To identify symptom-based predictors of dry eye disease (DED) signs, specifically ocular surface staining (OSS) and tear production (ST), using the Sjögren's International Collaborative Clinical Alliance (SICCA) registry.
Approach:
Study Population: Utilized patient data from the SICCA registry, a multicenter registry of individuals with suspected or known Sjögren disease (SjD) from 2003 to 2012.
Symptom-Based Cluster Analysis: Performed analysis to determine how various symptoms of DED identify unique SjD phenotypes.
Key Findings:
Artificial tear use and blurred vision were stronger predictors of dry eye disease signs than pain symptoms.
Symptom-based clustering revealed distinct levels of symptom burden.
Abnormal serologic test results were more frequent in the mild-moderate symptom burden cluster.
Symptom-based clusters predict distinct ocular and systemic Sjögren disease phenotypes.
Interpretation:
The study highlights the complexity of DED symptoms and their relationship with clinical signs, indicating that symptom assessment can aid in identifying different clinical phenotypes of SjD.
Limitations:
The study relied on deidentified data from the SICCA registry, which may limit the generalizability of findings.
Exclusion criteria may have omitted relevant patient populations.
Conclusion:
Understanding the interplay between symptoms and signs in DED can improve disease screening and diagnosis, and help tailor management strategies.
