Longitudinal trajectory of medical comorbidities & relationship with clinical outcomes
- Research Opportunity
- PhD, Masters by Research, Honours
- Number of Honour Places Available
- Medicine and Radiology
- Royal Melbourne Hospital
|Dr Steve Simpson, Jr.||email@example.com||Personal web page|
|A/Professor Tracey Weilandfirstname.lastname@example.org||Personal web page|
Multiple sclerosis (MS) is a progressive, autoimmune, demyelinating condition of the central nervous system, manifesting in sensory, motor and/or cognitive dysfunction. Given its onset is typically in the prime years of life – often in the 20s – it has devastating impacts on the quality of life and independent living of the patients so affected.
Medical comorbidities are a frequent issue among people living with chronic conditions like MS, partly because they share common inflammatory and other aetiologic pathways, as well as because of increased recognition due to the ongoing medical care required for MS. Regardless, however, these comorbidities can have potent negative impacts on the health and quality of life of people living with MS. The precise nature of these deleterious associations with clinical course in MS has not been well-studied.
This project will make use of advanced statistical techniques, particularly latent class analysis, to assess trajectories of medical comorbidities over time in the HOLISM longitudinal cohort (n=2,644 at baseline, n=1,401 at 2.5-yr follow-up, n=952 at 5-yr follow-up), assess the characteristics, clinical and demographic with membership in these trajectory groups, and correlate these trajectory groups with clinical outcomes -including relapse rate, disability as measured by the Patient Determined Disease Steps and fatigue as measured by the Fatigue Severity Scale - and quality of life as measured by the MSQOL-54. If indeed there is a deleterious relationship between medical comorbidities and worse clinical course in MS, this will be invaluable information for medical practitioners, both in terms of ongoing care for patients with MS but also to know what comorbidities may be at greater risk of inducing worse clinical outcomes.
The primary analysis method to be employed here is latent class analysis, which will glean the groupings of individuals’ medical comorbidities over time. Other analysis methods to be employed include linear regression, multilevel mixed-effects linear regression, log-binomial regression, and/or Poisson regression, including univariable and multivariable models, as well as potential inter-group assessment of interaction by sex, age, MS course, or others as appropriate.
Faculty Research Themes
School Research Themes
PhD, Masters by Research, Honours
Students who are interested in joining this project will need to consider their elegibility as well as other requirements before contacting the supervisor of this research
For further information about this research, please contact a supervisor.
Research NodeRoyal Melbourne Hospital
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