Cracking the Code: Which Ocular Symptoms Predict Dry Eye Signs? Insights From a Large International Sicca Registry

Area of Science:

• Ophthalmology
• Dry Eye Disease Research
• Clinical Diagnostics

Background:

• Dry eye disease (DED) is a multifactorial condition affecting the ocular surface.
• Identifying reliable predictors of DED signs is essential for accurate diagnosis and management.
• The Sjögrens International Collaborative Clinical Alliance (SICCA) cohort provides a valuable dataset for DED research.

Purpose of the Study:

• To identify symptom-based predictors of objective dry eye disease (DED) signs within the SICCA cohort.
• To explore the relationship between ocular symptoms, artificial tear use, and DED diagnostic signs.
• To investigate symptom clusters and their association with disease characteristics.

Main Methods:

• Retrospective analysis of 16 ocular symptoms and artificial tear (AT) use.
• Logistic regression models were employed for univariable and multivariable analyses.
• Hierarchical cluster analysis was used to identify distinct symptom-based groups.

Main Results:

• Artificial tear use, eye redness, and blurred vision were positively associated with abnormal ocular surface staining (OSS).
• Dry eye sensation, AT use, and blurred vision correlated with abnormal Schirmer test (ST) results.
• Cluster analysis identified three groups with varying symptom burdens, with a mild-moderate cluster showing more abnormal ocular signs and positive serologies.

Conclusions:

• Artificial tear use and blurred vision are stronger indicators of ocular surface and tear film abnormalities than pain.
• Symptom-based clustering reveals heterogeneity in sicca presentations, potentially linked to neurotrophic/neuropathic mechanisms.
• These findings underscore the need for phenotype-based targeted therapies for DED.