Person:

Abelson, Mark

Background: The goal of this study was to assess the effect of a controlled adverse environment (CAE) challenge on subjects with both allergic conjunctivitis and dry eye. Methods: Thirty-three subjects were screened and 17 completed this institutional review board-approved study. Subjects underwent baseline ocular assessments and conjunctival allergen challenge (CAC) on days 0 and 3. Those who met the ocular redness and itching criteria were randomized to receive either the controlled adverse environment (CAE) challenge (group A, n = 9) or no challenge (group B, n = 8) at day 6. Thirty minutes after CAE/no-CAE, subjects were challenged with allergen and their signs and symptoms graded. Exploratory confocal microscopy was carried out in a subset of subjects at hourly intervals for 5 hours post-CAC on days 3 and 6. Results: Seven minutes post-CAC, subjects exposed to the CAE had significantly greater itching (difference between groups, 0.55 ± 0.25, P = 0.028), conjunctival redness (0.59 ± 0.19, P = 0.002), episcleral redness (0.56 ± 0.19, P = 0.003) and mean overall redness (mean of conjunctival, episcleral, and ciliary redness, 0.59 ± 0.14, P < 0.001). The mean score at 7, 15, and 20 minutes post-CAC for conjunctival redness (0.43 ± 0.17, P = 0.012), episcleral redness (0.49 ± 0.15, P = 0.001), mean overall redness in all regions (0.43 ± 0.15, P = 0.005), and mean chemosis (0.20 ± 0.08, P = 0.017) were also all significantly greater in CAE-treated subjects. Confocal microscopic images of conjunctival vessels after CAC showed more inflammation in CAE-treated subjects. Conclusion: In subjects with both dry eye and allergic conjunctivitis, exposure to adverse environmental conditions causes an ocular surface perturbation that can intensify allergic reactions.

Purpose To classify blinks in dry eye and normal subjects into six subtypes, and to define the blink rate and duration within each type of blink, as well as the total lid-contact time/minute. Materials and methods This was a single-centered, prospective, double-blind study of eleven dry-eye and ten normal subjects. Predefined subjects watched a video while blinks were recorded for 10 minutes. Partial blinks were classified by percentage closure of maximal palpebral fissure opening: 25%, 50%, 75%. Complete blinks were characterized as full (>0 seconds), extended (>0.1 seconds), or superextended (>0.5 seconds). The mean duration of each type of blink was determined and standardized per minute as total lid-contact time. Results: Total blinks observed were 4,990 (1,414 normal, 3,756 dry eye): 1,809 (50.59%) partial and 1,767 (49.41%) complete blinks among dry-eye subjects versus 741 (52.90%) partial and 673 (47.60%) complete blinks among normal subjects. Only superextended blinks of ≥0.5-second duration were significantly more frequent in dry-eye subjects than normals (2.3% versus 0.2%, respectively; P=0.023). Total contact time was seven times higher in dry-eye subjects than normals (0.565 versus 0.080 seconds, respectively; P<0.001). Isolating only extended blinks (>0.1 second), the average contact time (seconds) was four times longer in dry-eye versus normal subjects (2.459 in dry eye, 0.575 in normals; P=0.003). Isolating only superextended blinks (>0.5 seconds), average contact time was also significantly different (7.134 in dry eye, 1.589 in normals; P<0.001). The contact rate for all full closures was 6.4 times longer in dry-eye (0.045 versus 0.007, P<0.001) than normal subjects. Conclusion: Dry-eye subjects spent 4.5% of a minute with their eyes closed, while normal subjects spent 0.7% of a minute with their eyes closed. Contact time might play a role in the visual function decay associated with increased blink rates.

Background: We have observed that dry eye redness is characterized by a prominence of fine horizontal conjunctival vessels in the exposed ocular surface of the interpalpebral fissure, and have incorporated this feature into the grading of redness in clinical studies of dry eye. Aim To develop an automated method of grading dry eye-associated ocular redness in order to expand on the clinical grading system currently used. Methods: Ninety nine images from 26 dry eye subjects were evaluated by five graders using a 0–4 (in 0.5 increments) dry eye redness (Ora Calibra™ Dry Eye Redness Scale [OCDER]) scale. For the automated method, the Opencv computer vision library was used to develop software for calculating redness and horizontal conjunctival vessels (noted as “horizontality”). From original photograph, the region of interest (ROI) was selected manually using the open source ImageJ software. Total average redness intensity (Com-Red) was calculated as a single channel 8-bit image as R – 0.83G – 0.17B, where R, G and B were the respective intensities of the red, green and blue channels. The location of vessels was detected by normalizing the blue channel and selecting pixels with an intensity of less than 97% of the mean. The horizontal component (Com-Hor) was calculated by the first order Sobel derivative in the vertical direction and the score was calculated as the average blue channel image intensity of this vertical derivative. Pearson correlation coefficients, accuracy and concordance correlation coefficients (CCC) were calculated after regression and standardized regression of the dataset. Results: The agreement (both Pearson’s and CCC) among investigators using the OCDER scale was 0.67, while the agreement of investigator to computer was 0.76. A multiple regression using both redness and horizontality improved the agreement CCC from 0.66 and 0.69 to 0.76, demonstrating the contribution of vessel geometry to the overall grade. Computer analysis of a given image has 100% repeatability and zero variability from session to session. Conclusion: This objective means of grading ocular redness in a unified fashion has potential significance as a new clinical endpoint. In comparisons between computer and investigator, computer grading proved to be more reliable than another investigator using the OCDER scale. The best fitting model based on the present sample, and usable for future studies, was C4=−12.24+2.12C2HOR+0.88C2RED:C4 is the predicted investigator grade, and C2HOR and C2RED are logarithmic transformations of the computer calculated parameters COM-Hor and COM-Red. Considering the superior repeatability, computer automated grading might be preferable to investigator grading in multicentered dry eye studies in which the subtle differences in redness incurred by treatment have been historically difficult to define.