Janelle Tong1, Jack Phu1, Michael Kalloniatis1, Barbara Zangerl2. 1. Centre for Eye Health and the School of Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia. 2. Centre for Eye Health and the School of Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia. Electronic address: bzangerl@cfeh.com.au.
Abstract
PURPOSE: To apply computational methods to model normal age-related changes in corneal parameters and to establish their association with demographic factors, thereby providing a framework for improved detection of subclinical corneal ectasia (SCE). DESIGN: Cross-sectional study. METHODS: One hundred seventeen healthy participants were enrolled from Centre for Eye Health (Sydney, Australia). Corneal thickness (CT), front surface sagittal curvature (FSSC), and back surface sagittal curvature (BSSC) measurements were extracted from 57 corneal locations from 1 eye per participant using the Pentacam HR. Cluster analyses were performed to identify locations demonstrating similar variations with age. Age-related changes were modeled using polynomial regression with sliding window methods, and model accuracy was verified with Bland-Altman comparisons. Pearson correlations were applied to examine the impacts of demographic factors. RESULTS: Concentric cluster patterns were observed for CT and FSSC but not for BSSC. Sliding window analyses were best fit with quartic and cubic regression models for CT and FSSC/BSSC, respectively. CT and FSSC sliding window models had narrower 95% limits of agreement compared with decade-based models (0.015 mm vs 0.017 mm and 0.14 mm vs 0.27 mm, respectively), but were wider for BSSC than decade-based models (0.73 mm vs 0.54 mm). Significant correlations were observed between CT and astigmatism (P = .02-.049) and FSSC and BSSC and gender (P = <.001-.049). CONCLUSIONS: The developed models robustly described aging variations in CT and FSSC; however, other mechanisms appear to contribute to variations in BSSC. These findings and the identified correlations provide a framework that can be applied to future model development and establishment of normal databases to facilitate SCE detection.
PURPOSE: To apply computational methods to model normal age-related changes in corneal parameters and to establish their association with demographic factors, thereby providing a framework for improved detection of subclinical corneal ectasia (SCE). DESIGN: Cross-sectional study. METHODS: One hundred seventeen healthy participants were enrolled from Centre for Eye Health (Sydney, Australia). Corneal thickness (CT), front surface sagittal curvature (FSSC), and back surface sagittal curvature (BSSC) measurements were extracted from 57 corneal locations from 1 eye per participant using the Pentacam HR. Cluster analyses were performed to identify locations demonstrating similar variations with age. Age-related changes were modeled using polynomial regression with sliding window methods, and model accuracy was verified with Bland-Altman comparisons. Pearson correlations were applied to examine the impacts of demographic factors. RESULTS: Concentric cluster patterns were observed for CT and FSSC but not for BSSC. Sliding window analyses were best fit with quartic and cubic regression models for CT and FSSC/BSSC, respectively. CT and FSSC sliding window models had narrower 95% limits of agreement compared with decade-based models (0.015 mm vs 0.017 mm and 0.14 mm vs 0.27 mm, respectively), but were wider for BSSC than decade-based models (0.73 mm vs 0.54 mm). Significant correlations were observed between CT and astigmatism (P = .02-.049) and FSSC and BSSC and gender (P = <.001-.049). CONCLUSIONS: The developed models robustly described aging variations in CT and FSSC; however, other mechanisms appear to contribute to variations in BSSC. These findings and the identified correlations provide a framework that can be applied to future model development and establishment of normal databases to facilitate SCE detection.
Authors: Janelle Tong; Jack Phu; David Alonso-Caneiro; Sieu K Khuu; Michael Kalloniatis Journal: Transl Vis Sci Technol Date: 2022-04-01 Impact factor: 3.048