current study is two folds 1) to develop a comprehensive interdisciplinary regression model to better describe factors that correlate with neck pain disability (Chapter 2 and 3); and 2) to create a decision tree to help clinicians screen for facetogenic neck injury using a receiver operator curve (Chapter 4). In the first two studies of the dissertation, a model was developed using a hierarchical multiple regression. The final model which included: sex, pain duration, etiology, pain intensity, pressure pain detection threshold, number of restricted planes, Spurlings’s test, medical legal status, and pain catastrophizing, explained 62% of the variance in neck disability as measured by the Neck Disability Index (NDI). The last study provided a decision tree that included two factors, pain intensity and pain catastrophizing, to help clinicians identify those patients likely to respond to cervical MBBs. These findings have important implications for front-line clinicians to help rule out patients not likely to benefit from the cervical MBBs and potentially reducing wait times for those likely to respond. However, additional work is still warranted on both the regression model and the decision tree before endorsing it’s use in clinical practice.
Mehta, Swati, "Predicting Response to Medial Branch Blocks: A Clinical Decision Making Tool" (2017). Electronic Thesis and Dissertation Repository. 4362.