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,  < 0.001; 64.6 ± 20.3 vs 73.4 ± 21.9 nm,  = 0.003; 0.231 ± 0.115 vs 0.289 ± 0.164,  = 0.012, respectively). Conversely, no significant differences were observed for MGL and blinking analysis (both  > 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,  = 0.002) and blinking analysis (R = 0.356,  < 0.001), while blinking analysis was negatively correlated with MGL (R = −0.315,  = 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.

中文翻译：

---

一种新型无创检查在干眼病环境中的诊断性能

目的。评估用于诊断干眼病 (DED) 的新型无创自动化检查的诊断性能。方法。一百名患有轻度至中度 DED 的患者和 100 名匹配的对照受试者参加了这项横断面研究。眼表检查通过 IDRA Plus (SBM Sistemi, Turin, Italy) 进行，它可以自动评估无创破裂时间 (NIBUT)、脂质层厚度 (LLT)、泪液半月板高度 (TMH)、红外光成像睑板腺损失（MGL）的测量和眨眼分析。使用 Mann-Whitney U比较 DED 患者和对照组之间的连续变量测试。计算受试者工作特征曲线的曲线下面积（AUC）。用Pearson相关分析评估眼表参数之间的相关性。结果。与对照组相比，DED 患者的 NIBUT、LLT 和 TMH 值显着降低（6.9 ± 2.5 vs 10.4 ± 2.4 s， < 0.001；64.6 ± 20.3 vs 73.4 ± 21.9 nm， = 0.003；0.231 ± 0.115 vs 0.289 ± 0.164， = 0.012，分别）。相反，MGL 和眨眼分析没有观察到显着差异（两者 > 0.05)。NIBUT 的诊断能力最高（AUC = 0.841，敏感性 = 0.89，特异性 = 0.69），其次是 LLT（AUC = 0.621，敏感性 = 0.89，特异性 = 0.55），TMH（AUC = 0.606，敏感性 = 0.57，和特异性 = 0.63），眨眼分析（AUC = 0.533，敏感性 = 0.48，特异性 = 0.59）和 MGL（AUC = 0.531，敏感性 = 0.54，特异性 = 0.48）。在 DED 患者中，NIBUT 与 TMH ( R  = 0.347,  = 0.002) 和眨眼分析 ( R  = 0.356,  < 0.001) 显着相关，而眨眼分析与 MGL ( R  = -0.315,  = 0.008) 呈负相关。结论. 本研究中验证的自动无创检查可能是一种有用的工具，可用于实现具有良好性能的 DED 无创诊断，尤其是对于 NIBUT。