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Alessandro Rabiolo, Esteban Morales, Abdelmonem A. Afifi, Fei Yu, Kouros Nouri-Mahdavi, Joseph Caprioli; Quantification of Visual Field Variability in Glaucoma: Implications for Visual Field Prediction and Modeling. Trans. Vis. Sci. Tech. 2019;8(5):25. doi: https://doi.org/10.1167/tvst.8.5.25.
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© ARVO (1962-2015); The Authors (2016-present)
To quantify visual field (VF) variability as a function of threshold sensitivity and location, and to compare weighted pointwise linear regression (PLR) with unweighted PLR and pointwise exponential regression (PER) for data fit and prediction ability.
Two datasets were used for this retrospective study. The first was used to characterize and estimate VF variability, and included a total of 4,747 eyes of 3,095 glaucoma patients with six or more VFs and 3 years or more of follow-up. After performing PER for each series, standard deviation of residuals was quantified for each decibel of sensitivity as a measure of variability. A separate dataset was used to test and compare unweighted PLR, weighted PLR, and PER for data fit and prediction, and included 261 eyes of 176 primary open-angle glaucoma patients with 10 or more VFs and 6 years or more of follow-up.
The degree of variability changed as a function of threshold sensitivity with a zenith and a nadir at 33 and 11 dB, respectively. Variability decreased with eccentricity and was higher in the central 10° (P < 0.001). Differences among the methods for data fit were negligible. PER was the best model to predict future sensitivity values in the mid term and long term.
VF variability increases with the severity of glaucoma damage and decreases with eccentricity. Weighted linear regression neither improves model fit nor prediction. PER exhibited the best prediction ability, which is likely related to the nonlinear nature of long-term glaucomatous perimetric decay.
This study suggests that taking into account heteroscedasticity has no advantage in VF modeling.
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