Ocular Biometric Determinants of Dark-to-Light Change in Angle Width: The Chinese American Eye Study

Role of Static and Dynamic Ocular Biometrics Measured in the Dark and Light as Risk Factors for Angle Closure Progression

PURPOSE

To assess the role of static and dynamic ocular biometric parameters measured in the dark and light for predicting progression of primary angle closure suspect (PACS) to primary angle closure (PAC).

DESIGN

Retrospective cohort study using prospective randomized controlled trial data from untreated, control eyes.

METHODS

Zhongshan Angle Closure Prevention Trial subjects underwent anterior segment optical coherence tomography (AS-OCT) imaging in the dark and light. Static biometric parameters were measured, consisting of angle, iris, lens, and anterior chamber parameters. Dynamic change parameters were calculated by subtracting light measurements from dark measurements. Cox proportional hazards regression models were developed to assess risk factors for PACD progression.

RESULTS

A total of 861 eyes of 861 participants were analyzed (36 progressors). On univariable analysis, TISA500 measurements in the light and dark were associated with progression (P < .001), whereas dynamic change parameters were not (P ≥ .08). In the primary multivariable model, older age (hazard ratio [HR] = 1.09 per year), higher intraocular pressure (IOP) (HR = 1.13 per mm Hg), and smaller TISA500 in the light (HR = 1.28 per 0.01 mm2) were significantly associated with greater risk of progression (P ≤ .04). Dark TISA500 had similar significance (HR = 1.28, P = .002) when replacing light TISA500. Risk of progression was more predictive among eyes in the lowest quartile of light TISA500 measurements (HR = 4.56, P < .001) compared to dark measurements (HR = 2.89, P = .003).

CONCLUSION

Static parameters measured in the light are as predictive, and possibly more so, of angle closure progression as those measured in the dark. Ocular biometrics measured under light and dark conditions may provide additional information for risk-stratifying patients for angle closure progression.

Classification of Angle Closure Severity by Hierarchical Cluster Analysis of Ocular Biometrics in the Dark and Light

Purpose

The purpose of this study was to investigate the classification of angle closure eyes based on hierarchical cluster analysis of ocular biometrics measured in the dark and light using anterior segment optical coherence tomography (AS-OCT).

Methods

Participants of the Chinese American Eye Study received complete eye examinations to identify primary angle closure suspects (PACS) and primary angle closure without/with glaucoma (PAC/G). AS-OCT was performed in the dark and light. Biometric parameters describing the angle, iris, lens, and anterior chamber were analyzed. Hierarchical clustering was performed using Ward's method. Post hoc logistic regression models were developed to identify biometric predictors of angle closure staging.

Results

Analysis of 159 eyes with PACS (N = 120) or PAC/G (N = 39) produced 2 clusters in the dark and light. In both analyses, cluster 1 (N = 132 in the dark and N = 126 in the light) was characterized by smaller angle opening distance (AOD)750 and trabecular iris space area (TISA)750, greater iris curvature (IC), and greater lens vault (LV; P < 0.001) than cluster 2. The proportion of PAC/PACG to PACS eyes was significantly higher in cluster 1 than 2 in the light (36:90 and 3:30, respectively; P = 0.02), but not the dark (36:96 and 3:24, respectively; P = 0.08). On multivariable regression analyses, smaller TISA750 (odds ratio [OR] = 0.84 per 0.01 mm2) and AOD750 (OR = 0.93 per 0.01 mm) in the light and smaller TISA750 (OR = 0.86 per 0.01 mm2) in the dark conferred higher risk of PAC/G (P ≤ 0.02).

Conclusions

Unsupervised cluster analysis of ocular biometrics can classify angle closure eyes by severity. Static biometrics measured in the light and dark are both predictive of PAC/G.

Translational Relevance

Clustering of biometrics measured in the light could provide an alternative source of information to risk-stratify angle closure eyes for more severe disease.

Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos

Background

To study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance.

Methods

A total of 369 AS-OCT videos (19,940 frames)—159 angle-closure subjects and 210 normal controls (two datasets using different AS-OCT capturing devices)—were included. The correlation between iris changes (pupil constriction) and PACD was analyzed based on dynamic clinical parameters (pupil diameter) under the guidance of a senior ophthalmologist. A temporal network was then developed to learn discriminative temporal features from the videos. The datasets were randomly split into training, and test sets and fivefold stratified cross-validation were used to evaluate the performance.

Results

For dynamic clinical parameter evaluation, the mean velocity of pupil constriction (VPC) was significantly lower in angle-closure eyes (0.470 mm/s) than in normal eyes (0.571 mm/s) (P < 0.001), as was the acceleration of pupil constriction (APC, 3.512 mm/s²vs. 5.256 mm/s²; P < 0.001). For our temporal network, the areas under the curve of the system using AS-OCT images, original AS-OCT videos, and aligned AS-OCT videos were 0.766 (95% CI: 0.610–0.923) vs. 0.820 (95% CI: 0.680–0.961) vs. 0.905 (95% CI: 0.802–1.000) (for Casia dataset) and 0.767 (95% CI: 0.620–0.914) vs. 0.837 (95% CI: 0.713–0.961) vs. 0.919 (95% CI: 0.831–1.000) (for Zeiss dataset).

Conclusions

The results showed, comparatively, that the iris of angle-closure eyes stretches less in response to illumination than in normal eyes. Furthermore, the dynamic feature of iris motion could assist in angle-closure classification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40662-022-00314-1.

Age-Related Changes in Dynamic Iris Behavior Assessed Using a Programmable Closed-Loop Iris Control System

Purpose

The purpose of this study was to develop and test a programmable closed-loop system for tracking, modulating, and assessing dynamic iris behavior, including in the mid-dilated position.

Methods

A programmable closed-loop iris control system was developed by customizing an ANTERION OCT device (Heidelberg Engineering, Heidelberg, Germany). Custom software was developed to store camera and optical coherence tomography (OCT) images, track pupillary diameter (PD), control a light-emitting diode (LED), and modulate ambient lighting to maintain the iris in a dilated, constricted, or mid-dilated position in real-time. Study participants underwent 3 consecutive 65-second scan sessions. Dynamic iris behavior in the form of peak constriction velocity (PCV) and mid-dilated iris activity (MDIA) were calculated and analyzed offline.

Results

Among 58 participants, 56 (96.6%) were eligible for analysis based on achieving and maintaining mean PD within ±10% of the calculated mid-dilated PD. Mean participant age was 49.8 ± 18.9 years. Mean PCV was 3.92 ± 0.83 mm/s, and mean MDIA was 0.37 ± 0.15 mm. The mean difference between the calculated and achieved mid-dilated PD was 0.166 ± 0.192 mm. There were significant negative correlations between PCV and age (slope = -0.022, P < 0.001) and MDIA and age (slope = -0.004, P < 0.001). Success rates were lower (69.0%) but relationships between dynamic iris behavior and age were similar based on achieving and maintaining mean PD within ±5% of the calculated mid-dilated PD.

Conclusions

A programmable closed-loop iris control system can modulate dynamic iris behavior and maintain the iris in a mid-dilated position. Pupillary constriction velocity and iris activity in the mid-dilated position decrease with age.

Translational Relevance

This system can be applied to study dynamic disease processes involving the iris and establish novel biometric measures that could serve as risk factors for acute and chronic primary angle closure glaucoma (PACG).