A test of a linear model of glaucomatous structure-function loss reveals sources of variability in retinal nerve fiber and visual field measurements.
A test of a linear model of glaucomatous structure-function loss reveals sources of variability in retinal nerve fiber and visual field measurements.
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DOI：
10.1167/iovs.08-2697
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发表时间：
2009-09
影响因子：
4.4
通讯作者：
Kardon RH
中科院分区：
文献类型：
--
作者：
Hood DC;Anderson SC;Wall M;Raza AS;Kardon RH
在模型的背景下分析青光眼患者的视网膜神经纤维（RNFL）厚度和视野丧失数据，以更好地了解结构与功能的个体差异。对140例青光眼患者和82例正常对照者的一只眼的弓形区进行了光学相干断层扫描（OCT）RNFL厚度和标准自动视野检查（SAP）视野损失的测量。通过在34例患者和22例对照受试者的短时间内不同日期进行重复测量，获得个体内（测量）误差的估计值。一个线性模型，以前示出的结构-功能数据的一般特征描述，扩展到预测数据的变异性。对于正常对照受试者，个体间误差（个体差异）分别占OCT和SAP测量总方差的87%和71%。SAP个体内误差随着SAP丢失的增加而增加，然后减少，而OCT误差保持不变。具有变异性的线性模型（LMV）描述了数据中的大部分变异性。然而，12.5%的患者的点落在95%边界之外。对这些点的检查揭示了可能导致数据总体变异的因素。这些因素包括视网膜前膜、水肿、视野-椎间盘映射的个体差异、血管的位置以及RNFL算法包含血管的程度。该模型和分区内与个体间的变异性有助于阐明的因素，在结构与功能的数据相当大的变异性。
Retinal nerve fiber (RNFL) thickness and visual field loss data from patients with glaucoma were analyzed in the context of a model, to better understand individual variation in structure versus function. Optical coherence tomography (OCT) RNFL thickness and standard automated perimetry (SAP) visual field loss were measured in the arcuate regions of one eye of 140 patients with glaucoma and 82 normal control subjects. An estimate of within-individual (measurement) error was obtained by repeat measures made on different days within a short period in 34 patients and 22 control subjects. A linear model, previously shown to describe the general characteristics of the structure–function data, was extended to predict the variability in the data. For normal control subjects, between-individual error (individual differences) accounted for 87% and 71% of the total variance in OCT and SAP measures, respectively. SAP within-individual error increased and then decreased with increased SAP loss, whereas OCT error remained constant. The linear model with variability (LMV) described much of the variability in the data. However, 12.5% of the patients’ points fell outside the 95% boundary. An examination of these points revealed factors that can contribute to the overall variability in the data. These factors include epiretinal membranes, edema, individual variation in field-to-disc mapping, and the location of blood vessels and degree to which they are included by the RNFL algorithm. The model and the partitioning of within- versus between-individual variability helped elucidate the factors contributing to the considerable variability in the structure-versus-function data.
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影响因子：
4.4
作者：
Leung, Christopher Kai-shun;Cheung, Carol Yim-lui;Weinreb, Robert N.
通讯作者：
Weinreb, Robert N.
影响因子：
13.7
作者：
Budenz, Donald L.;Anderson, Douglas R.;Tielsch, James
通讯作者：
Tielsch, James
影响因子：
3.8
作者：
Hood DC;Raza AS;Kay KY;Sandler SF;Xin D;Ritch R;Liebmann JM
通讯作者：
Liebmann JM
影响因子：
13.7
作者：
Budenz, Donald L.;Fredette, Marie-Josee;Anderson, Douglas R.
通讯作者：
Anderson, Douglas R.
影响因子：
17.8
作者：
Hood, Donald C.;Kardon, Randy H.
通讯作者：
Kardon, Randy H.