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K. Sung, G. Wollstein, H. Ishikawa, R. A. Bilonick, K. A. Townsend, L. Kagemann, Jr., V. Jurisic, R. J. Noecker, M. L. Gabriele, J. S. Schuman; Glaucomatous Progression Detection by Perimetry Using Visual Field Index (VFI) and Global Parameters. Invest. Ophthalmol. Vis. Sci. 2008;49(13):1091. doi: https://doi.org/.
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Visual field index (VFI), a new parameter representing global sensitivity, is based on location weighted average of pattern deviation depression at 5% or worse compared to age matched normal values, with values ranging from 0% for no response to light to 100% for values at the level of the age matched normals. The purpose of this study was to assess the performance of VFI and global perimetry parameters on glaucomatous progression detection.
Sixty-two subjects with glaucoma (90 eyes) and glaucoma suspects (20 eyes) with at least 5 reliable visual fields (VF) were analyzed. Four global parameters were recorded: VFI, mean deviation (MD), pattern standard deviation (PSD) and advanced glaucoma intervention study (AGIS) score. A latent class regression (LCR) model was used to define glaucoma progressors and nonprogressors. Each parameter was transformed to have a mean of zero and a standard deviation of one to place all measures on the same scale. The slopes for each parameter and measurement error standard deviations (SD) were calculated for each latent group during the period of follow-up. The measurement error provides an estimate on the repeatability of the measurements over time. The prediction of progression by baseline measurement was assessed by the area under the receiver operating characteristics curve (AUC).
The LCR model categorized 36 eyes as latent progressors and 74 eyes as latent non-progressors. The table summarizes the slope and measurement error SD for each parameter in the progressors and the non-progressors groups.
VFI as well as all other parameters showed similar slope and measurement error over time. AGIS score had the overall best performance among the tested parameters though slightly lower prediction ability due to lower AUC compared to the other parameters.
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