Differences in symptom experience among patients with systemic sclerosis: a cluster analytic approach to identifying...

Area of Science:

• Rheumatology
• Clinical Medicine
• Patient-Reported Outcomes

Background:

• Systemic sclerosis (SSc) presents with diverse and clinically challenging symptoms.
• Understanding symptom co-occurrence and underlying mechanisms is crucial for targeted SSc treatments.
• Identifying distinct patient subgroups based on symptom experience can refine therapeutic strategies.

Purpose of the Study:

• To identify and characterize patient subgroups within a systemic sclerosis (SSc) cohort based on their symptom experience.
• To explore potential differences in symptom presentation and underlying mechanisms across identified SSc subgroups.

Main Methods:

• Exploratory hierarchical agglomerative cluster analysis on a cohort of 587 SSc patients.
• Utilized patient-reported outcomes for pain interference, fatigue, sleep disturbance, dyspnoea, depression, and anxiety for clustering.
• Multivariate analysis of variance (MANOVA) and analyses of variance (ANOVA) were employed to examine subgroup characteristics and variable contributions.

Main Results:

• Cluster analysis revealed three distinct SSc subgroups based on symptom severity: no/minimal, mild, and moderate.
• The mild symptom subgroup was characterized by primary clusters of pain, fatigue, and sleep disturbance.
• The moderate symptom subgroup exhibited co-occurring pain, fatigue, sleep disturbance, depression, and anxiety, despite similar disease severity to the mild group.

Conclusions:

• Distinct symptom clusters identified in SSc patients, particularly in mild and moderate groups, indicate potential differences in treatment approaches.
• These findings suggest the need for further investigation into the mechanisms underlying symptom experience in SSc.
• Tailoring treatment based on identified symptom clusters may improve clinical management of SSc.