The Role of Different Tear Volume Detection Methods in the Evaluation and Diagnosis of Mild Dry Eye Disease

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Abstract

Purpose: To compare the diagnostic power of strip meniscometry (SM), Schirmer test (ST), and tear meniscus (TM) in mild dry eye disease (DED) and to evaluate the association with DED-related parameters. Methods: Forty left eyes with mild DED and 40 left eyes of control participants were investigated. All participants underwent a comprehensive ocular surface examination, including the Ocular Surface Disease Index (OSDI), fluorescein tear film break-up time (FTBUT), ocular surface staining grades, meiboscores, and tear film volume examina-tions, including SM, ST, tear meniscus height (TMH), and tear meniscus cross-sectional area (TMA) measurements, respectively, by optical coherence tomography (OCT) and Keratograph 5M (K5M). The correlation between these parameters was evaluated, and the receiver operating characteristic (ROC) curve was used to verify the diagnostic power by the area under the curve (AUC). Results: All tear film volume examinations significantly correlated with DED parameters. Among them, the most relevant factor to OSDI scores and FTBUT was SM. In addition, SM (AUC = 0.992), TMH-OCT (AUC = 0.978), and TMA-OCT (AUC = 0.960) showed better diagnostic power than ST (AUC = 0.650) in DED, in which the cutoff value of SM was 3.5 mm (sensitivity, 97.5%; specificity, 95.0%). Conclusions: Compared with ST, SM and TM parameters obtained by OCT were more relevant to ocular surface parameters and can provide a more valuable approach to discriminate mild DED from control participants. Translational Relevance: This study made a comprehensive comparison of the existing tear volume detection methods and provided a basis for the clinical selection of appro-priate detection methods and the diagnosis of mild DED.

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Wang, Y., Xu, Z., Gong, Q., Ren, W., Chen, L., Lu, F., & Hu, L. (2022). The Role of Different Tear Volume Detection Methods in the Evaluation and Diagnosis of Mild Dry Eye Disease. Translational Vision Science and Technology, 11(3). https://doi.org/10.1167/tvst.11.3.15

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