Su Tai-Yuan, Chang Shu-Wen
Department of Electrical Engineering, Yuan-Ze University, Chung-Li, Taiwan.
Department of Ophthalmology, Far Eastern Memorial Hospital, Banchiao District, New Taipei City, Taiwan; Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan.
Ocul Surf. 2021 Jan;19:74-82. doi: 10.1016/j.jtos.2020.04.002. Epub 2020 Apr 13.
To construct normalized ocular surface temperature (NOST) models for different tear film characteristics and evaluate its potential in dry-eyes screening.
We included 227 right eyes of 227 patients. Tear film characteristics were categorized into 4 types according to fluorescein tear film breakup time (FTBUT) and Schirmer test results, namely type 1: FTBUT >5 s and Schirmer >5 mm; type 2: FTBUT ≤5 s and Schirmer >5 mm; type 3: FTBUT ≤5 s and Schirmer ≤5 mm; and type4: FTBUT >5 s and Schirmer ≤5 mm. Ocular surface temperature was measured by a video-thermographer. Mean temperatures of the central cornea were calculated from the videos of each frame during the 4-s blink interval. We first constructed individual NOST model for every tear characteristic. Participants were included for further analysis when their OSDI was ≥23, FTBUT ≤5 s, and Schirmer >5 mm. They were subdivided into short-BUT and short BUT with SPK subgroups according to the absence or presence of corneal fluorescein-stain. The NOST models of the normal, short-BUT and short BUT with SPK groups were separately constructed and the potential of screening analyzed via ROC curves.
Each tear film type had a different NOST model. At 3 s after blinking, the order of NOST was type 4 >type 1 >type 3 >type 2. In dry-eye screening, the NOST was normal > short-BUT > short BUT with SPK. The NOST displayed a sensitivity 0.87, specificity 0.80, and AUC 0.88 for diagnosing short BUT with SPK.
NOST models are useful in differentiating tear film characteristics and screening dry-eyes. It alleviates the discomfort and inconvenience encountered during conventional dry-eye diagnosis.
构建针对不同泪膜特征的标准化眼表温度（NOST）模型，并评估其在干眼筛查中的潜力。
纳入227例患者的227只右眼。根据荧光素泪膜破裂时间（FTBUT）和泪液分泌试验结果，将泪膜特征分为4种类型，即1型：FTBUT>5秒且泪液分泌试验>5毫米；2型：FTBUT≤5秒且泪液分泌试验>5毫米；3型：FTBUT≤5秒且泪液分泌试验≤5毫米；4型：FTBUT>5秒且泪液分泌试验≤5毫米。使用视频热成像仪测量眼表温度。在4秒眨眼间隔期间，从每一帧视频中计算中央角膜的平均温度。我们首先为每种泪膜特征构建个体NOST模型。当参与者的眼表疾病指数（OSDI）≥23、FTBUT≤5秒且泪液分泌试验>5毫米时，纳入进一步分析。根据角膜荧光素染色的有无，将他们分为短泪膜破裂时间组和伴有角膜点状上皮糜烂的短泪膜破裂时间组。分别构建正常组、短泪膜破裂时间组和伴有角膜点状上皮糜烂的短泪膜破裂时间组的NOST模型，并通过ROC曲线分析筛查潜力。
每种泪膜类型都有不同的NOST模型。眨眼后3秒，NOST的顺序为4型>1型>3型>2型。在干眼筛查中，NOST正常>短泪膜破裂时间>伴有角膜点状上皮糜烂的短泪膜破裂时间。NOST诊断伴有角膜点状上皮糜烂的短泪膜破裂时间的灵敏度为0.87，特异性为0.80，曲线下面积（AUC）为0.88。
NOST模型有助于区分泪膜特征并筛查干眼。它减轻了传统干眼诊断过程中遇到的不适和不便。
