PhD Student Wojeck and Bailey Submit NIH NRSA Application

Kudos to Robyn Wojeck, PhD student; and her faculty sponsors Chip Bailey, associate professor; and Tamara Somers, associate professor at Duke University School of Medicine; and their entire team for the submission of her NIH NRSA (F31) application entitled “Symptom Clusters in Systemic Sclerosis."  This proposal requests funding for two-year period with a start date of Sept. 1, 2019.

An estimated 2.5 million people worldwide are challenged with managing the disfiguring, debilitating, and often unremitting symptoms of systemic sclerosis (SSc; scleroderma). SSc is a rare, chronic multisystem autoimmune disease that often leads to high levels of pain, fatigue, sleep disturbance, and altered mood (i.e., anxiety and depressive symptoms) as well as significant functional disability. Prior research in SSc has focused on single symptoms and their effects on patient outcomes. However, little is known about the prevalence and impact of multiple co-occurring symptoms, or symptom clusters, and their relationship with functional disability in patients with SSc. The purpose of this study is to understand the distinct symptom experiences of SSc patients with co-occurring symptoms and their relationship with functional disability to inform future development of targeted symptom management interventions in patients with SSc. This study aims to: 1) identify subgroups of SSc patients with distinct symptom experiences using a prespecified symptom cluster (i.e., pain, fatigue, sleep disturbance, and altered mood), 2) determine the individual characteristics (i.e., demographic and clinical characteristics) associated with each symptom cluster subgroup, and 3) determine the extent to which symptom cluster subgroups predict functional disability. This descriptive cross- sectional study will use existing symptom data collected at enrollment from the Scleroderma Patient-centered Intervention Network (SPIN) Cohort, including the Patient Reported Information System-29 (PROMIS-29) and the Health Assessment Questionnaire Disability Index (HAQ-DI). Latent profile analysis (LPA) will be used to identify subgroups of SSc patients with distinct symptom profiles using a prespecified symptomcluster. Bivariate and multiple regression models will be used to determine individual characteristics associated with each symptom cluster subgroup. A separate analysis will be conducted for each subgroup membership probability score. Then a regression model approach will be used to determine whether subgroup membership is associated with functional disability. The proposed study supports the National Institute of Nursing Research’s mission and area of strategic focus in symptom science by providing a new lens to SSc symptom research by gaining a better understanding of the complex symptom experience through exploration of multiple co-occurring symptoms, or symptom clusters, and their effects on functional disability in SSc. Findings from this study will inform the next stages of symptom science research (i.e., biomarker discovery and clinical application) in patients with SSc and has the strong potential to inform symptom science in other rare diseases.­