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Journal Ophthalmology
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Real-World Diagnostic Performance of a Novel Noninvasive Work-up in the Setting of Dry Eye Disease Journal of Ophthalmology Luca Vigo,1* Marco Pellegrini,2* Federico Bernabei,2 Francesco Carones,1 Vincenzo Scorcia,3 Giuseppe Giannaccare.3 From: 1 2
Carones Ophthalmology Center, 20122 Milan, Italy Ophthalmology Unit, S.Orsola-Malpighi University Hospital, University of Bologna, 40138
Bologna, Italy 3
Department of Ophthalmology, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
*
Equally contributed to this work and share primary authorship.
Corresponding Author: Giuseppe Giannaccare, MD, PhD, FEBOphth Professor of Ophthalmology Department of Ophthalmology, University of “Magna Græcia”, Catanzaro, Italy Address: Viale Europa, 88100, Germaneto, Catanzaro Tel: +39 0961 3647110 Fax: +39 0961 3647094 E-mail: [email protected]
ABSTRACT Purpose: To evaluate the diagnostic performance of a novel noninvasive automated work-up employed for the diagnosis of dry eye disease (DED). Methods: One hundred patients with mild to moderate DED and 100 matched controls subjects were enrolled in this cross-sectional study. Ocular surface examinations were carried out by means of IDRA Plus (SBM Sistemi, Turin, Italy), which allows the automated evaluation of non-invasive break-up time (NIBUT), lipid layer thickness (LLT), tear meniscus height (TMH), infrared meibography for the measurement of meibomian gland loss (MGL) and blinking analysis. Continuous variables were compared between patients with DED and controls by using the Mann–Whitney U test. The area under the curve (AUC) of receiver operating characteristic curves was calculated. The correlations between ocular surface parameters were evaluated with Pearson correlation analysis. Results: Patients with DED showed significantly lower values of NIBUT, LLT and TMH compared to controls (respectively, 6.9 ± 2.5 vs 10.4 ± 2.4 s, P < 0.001; 64.6 ± 20.3 vs 73.4 ± 21.9 nm, P = 0.003; 0.231 ± 0.115 vs 0.289 ± 0.164; P = 0.012). Conversely, no significant differences were observed for MGL and blinking analysis (both P > 0.05). NIBUT had the highest diagnostic power (AUC = 0.841, sensitivity = 0.89, specificity = 0.69), followed by LLT (AUC = 0.621, sensitivity = 0.89, specificity = 0.55), TMH (AUC = 0.606, sensitivity = 0.57, specificity = 0.63), blink analysis (AUC = 0.533, sensitivity = 0.48, specificity = 0.59) and MGL (AUC = 0.531, sensitivity = 0.54, specificity = 0.48). In patients with DED, NIBUT showed a significant correlation with TMH (R = 0.347, P = 0.002) and blinking analysis (R = 0.356, P 0.05). The AUC of ROC curves along with optimal cut-off values with corresponding sensitivity and specificity of the ocular surface parameters analyzed are reported in Table 2: NIBUT had the highest diagnostic power (AUC = 0.841, sensitivity = 0.89, specificity = 0.69), followed by lipid layer thickness (AUC = 0.621, sensitivity = 0.89, specificity = 0.55), tear meniscus height (AUC = 0.606, sensitivity = 0.57, specificity = 0.63), blinking analysis (AUC = 0.533, sensitivity = 0.48, specificity = 0.59) and meibomian gland loss (AUC = 0.531, sensitivity = 0.54, specificity = 0.48). Figure 2 shows the ROC curves of NIBUT, lipid layer thickness and tear meniscus height. In patients with DED, NIBUT showed a significant correlation with tear meniscus height (R = 0.347, P = 0.002) and blinking analysis (R = 0.356, P