Centre for Eye Health and the School of Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia.
Centre for Eye Health and the School of Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia.
Am J Ophthalmol. 2020 Jan;209:117-131. doi: 10.1016/j.ajo.2019.08.014. Epub 2019 Aug 27.
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).
Cross-sectional study.
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.
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).
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.
应用计算方法建立正常年龄相关的角膜参数模型，并探讨其与人口统计学因素的相关性，从而为提高亚临床角膜扩张（SCE）的检测能力提供框架。
横断面研究。
从澳大利亚悉尼眼健康中心招募了 117 名健康参与者。使用 Pentacam HR 从每个参与者的 1 只眼中的 57 个角膜位置提取角膜厚度（CT）、前表面矢状曲率（FSSC）和后表面矢状曲率（BSSC）测量值。采用聚类分析确定具有相似年龄变化的位置。采用多项式回归和滑动窗口方法对年龄相关性变化进行建模，并通过 Bland-Altman 比较验证模型准确性。采用 Pearson 相关性分析检验人口统计学因素的影响。
观察到 CT 和 FSSC 呈同心聚类模式，但 BSSC 无此模式。滑动窗口分析最适合 CT 和 FSSC/BSSC 的四次方和三次方回归模型，CT 和 FSSC 滑动窗口模型的 95%一致性界限比基于十年的模型更窄（分别为 0.015 mm 与 0.017 mm 和 0.14 mm 与 0.27 mm），但 BSSC 比基于十年的模型更宽（0.73 mm 与 0.54 mm）。CT 与散光（P=0.02-0.049）和 FSSC 与 BSSC 与性别（P<0.001-0.049）之间存在显著相关性。
所开发的模型能够稳健地描述 CT 和 FSSC 的老化变化；然而，其他机制似乎对 BSSC 的变化有贡献。这些发现和确定的相关性为未来的模型开发和正常数据库的建立提供了一个框架，以促进 SCE 的检测。
