Development of a dry eye index as a new biomarker of dry eye disease

Abstract

Purpose: To evaluate signs and symptoms in patients diagnosed with dry eye disease (DED), divided into dry eye (DE) groups, in order to find a new biomarker that allows an accurate diagnosis, management and classification of DED. Methods: This cross-sectional, observational study included 71 DED subjects. Subjective symptoms, visual quality and DE signs were assessed using the Ocular Surface Disease Index (OSDI), the Quality of Vision (QoV) questionnaire, best corrected distance visual acuity (VA), functional visual acuity (FVA), contrast sensitivity (CS), high-and low-order corneal aberrations (HOA and LOA, respectively), tear break-up time (TBUT), Meibomian Gland Dysfunction (MGD), Schirmer test, corneal staining, lid wiper epitheliopathy (LWE) and meibography. Participants were classified into three groups based on dryness severity using a cluster analysis, i.e., mild (N = 17, 55.8 ± 15.4 years), moderate (N = 41, 63.5 ± 10.6 years) and severe (N = 13, 65.0 ± 12.0). A new Dry Eye Severity Index (DESI) based on ocular surface signs has been developed and its association with symptoms, visual quality and signs was assessed. Comparisons between groups were made using Kruskal–Wallis and Chi-squared tests. Spearman correlation analysis was also performed. Results: The DESI was based on three tests for DE signs: TBUT, Schirmer test and MGD. The DESI showed significant differences between different pairs of groups: Mild Dryness versus Moderate Dryness (p < 0.001), Mild Dryness versus Severe Dryness (p < 0.001) and Moderate Dryness versus Severe Dryness (p < 0.001). The DESI was significantly correlated with age (rho = −0.30; p = 0.01), OSDI score (rho = −0.32; p = 0.007), QoV score (rho = −0.35; p = 0.003), VA (rho = −0.34; p = 0.003), FVA (rho = −0.38; p = 0.001) and CS (rho = 0.42; p < 0.001) Also, significant differences between the severity groups were found for OSDI and QoV scores, VA, FVA, CS and MGD (p < 0.05). Conclusions: The DESI has good performance as a biomarker for the diagnosis, classification and management of DED.