PURPOSE: To determine whether adjusting corneal hysteresis (CH) values for central corneal thickness (CCT) and intraocular pressure (IOP) improves its capability to differentiate primary open-angle glaucoma (POAG) from ocular hypertension (OH). METHODS: This prospective, observational, cross-sectional study included 169 eyes of 169 subjects with a diagnosis of POAG (n=81) or OH (n=88). We utilized the Ocular Response Analyzer (ORA), Pascal Dynamic Contour Tonometer (DCT), Goldmann applanation tonometer (GAT), and ORA ultrasound pachymeter to obtain CH, IOP, and CCT values. Correlational, regression, and t-test analyses were conducted before and after the sample was divided into low, intermediate, and thick CCT subgroups. RESULTS: In the full sample, CH and CCT were moderately correlated (r=0.44, P<0.001). Although both were related to diagnosis in univariate regression analysis, only CH was independently related to glaucoma diagnosis in multivariate analysis. After the sample was divided into CCT tertiles, CH was significantly lower in POAG vs OH eyes within all three CCT subgroups, and CH was the only multivariate variable that differentiated POAG from OH in each CCT subgroup. Moreover, the relationship between CH and diagnosis was more robust within the CCT subgroups compared with the full sample, suggesting that integrating CCT into CH interpretation is beneficial. Adjusting CH for IOP did not aid diagnostic precision in this study. CONCLUSION: Our findings suggest that combining CH and CCT for glaucoma risk assessment improves diagnostic capability compared to using either factor alone. Conversely, adjusting CH for IOP provided no clear clinical benefit in this study.
PURPOSE: To determine whether adjusting corneal hysteresis (CH) values for central corneal thickness (CCT) and intraocular pressure (IOP) improves its capability to differentiate primary open-angle glaucoma (POAG) from ocular hypertension (OH). METHODS: This prospective, observational, cross-sectional study included 169 eyes of 169 subjects with a diagnosis of POAG (n=81) or OH (n=88). We utilized the Ocular Response Analyzer (ORA), Pascal Dynamic Contour Tonometer (DCT), Goldmann applanation tonometer (GAT), and ORA ultrasound pachymeter to obtain CH, IOP, and CCT values. Correlational, regression, and t-test analyses were conducted before and after the sample was divided into low, intermediate, and thick CCT subgroups. RESULTS: In the full sample, CH and CCT were moderately correlated (r=0.44, P<0.001). Although both were related to diagnosis in univariate regression analysis, only CH was independently related to glaucoma diagnosis in multivariate analysis. After the sample was divided into CCT tertiles, CH was significantly lower in POAG vs OH eyes within all three CCT subgroups, and CH was the only multivariate variable that differentiated POAG from OH in each CCT subgroup. Moreover, the relationship between CH and diagnosis was more robust within the CCT subgroups compared with the full sample, suggesting that integrating CCT into CH interpretation is beneficial. Adjusting CH for IOP did not aid diagnostic precision in this study. CONCLUSION: Our findings suggest that combining CH and CCT for glaucoma risk assessment improves diagnostic capability compared to using either factor alone. Conversely, adjusting CH for IOP provided no clear clinical benefit in this study.
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