# Symptom Clusters in Systemic Sclerosis

> **NIH NIH F31** · DUKE UNIVERSITY · 2020 · $36,979

## Abstract

ABSTRACT
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. 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 (N=1942), including the Patient-Reported Outcomes Measurement 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
symptom cluster. Bivariate and multiple regression models will be used to determine individual characteristics
associated with each symptom cluster subgroup. Finally, 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 to capture the complexities of the 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.

## Key facts

- **NIH application ID:** 9991018
- **Project number:** 1F31NR019007-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Robyn Wojeck
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $36,979
- **Award type:** 1
- **Project period:** 2020-06-01 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991018

## Citation

> US National Institutes of Health, RePORTER application 9991018, Symptom Clusters in Systemic Sclerosis (1F31NR019007-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9991018. Licensed CC0.

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