Multidomain evaluation and data-driven approaches to predict recurrent neck pain (end-rnp)

Neck pain (NP) remains one of the leading causes of disability worldwide, resulting in financial burden and poor quality of life. The incidence has steadily increased since 1990, with data from the UK showing the largest increase. In 2020, more than 203 million people suffered from NP globally, and this number is expected to grow by more than 30% over the next three decades. Of those who experience an episode of NP, between 50 and 85% will report NP 1-5 years later, with over 20% of people developing recurrent pain episodes. In June 2022, more than 250,000 individuals in the UK had back or neck pain severe enough to cause them to leave work. Despite being a research priority, the factors contributing to the development of new episodes of NP remain poorly understood. Understanding these factors, would allow targeted interventions and preventative measures. Advancing research on NP recurrence implies overcoming significant challenges, including (i) acknowledging the multidimensional nature of NP, encompassing physical, psychosocial, and environmental factors, and (ii) employing methodologies capable of managing such complexity while monitoring NP progression over time. Previous research has often focused on a single domain, largely overlooking the variation in individual and contextual physiological responses to pain. Moreover, previous studies have predominantly concentrated on patient-reported outcomes, exploring what patients perceive and how they relate to their health condition with limited attention to physical impairments associated with NP. Yet, our research has shown that individuals with recurrent NP exhibit persistent physical alterations when in remission from their pain, and these physical factors may potentially play a role in NP recurrence. A comprehensive, multidimensional approach is therefore essential to address the complexity of NP and identify predictors of new NP episodes. This approach must recognise the fluctuating and recurrent nature of NP, characterised by periods of symptom exacerbation and remission. Based on established research priorities and recognised challenges in NP research, the primary aim of this project is to identify physical and psychosocial predictors related to the development of new NP episodes in people with recurrent NP. Predictors of future NP episodes and pain severity will be identified to develop clinical prediction models for estimating individualised risk of NP recurrence over a 12-month period. Additionally, we will monitor behavioural and environmental factors during the 12-month period to investigate whether changes in these fluctuating factors are related to new episodes of NP. To achieve these goals, we will use different data-driven approaches within a biopsychosocial framework. The findings from this project will reveal factors that can help predict the development of future NP episodes, and thus aid in the development of a clinical prediction tool, helping clinicians in identifying patients at higher risk of experiencing new and more severe NP episodes. By identifying at-risk individuals and relevant modifiable predictors, the prediction model will enable the implementation of early intervention strategies, tailored rehabilitation programs, and personalised preventive measures. These efforts aim to reduce both the frequency and severity of NP episodes to improve quality of life for those affected. The predictors identified for future NP episodes could represent potential treatment targets, important for consideration in future randomised controlled trials. This will guide clinicians in making informed decisions about the most appropriate and effective treatment plans for their patients, which ultimately may reduce healthcare costs associated with recurrent NP.