Diagnostic Performance of a Novel Noninvasive Workup in the Setting of Dry Eye Disease

• Main Authors: Luca Vigo, Marco Pellegrini, Federico Bernabei, Francesco Carones, Vincenzo Scorcia, Giuseppe Giannaccare
• Format: Article
• Language: English
• Published: Hindawi Limited 2020-01-01
• Series: Journal of Ophthalmology
• Online Access: http://dx.doi.org/10.1155/2020/5804123

Purpose. To evaluate the diagnostic performance of a novel noninvasive automated workup employed for the diagnosis of dry eye disease (DED). Methods. One hundred patients with mild to moderate DED and 100 matched control 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 noninvasive breakup 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 (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, respectively). 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, and specificity = 0.69), followed by LLT (AUC = 0.621, sensitivity = 0.89, and specificity = 0.55), TMH (AUC = 0.606, sensitivity = 0.57, and specificity = 0.63), blink analysis (AUC = 0.533, sensitivity = 0.48, and specificity = 0.59), and MGL (AUC = 0.531, sensitivity = 0.54, and 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 < 0.001), while blinking analysis was negatively correlated with MGL (R = −0.315, P = 0.008). Conclusions. The automated noninvasive workup validated in this study may be a useful tool for reaching a noninvasive diagnosis of DED with a good performance, especially for NIBUT.