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Invest Ophthalmol Vis SciSeptember 200990 citations

A test of a linear model of glaucomatous structure-function loss reveals sources of variability in retinal nerve fiber and visual field measurements.

Hood Donald C, Anderson Susan C, Wall Michael, Raza Ali S, Kardon Randy H


AI Summary

This study analyzed glaucoma patients' RNFL and visual field data, finding significant variability from individual differences and factors like epiretinal membranes. This improves understanding of structure-function relationships in glaucoma.

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.


MeSH Terms

AdultAgedAged, 80 and overAlgorithmsGlaucomaHumansIntraocular PressureLinear ModelsMiddle AgedNerve FibersOphthalmoscopyOptic Nerve DiseasesRetinal Ganglion CellsTomography, Optical CoherenceVisual Field TestsVisual FieldsYoung Adult

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