An Emerging Method to Assess Tear Film Spread and Dynamics as Possible Tear Film Homeostasis Markers. (2nd September 2021)

• Record Type: Journal Article
• Authors: García-Marqués, José Vicente; Talens-Estarelles, Cristian; Martínez-Albert, Noelia; García-Lázaro, Santiago; Cerviño, Alejandro
• Is Part Of: Current eye research. Volume 46:Number 9(2021)
• Page Start: 1291
• Page End: 1298
• Publication Date: 2021-09-02
• Subjects: Dry Eye -- objective medical image analysis -- tear film -- tear hydrodynamics -- tear film homeostasis; Ophthalmology -- Periodicals; Eye -- Diseases -- Periodicals; Ophthalmology -- Periodicals; 573.88
• DOI: 10.1080/02713683.2021.1887270
• Languages: English
• ISSNs: 0271-3683

Abstract

ABSTRACT: Purpose : This study aims to assess the performance of an analysis method to measure in vivo the movement speed of tear film particles post-blink as a measure of tear film spreading. Materials and methods : Ocular surface parameters and the recording of tear film particles' spreading post-blink were assessed in eighty-one healthy volunteers (43.7 ± 27.0 years) using Keratograph 5 M. The developed software automatically decomposed the video into frames to manually track particles' position for 1.75 seconds after a blink. The following tear film-dynamic metrics were automatically calculated: mean, median, maximum, and minimum particles' speed at different times after blinking and time for particle speed to decrease to <1.20 mm/second. Repeatability of each tear film-dynamic metric and its correlations with ocular surface signs and symptoms were analyzed. Binomial logistic regression was performed to assess the predictability of new metrics to ocular parameters. Results : Repeatability tended to be lower just after blinking (variability of 12.24%), whereas the metrics from 0.5 s onwards had acceptable repeatability (variability below 10%). Tear film-dynamic metrics correlated positively with Non-Invasive Break-Up Time (NIKBUT) while negatively with meibomian gland drop-out. Binomial logistic regression analysis revealed that tear film-dynamic metrics were able to predict NIKBUT. Nevertheless, no statistically significant association was found with gland drop-out. This means that higher particle speed is related to larger NIKBUT. The metric "time for particle speed to decrease to <1.20 mm/second" can be considered the best metric to assess the quality of the tear film, since it was more strongly correlated with NIKBUT (r = 0.42, p = .004), it was more strongly associated in the binomial logistic regression analysis with NIKBUT and showed good repeatability (variability = 5.49%). Conclusions : Tear film-dynamic metrics are emerging homeostasis parameters for assessing indirectly the tear film quality in natural conditions with acceptable repeatability.