Centre for Eye Health, University of New South Wales, Kensington, Australia; School of Optometry and Vision Science, University of New South Wales, Kensington, Australia.
School of Optometry and Vision Science, University of New South Wales, Kensington, Australia.
Ophthalmol Glaucoma. 2020 Jul-Aug;3(4):274-287. doi: 10.1016/j.ogla.2020.04.009. Epub 2020 Apr 26.
To use factor analysis to visualize and assess the reproducibility and consistency of clinical quantitative parameters that can optimally distinguish among healthy, glaucoma suspect, and manifest glaucoma patients at a cross-sectional level and thus to describe the transition of quantitative change among the diagnostic categories.
Retrospective cross-sectional study.
The medical records of healthy, glaucoma suspect, and manifest glaucoma patients (diagnosed by expert clinicians) seen at the Centre for Eye Health in 2015 (n = 148, n = 664, and n = 129, respectively) and 2018 (n = 242, n = 464, and n = 126, respectively) were reviewed. One eye was selected for the study.
Quantitative clinical measures (intraocular pressure [IOP], central corneal thickness [CCT], visual field [VF], and OCT) were extracted and binary logistic (backward stepwise) regression was performed to identify factors that dictated separation between diagnostic pairs. These were used systematically as inputs for factor analysis to determine a final model that could potentially predict a clinical diagnosis.
Intraocular pressure, CCT, VF (mean deviation and pattern standard deviation) indices, and OCT optic nerve head parameters and thickness values (retinal nerve fiber layer [RNFL] and ganglion cell-inner plexiform layer).
Few clinical parameters were identified commonly as significant across all diagnostic pairings for 2015 (3 of 23: IOP, pattern standard deviation, and 7-o'clock RNFL thickness) and 2018 (1 of 23: vertical cup-to-disc ratio). Few parameters overlapped when comparing 2015 and 2018 results, highlighting inconsistencies in the models between years. Factor analysis showed good separation between healthy persons and glaucoma patients. Using biplots to visualize the data in 2-dimensional clusters, glaucoma suspect patients demonstrated substantial overlap with healthy and glaucoma cohorts. The contributions of each parameter to diagnostic separation changed between groups and years.
Despite advances in quantitative ocular imaging and perimetry, the transition among healthy, glaucoma suspect, and manifest glaucoma patients remains confounded by a lack of consistent, reproducible combinations of quantitative clinical criteria. These results highlight the nebulousness (at patient-, instrument-, and clinician-related levels) of glaucoma diagnosis that remains contingent on individual clinical expertise and assessment.
使用因子分析直观呈现和评估临床定量参数在横断面上的可重复性和一致性，这些参数能够最佳地区分健康人群、青光眼疑似患者和显性青光眼患者，从而描述诊断类别之间定量变化的转变。
回顾性横断面研究。
2015 年（分别为 n=148、n=664 和 n=129）和 2018 年（分别为 n=242、n=464 和 n=126）在眼健康中心就诊的健康人群、青光眼疑似患者和显性青光眼患者（由专家临床医生诊断）的病历被回顾。每只眼都被纳入研究。
提取定量临床指标（眼压[IOP]、中央角膜厚度[CCT]、视野[VF]和 OCT），并进行二元逻辑（逐步向后）回归，以确定决定诊断对之间分离的因素。这些因素被系统地用作因子分析的输入，以确定一个潜在的可预测临床诊断的最终模型。
IOP、CCT、VF（平均偏差和模式标准差）指数以及 OCT 视神经头参数和厚度值（视网膜神经纤维层[RNFL]和节细胞内丛状层）。
在 2015 年（3 个指标中有 23 个：IOP、模式标准差和 7 点钟的 RNFL 厚度）和 2018 年（23 个中有 1 个：垂直杯盘比）的所有诊断配对中，很少有临床参数被普遍确定为重要指标。当比较 2015 年和 2018 年的结果时，很少有参数重叠，这突出了多年来模型之间的不一致性。因子分析显示健康人群和青光眼患者之间有很好的分离。使用双标图将数据直观呈现为二维聚类，青光眼疑似患者与健康人群和青光眼患者群有很大的重叠。每个参数对诊断分离的贡献在组间和年间发生变化。
尽管在定量眼部成像和视野检查方面取得了进展，但健康人群、青光眼疑似患者和显性青光眼患者之间的转变仍然受到缺乏一致、可重复的定量临床标准组合的困扰。这些结果突出了青光眼诊断的模糊性（在患者、仪器和临床医生相关水平上），仍然取决于个体的临床专业知识和评估。
