PURPOSE: To evaluate the relative influences of several demographic, ocular, and systemic parameters on corneal hysteresis (CH). METHODS: This is a prospective, observational, cross-sectional study using subjects recruited from consecutive Albuquerque VAMC eye clinic patients. We classified eligible subjects as primary open-angle glaucoma (POAG), ocular hypertension, glaucoma suspect, or normal. We used the Ocular Response Analyzer, Pascal Dynamic Contour Tonometer, and Goldmann applanation tonometer to obtain intraocular pressure (IOP), CH, corneal resistance factor, and ocular pulse amplitude values. We also obtained corneal curvature, central corneal thickness (CCT), axial length, retinal nerve fiber layer thickness, clinical cup/disc ratio (CDR) estimates, and standard automated perimetry metrics (mean defect, pattern standard deviation). We gathered glycosylated hemoglobin (A1C) data through chart review. Multivariate regression analyses were used to determine independent relationships between CH and the other parameters. RESULTS: Three hundred seventeen eyes in 317 subjects were studied (116 POAG, 87 ocular hypertension, 47 glaucoma suspect, and 67 normal). In univariate regression analysis, CH varied directly with CCT (β = 0.39, p < 0.001), corneal curvature (β = 0.16, p = 0.01), corneal resistance factor (β = 0.57, p < 0.001), A1C (β = 0.15, p = 0.01), mean defect (β = 0.29, p < 0.001), and retinal nerve fiber layer (β = 0.31, p < 0.001). Factors inversely related to CH were age (β = -0.22, p < 0.001), IOP (β = -0.29, p < 0.001), ocular pulse amplitude (β = -0.11, p = 0.04), CDR (β = -0.34, p < 0.001), and pattern standard deviation (β = -0.29, p < 0.001). CH was lower in POAG compared with the other diagnostic groups. In multivariate analysis, CH was independently associated with age, IOP, CCT, A1C, glaucoma diagnosis, and CDR. Of these factors, CCT and IOP demonstrated twice as much influence on CH compared with the other four factors. CONCLUSIONS: Although this study identified six separate variables that independently influence CH values, the overall r value indicates that these variables together only explain 40% of CH variability. These results suggest that other significant sources of variability exist and deserve investigation.
PURPOSE: To evaluate the relative influences of several demographic, ocular, and systemic parameters on corneal hysteresis (CH). METHODS: This is a prospective, observational, cross-sectional study using subjects recruited from consecutive Albuquerque VAMC eye clinic patients. We classified eligible subjects as primary open-angle glaucoma (POAG), ocular hypertension, glaucoma suspect, or normal. We used the Ocular Response Analyzer, Pascal Dynamic Contour Tonometer, and Goldmann applanation tonometer to obtain intraocular pressure (IOP), CH, corneal resistance factor, and ocular pulse amplitude values. We also obtained corneal curvature, central corneal thickness (CCT), axial length, retinal nerve fiber layer thickness, clinical cup/disc ratio (CDR) estimates, and standard automated perimetry metrics (mean defect, pattern standard deviation). We gathered glycosylated hemoglobin (A1C) data through chart review. Multivariate regression analyses were used to determine independent relationships between CH and the other parameters. RESULTS: Three hundred seventeen eyes in 317 subjects were studied (116 POAG, 87 ocular hypertension, 47 glaucoma suspect, and 67 normal). In univariate regression analysis, CH varied directly with CCT (β = 0.39, p < 0.001), corneal curvature (β = 0.16, p = 0.01), corneal resistance factor (β = 0.57, p < 0.001), A1C (β = 0.15, p = 0.01), mean defect (β = 0.29, p < 0.001), and retinal nerve fiber layer (β = 0.31, p < 0.001). Factors inversely related to CH were age (β = -0.22, p < 0.001), IOP (β = -0.29, p < 0.001), ocular pulse amplitude (β = -0.11, p = 0.04), CDR (β = -0.34, p < 0.001), and pattern standard deviation (β = -0.29, p < 0.001). CH was lower in POAG compared with the other diagnostic groups. In multivariate analysis, CH was independently associated with age, IOP, CCT, A1C, glaucoma diagnosis, and CDR. Of these factors, CCT and IOP demonstrated twice as much influence on CH compared with the other four factors. CONCLUSIONS: Although this study identified six separate variables that independently influence CH values, the overall r value indicates that these variables together only explain 40% of CH variability. These results suggest that other significant sources of variability exist and deserve investigation.
Authors: Diane N Sayah; Javier Mazzaferri; Denise Descovich; Santiago Costantino; Mark R Lesk Journal: Invest Ophthalmol Vis Sci Date: 2020-11-02 Impact factor: 4.799