The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada.
Department of Ophthalmology & Vision Sciences, University of Toronto, Toronto, ON, Canada.
Transl Vis Sci Technol. 2023 Jun 1;12(6):27. doi: 10.1167/tvst.12.6.27.
To develop a simulation model for glaucomatous longitudinal visual field (VF) tests with controlled progression rates.
Longitudinal VF tests of 1008 eyes from 755 patients with glaucoma were used to learn the statistical characteristics of VF progression. The learned statistics and known anatomic correlations between VF test points were used to automatically generate progression patterns for baseline fields of patients with glaucoma. VF sequences were constructed by adding spatially correlated noise templates to the generated progression patterns. The two one-sided test (TOST) procedure was used to analyze the equivalence between simulated data and data from patients with glaucoma. VF progression detection rates in the simulated VF data were compared to those in patients with glaucoma using mean deviation (MD), cluster, and pointwise trend analysis.
VF indices (MD, pattern standard deviation), MD linear regression slopes, and progression detection rates for the simulated and patients' data were practically equivalent (TOST P < 0.01). In patients with glaucoma, the detection rates in 7 years using MD, cluster, and pointwise trend analysis were 24.4%, 26.2%, and 38.4%, respectively. In the simulated data, the mean detection rates (95% confidence interval) for MD, cluster, and pointwise trend analysis were 24.7% (24.1%-25.2%), 24.9% (24.2%-25.5%), and 35.7% (34.9%-36.5%), respectively.
A novel simulation model generates glaucomatous VF sequences that are practically equivalent to longitudinal VFs from patients with glaucoma.
Simulated VF sequences with controlled progression rates can support the evaluation and optimization of methods to detect VF progression and can provide guidance for the interpretation of longitudinal VFs.
开发一种具有可控进展率的青光眼纵向视野（VF）测试模拟模型。
使用来自 755 名青光眼患者的 1008 只眼的纵向 VF 测试数据来学习 VF 进展的统计特征。使用所学的统计数据和 VF 测试点之间已知的解剖相关性，为青光眼患者的基线视野自动生成进展模式。通过将空间相关的噪声模板添加到生成的进展模式中，构建 VF 序列。使用双侧单边检验（TOST）程序分析模拟数据与青光眼患者数据之间的等效性。使用平均偏差（MD）、簇和逐点趋势分析比较模拟 VF 数据中的 VF 进展检测率与青光眼患者的数据。
VF 指数（MD、模式标准差）、MD 线性回归斜率以及模拟和患者数据的进展检测率在实践中是等效的（TOST P < 0.01）。在青光眼患者中，使用 MD、簇和逐点趋势分析在 7 年内的检测率分别为 24.4%、26.2%和 38.4%。在模拟数据中，MD、簇和逐点趋势分析的平均检测率（95%置信区间）分别为 24.7%（24.1%-25.2%）、24.9%（24.2%-25.5%）和 35.7%（34.9%-36.5%）。
一种新颖的模拟模型生成的青光眼 VF 序列在实践上与青光眼患者的纵向 VF 相当。
温迪
