Phenotyping Complex Symptoms in Adults with Multiple Sclerosis
Multiple Sclerosis (MS) is an incurable chronic neurodegenerative autoimmune disease of the central nervous system with a complex symptom profile. An estimated 2.5 million people in the world have MS, and it is the most common cause of non-traumatic disability in young adults. A better understanding of symptoms for adults with MS may assist in the assessment, treatment, management, and prevention of impairment to improve quality of life and maintain desired functionality.
The purpose of this dissertation is to provide the foundation for my program of research on the symptom experiences of people with Multiple Sclerosis (Duke Health IRB Pro00073408). This work will explore MS symptom phenotypes, including clusters of early MS-specific symptoms and pervasive symptom trajectory typologies. The resulting symptom clusters and trajectories will describe MS symptom experiences and inform my future research regarding symptom management and prevention of adverse symptoms in adults with MS.
This dissertation is organized into five chapters. Chapter one introduces the research problem, background, and theoretical framework/underpinning. Chapter two provides a literature review exploring the symptom-informed diagnosis experience of people with MS. Chapter three explores which early MS symptoms occur together using an exploratory factor analysis to cluster MS-specific symptoms from the first MS attack according to possible latent factors. Chapter four examines trends in longitudinal pervasive symptom trajectory typologies classified by latent class growth analysis. Chapter five is the synthesis of the findings from each chapter and implications for practice and future research.
This better understanding of symptom clusters and trajectories for MS will aid in the development of a more detailed understanding of symptoms, with the potential to incorporate additional data like genomics, imaging, and other biomarkers to aid in the diagnosis and treatment of MS and its associated symptoms, as well as to better understanding the biological underpinning of symptoms.
This research was funded by Duke University School of Nursing, NIH National Institute of Nursing Research (NINR) (F31NR017121), Jonas Center for Nursing Excellence (Jonas V Veterans Healthcare Scholar), Duke University Summer Research Fellowships (2015, 2016, and 2017), Duke University Graduate School COVID-19 Funding Extension (2020 and 2021).
Exploratory Factor Analysis
Latent Class Growth Analysis
Secondary Data Analysis
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