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Biopsychosocial Prediction Models of Pain Management Programme (PMP) Outcomes for Chronic Pain


   School of Psychology

  , ,  Monday, August 22, 2022  Competition Funded PhD Project (UK Students Only)

About the Project

Project background: Chronic pain is moderately or severely disabling in an estimated 8-12% of the UK population. Those struggling to cope are often referred to multidisciplinary Pain Management Programmes (PMPs), currently considered best practice for intractable chronic pain. PMPs are resource-intensive and expensive, yet outcomes are highly variable depending on the patient, despite multidisciplinary assessment to determine suitability. We need to be able to better identify patients who are likely to benefit and/or to refine the content of PMPs to individuals, to improve overall outcomes. There have been recent advances in the use of statistical prediction models (mathematical equations that make predictions of treatment outcome based on patient characteristics), to find out if a treatment programme is suitable for an individual patient. However, there are a number of limitations of previous research into prediction models for PMPs, which this project aims to overcome. Importantly, there has been little research on the impact of “enabling factors” that promote PMP engagement and long-term benefit. This project would be the first of its kind to identify positive biological, psychological and social factors that are unique in enabling individuals to benefit from PMPs (in contrast to previous studies focussing on negative traits predicting poor outcomes).

Project aims: 1. Systematically review the available literature to assess the quality of evidence supporting certain predictors of outcome from PMPs; 2. Develop and test an initial statistical prediction model of PMP outcomes using an existing clinical database (the Pain Management Registry (PMR) at the Walton Centre NHS Foundation Trust, a leading centre for pain management and neuroscience); 3. Identify new “enabling factors” using qualitative and quantitative methods; 4. Establish the feasibility and patient acceptability of the clinical measurement of these enabling factors. The project will eventually enable clinicians to better select patients for specific PMPs (whilst maximising access and avoiding discrimination) and to tailor treatment to individual needs.

Research training: The University of Liverpool offers a multidisciplinary research environment in the fields of psychology, clinical psychology and neuroscience. This is a multidisciplinary project in which the student will work at the interface of academic and clinical research environments, developing knowledge about both University and NHS research regulations. The PhD project will provide excellent training and experience to enable the student to develop (1) clinical knowledge of best-practice care for patients with chronic pain, (2) expertise in methods in how to conduct high-quality systematic reviews that are of the standard to be published in leading international journals, (3) knowledge of methods for advanced statistical modelling of large clinical datasets and how to publish findings in leading journals, (4) detailed knowledge of quantitative methodologies and expert supervision in mixed qualitative-quantitative Q-methodology its implementation in online environments, (5) valuable skills for future work in clinical pain neuroscience, including training in EEG acquisition and analysis.

Eligibility: Applicants should have at least a first class or an upper second-class Honours degree (or equivalent) in an appropriate discipline such as Psychology or Neuroscience. A Masters degree is desirable but not essential.

We are looking for candidates with the following characteristics:

  • Strong interest in applied psychology and neuroscience research and practice in terms of its applicability within the NHS.
  • Good interpersonal skills – able to communicate clearly, effectively and sensitively with NHS patients who may have physical and mental health vulnerabilities.
  • Excellent attention to detail when working with complex quantitative and qualitative datasets.
  • Good computing skills and a willingness and aptitude for learning and developing statistical knowledge and related programming skills (for example in the R programming language).
  • Good academic writing skills as demonstrated by high marks in University coursework or by contribution to published research papers.
  • Highly resilient to setbacks and able to either persist to achieve study objectives or adapt goals to changing circumstances.

Dr Christopher Brown: https://www.liverpool.ac.uk/population-health/staff/chris-brown/

Dr Jennie Day: https://www.liverpool.ac.uk/population-health/staff/jennie-day/

Dr Katie Herron: https://www.liverpool.ac.uk/pain-research-institute/our-people/katie-herron/

For any enquiries please contact: Dr Christopher Brown on:

To apply please send curriculum vitae, names and details of 2 academic referees and a covering letter to Dr Brown on:


Funding Notes

This is a Prof John Miles Prize PhD Studentship provided by the Pain Relief Foundation (View Website). The funding will enable a graduate to embark on a research career in human chronic pain. PhD funding is for three years, covering full UK tuition fees and a tax-free stipend of £15,609 in the first year, with increases in years two and three. There is also a small budget available to cover project expenses.

References

Halicka M, Duarte R, Catherall S, Maden M, Coetsee M, Wilby M, Brown CA. Systematic review and meta-analysis of predictors of return to work after spinal surgery for chronic low back and leg pain. J Pain 2022. doi: 10.1016/j.jpain.2022.02.003.
Halicka M, Wilby M, Duarte R, Brown CA. Predicting patient-reported outcomes following lumbar spine surgery: development and external validation of multivariable prediction models. Pre-print: https://doi.org/10.1101/2022.02.15.22270980
Wilson IR. Management of chronic pain through pain management programmes. Br Med Bull. 2017 Dec 1;124(1):55-64. doi: 10.1093/bmb/ldx032. PMID: 28927228.

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