Differences in Ocular Biometric Measurements among Subtypes of Primary Angle Closure Disease: The Chinese American...

To assess differences in ocular biometric measurements between primary angle closure suspect (PACS) eyes and primary angle closure (PAC) and primary angle closure glaucoma (PACG) eyes.

Cross-sectional study.

Patients with primary angle closure disease (PACD) were identified from the Chinese American Eye Study, a population-based study in Los Angeles, California.

Patients previously underwent complete ocular examinations including gonioscopy and anterior segment (AS)-OCT imaging with the Tomey CASIA SS-1000 (Tomey Corporation). Four AS-OCT images were analyzed per eye. Averaged and sectoral measurements of biometric parameters, including angle recess area (ARA), trabecular iris space area (TISA), iris area, iris curvature, lens vault, anterior chamber depth, and anterior chamber area, were compared between early PACD (PACS) and late PACD (PAC and PACG) groups. Machine learning classifiers that attempt to differentiate between early and late PACD eyes were developed by applying different regression algorithms to a training dataset of sectoral parameter measurements. Classifier performance was assessed using an independent test dataset.

Averaged and sectoral measurements of biometric parameters.

Two hundred ninety-eight eyes (231 PACS, 67 PAC or PACG) of 298 patients were analyzed. No difference was found in averaged biometric measurements between the 2 groups before (P > 0.09) or after (P > 0.14) adjusting for age and gender. Differences (P < 0.04) between the 2 groups were found for 11 sectoral parameter measurements, including ARA and TISA. The performance of machine learning classifiers developed using sectoral parameter measurements was poor on the independent test dataset for all regression algorithms (area under the receiver operating characteristic curve, 0.529-0.628).

Differences in biometric measurements between subtypes of PACD eyes were small in a population-based cohort of Chinese Americans. The poor performance of classifiers based on these measurements highlights potential challenges of developing quantitative methods to detect late PACD.