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ABSTRACT: Purpose
To investigate the biometric characteristics of Chinese patients with a history of acute angle closure (AAC).Methods
In this clinic-based, retrospective, observational, cross-sectional study, biometric parameters of eyes were acquired from a general population of Chinese adults. The crowding value (defined as lens thickness (LT); central corneal thickness (CCT); anterior chamber depth (ACD)/axial length (AL)) was calculated for each patient. Logistic regression analysis was performed to identify risk factors for AAC. Receiver operating characteristic (ROC) curves were plotted, and biometric variables were compared to compile a risk assessment for AAC.Result
This study included 1500 healthy subjects (2624 eyes, mean age of 66.54 ± 15.82 years) and 107 subjects with AAC (202 eyes, mean age of 70.01 ± 11.05 years). Eyes with AAC had thicker lens (P ≤ 0.001), shallower anterior chamber depth (P ≤ 0.001), and shorter axial length (P ≤ 0.001) than healthy eyes. Logistic regression analysis and ROC curve analysis indicated that a crowding value above 0.13 was a significant (P < 0.05) risk factor for the development of AAC.Conclusions
Biometric parameters were significantly different between the eyes from the AAC group to the normal group. Ocular crowding value might be a new noncontact screening method to assess the risk of AAC in adults.
SUBMITTER: Niu WR
PROVIDER: S-EPMC6207858 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature

Niu Wei-Ran WR Dong Chun-Qiong CQ Zhang Xi X Feng Yi-Fan YF Yuan Fei F
Journal of ophthalmology 20181017
<h4>Purpose</h4>To investigate the biometric characteristics of Chinese patients with a history of acute angle closure (AAC).<h4>Methods</h4>In this clinic-based, retrospective, observational, cross-sectional study, biometric parameters of eyes were acquired from a general population of Chinese adults. The crowding value (defined as lens thickness (LT); central corneal thickness (CCT); anterior chamber depth (ACD)/axial length (AL)) was calculated for each patient. Logistic regression analysis w ...[more]