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一种新型非侵入性检查在干眼症诊断中的性能

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

作者信息

Vigo Luca, Pellegrini Marco, Bernabei Federico, Carones Francesco, Scorcia Vincenzo, Giannaccare Giuseppe

机构信息

Carones Ophthalmology Center, Milan 20122, Italy.

Ophthalmology Unit, S.Orsola-Malpighi University Hospital, University of Bologna, Bologna 40138, Italy.

出版信息

J Ophthalmol. 2020 Dec 11;2020:5804123. doi: 10.1155/2020/5804123. eCollection 2020.

Abstract

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 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 ( = 0.347,  = 0.002) and blinking analysis ( = 0.356,  < 0.001), while blinking analysis was negatively correlated with MGL ( = -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.

摘要

目的

评估一种用于诊断干眼病(DED)的新型无创自动化检查方法的诊断性能。

方法

本横断面研究纳入了100例轻至中度干眼病患者和100例匹配的对照受试者。通过IDRA Plus(意大利都灵的SBM Sistemi公司)进行眼表检查,该设备可自动评估无创泪膜破裂时间(NIBUT)、脂质层厚度(LLT)、泪河高度(TMH)、用于测量睑板腺缺失(MGL)的红外睑板腺造影以及眨眼分析。使用Mann-Whitney检验比较DED患者和对照组之间的连续变量。计算受试者工作特征曲线的曲线下面积(AUC)。采用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值显著更低(分别为6.9±2.5秒对10.4±2.4秒,P<0.001;64.6±20.3纳米对73.4±21.9纳米,P = 0.003;0.231±0.115对0.289±0.164,P = 0.012)。相反,MGL和眨眼分析未观察到显著差异(均P>0.05)。NIBUT具有最高的诊断效能(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,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(P = 0.347,P = 0.002)和眨眼分析(P = 0.356,P<0.001)显著相关,而眨眼分析与MGL呈负相关(P = -0.315,P = 0.008)。

结论

本研究中验证的自动化无创检查方法可能是一种用于无创诊断DED的有用工具,性能良好,尤其是对于NIBUT。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25c4/7803097/4c2f72b2595c/joph2020-5804123.001.jpg

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