Optimal number and orientation of anterior segment OCT images to measure ocular biometric parameters in angle closure eyes: the Chinese American Eye Study

Shan, Jing; Pardeshi, Anmol A.; Jiang, Xuejuan; Richter, Grace M.; McKean‐Cowdin, Roberta; Varma, Rohit; Xu, Benjamin

doi:10.1136/bjophthalmol-2021-319275

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…In the dark analysis, Cluster 1A (71) was characterized by smaller AOD750 and TISA750; Cluster 1B (61) was characterized by greater IC and LV; Cluster 2 (27) was characterized by greater ACD, IT, and PD. In the light analysis, Cluster 1A (110) was characterized by greater IC; Cluster 1B (16) was characterized by smaller AOD750 and TISA750, ACW, and PD; Cluster 2 (33) was characterized by greater ACD. Similar to the 2-cluster analysis, there was a significant inter-cluster difference in proportion of PAC/G in the light analysis (p = 0.01), but not in the dark analysis (p = 0.07).…”

Section: Resultsmentioning

confidence: 99%

“…While this approach better captures anatomical variations of the angle, it may weaken the effect of specific sectors that are more predictive of disease severity or progression, such as the temporal and nasal sectors. 32,33 A more thorough investigation of these factors in the future may help elucidate differences between the proportion of PAC/G in the light and dark. Finally, CHES participants all self-identified as Chinese American.…”

Section: Discussionmentioning

confidence: 99%

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

Cho

Pablo

Pardeshi

et al. 2023

Preprint

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Purpose: To investigate the classification of eyes with primary angle closure disease (PACD) based on hierarchical cluster analysis of ocular biometrics measured in the dark and light using anterior segment OCT (AS-OCT). Design: Population-based cross-sectional study. Methods: Participants of the Chinese American Study received complete eye exams including gonioscopy and AS-OCT imaging to identify primary angle closure suspects (PACS) and primary angle closure without/with glaucoma (PAC/G). Biometric parameters analyzed included angle opening distance (AOD750) and trabecular iris space area (TISA750) iris area (IA) iris thickness (IT750) iris curvature (IC) lens vault (LV) anterior chamber width (ACW) and anterior chamber depth (ACD). Hierarchical cluster analysis was performed using Wards method and Euclidean distance. Results: Analysis of 159 eyes with PACS or PAC/G produced 2 clusters in both dark and light. In both analyses, the primary cluster (N=132 in the dark, N=126 in the light) was characterized by smaller AOD750 and TISA750, greater IC, and greater LV (p<0.001). The proportion of PACS to PAC/PACG eyes was significantly different between clusters in the light (p=0.02) but not the dark cluster analysis (p=0.08). On multivariable logistic regression analysis, smaller TISA750 (OR=0.84 per 0.01mm2) and AOD750 (OR=0.93 per 0.01um) in the light and smaller TISA750 (OR=0.86 per 0.01um2) in the dark were significantly associated (p less than or equal to 0.02) with higher odds of PAC/G. Conclusion: Unsupervised cluster analysis of ocular biometrics can classify PACD eyes by disease severity. Ocular biometrics appear equally if not more strongly predictive of disease severity when measured in the light than dark. Clustering of biometric measurements obtained in the light could provide a novel method to risk-stratify patients for more severe PACD.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Cho

Pablo

Pardeshi

et al. 2023

Preprint

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“…masked to the identities and examination results of the subjects who confirmed the segmentation and marked the scleral spurs in each image 17. Four images per eye spaced 45 degrees apart were analyzed to capture the majority of each angle’s anatomic variation 18,19. The first image analyzed was oriented along the horizontal (temporal-nasal) meridian.…”

Section: Methodsmentioning

confidence: 99%

“…17 Four images per eye spaced 45 degrees apart were analyzed to capture the majority of each angle's anatomic variation. 18,19 The first image analyzed was oriented along the horizontal (temporal-nasal) meridian. Eyes in which 3 or more of the 9 scleral spurs could not be identified were excluded from the analysis.…”

Section: Ocular Biometrymentioning

confidence: 99%

Refractive Error and Anterior Chamber Depth as Risk Factors in Primary Angle Closure Disease: The Chinese American Eye Study

Zhou

Pardeshi²,

Burkemper³

et al. 2023

Journal of Glaucoma

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Précis: The risk of primary angle closure disease (PACD) rises rapidly with greater hyperopia while remaining relatively low for all degrees of myopia. Refractive error (RE) is useful for angle closure risk stratification in the absence of biometric data. Purpose: To assess the role of RE and anterior chamber depth (ACD) as risk factors in PACD. Methods: Chinese American Eye Study participants received complete eye examinations including refraction, gonioscopy, amplitude-scan biometry, and anterior segment ocular coherence tomography imaging. PACD included primary angle closure suspect (≥3 quadrants of angle closure on gonioscopy) and primary angle closure/primary angle closure glaucoma (peripheral anterior synechiae or intraocular pressure >21 mm Hg). Logistic regression models were developed to assess associations between PACD and RE and/or ACD adjusted for sex and age. Locally weighted scatterplot smoothing curves were plotted to assess continuous relationships between variables. Results: Three thousand nine hundred seventy eyes (3403 open angle and 567 PACD) were included. The risk of PACD increased with greater hyperopia [odds ratio (OR) = 1.41 per diopter (D); P < 0.001] and shallower ACD (OR = 1.75 per 0.1 mm; P < 0.001). Hyperopia (≥ + 0.5 D; OR = 5.03) and emmetropia (−0.5 D to +0.5 D; OR = 2.78) conferred a significantly higher risk of PACD compared with myopia (≤0.5 D). ACD (standardized regression coefficient = −0.54) was a 2.5-fold stronger predictor of PACD risk compared with RE (standardized regression coefficient = 0.22) when both variables were included in one multivariable model. The sensitivity and specificity of a 2.6 mm ACD cutoff for PACD were 77.5% and 83.2% and of a +2.0 D RE cutoff were 22.3% and 89.1%. Conclusion: The risk of PACD rises rapidly with greater hyperopia while remaining relatively low for all degrees of myopia. Although RE is a weaker predictor of PACD than ACD, it remains a useful metric to identify patients who would benefit from gonioscopy in the absence of biometric data.

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“…This approach captures the majority of the anatomic variation along the angle. 24 , 25 Sectoral measurements were averaged, and these average measurements were further analyzed. Eyes missing measurements from more than three of the eight sectors were excluded from the analysis.…”

Section: Methodsmentioning

confidence: 99%

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

Cho,

Lewinger,

Pardeshi

et al. 2023

Trans. Vis. Sci. Tech.

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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 mm 2 ) and AOD750 (OR = 0.93 per 0.01 mm) in the light and smaller TISA750 (OR = 0.86 per 0.01 mm 2 ) 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.

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Optimal number and orientation of anterior segment OCT images to measure ocular biometric parameters in angle closure eyes: the Chinese American Eye Study

Cited by 5 publications

References 28 publications

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

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

Refractive Error and Anterior Chamber Depth as Risk Factors in Primary Angle Closure Disease: The Chinese American Eye Study

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

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