Reducing Variability in Visual Field Assessment for Glaucoma Through Filtering That Combines Structural and Functional Information

Abstract

PURPOSE. To reduce variability and improve measurements of true change signal in visual field (VF) assessments through the use of filters that combine functional and structural test results.METHODS. Humphrey VF data (Swedish Interactive Thresholding Algorithm [SITA] Standard, 24-2) and confocal scanning laser ophthalmoscopy (Heidelberg Retina Tomograph [HRT]) data from 1057 eyes of 637 participants were used to derive a filter. Another dataset, consisting of VF and HRT data from 112 eyes of 62 participants each with ‡5 visits, was used to test the filter. At each VF location per eye, the trend over time was modeled by a linear model (LM), and a nonlinear model (NLM), using filtered or unfiltered data, but with the last visit excluded. The SD of residuals from the trends, and prediction errors (PE) for the last visit were compared between filtered and unfiltered data. The filter was reconstructed and analyses were repeated after truncating VF data so that thresholds < 19 dB were replaced by 19 dB to reduce noise.RESULTS. The SD of the residuals at all 52 VF locations for all analyses was reduced by filtering (P < 0.001). The PE was reduced by filtering at 43 and 47 VF locations (P < 0.05) for LM analyses on observed and truncated data, and all 52 VF locations (P < 0.05) for both NLM analyses. Truncating data before filtering reduced variability (P < 0.01) at 41 and 40 VF locations for LM and NLM analyses.CONCLUSIONS. Filtering can reduce variability about trends in longitudinal sequences of VF data, and improves the accuracy of predicting the next test result.

Reducing Variability of Perimetric Global Indices from Eyes with Progressive Glaucoma by Censoring Unreliable Sensitivity Data

“…For example, variability in SAP data can be reduced through improved data acquisition methods. 7 Alternatively, post-processing techniques, such as spatial filtering of currently available data 8 – 11 appear to reduce variability or choosing a more valid statistical model, 12 explains variability in the SAP data to some degree. Furthermore, a recent study 13 recommended using Goldmann size V stimuli instead of size III to produce more reliable VF data later into the glaucomatous disease process.…”

Factors Associated with Midterm Visual Field Variability in Patients with Stable Glaucoma

“…Variables associated with VF loss and VF variability may help identify patients who need greater clinical scrutiny. Variability obscures the identification of true glaucomatous progression in VF and makes detection of progression challenging [37]. Therefore, reducing variability is particularly important [30] as the change in a glaucomatous VF must be greater than the noise about the measurement before it becomes statistically distinguishable [25, 28].…”