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Gabriel A. Villasana, Chris Bradley, Tobias Elze, Jonathan S. Myers, Louis Pasquale, C Gustavo De Moraes, Sarah Wellik, Michael V. Boland, Pradeep Ramulu, Greg Hager, Mathias Unberath, Jithin Yohannan; Improving Visual Field Forecasting by Correcting for the Effects of Poor Visual Field Reliability. Trans. Vis. Sci. Tech. 2022;11(5):27. https://doi.org/10.1167/tvst.11.5.27.
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The purpose of this study was to accurately forecast future reliable visual field (VF) mean deviation (MD) values by correcting for poor reliability.
Four linear regression techniques (standard, unfiltered, corrected, and weighted) were fit to VF data from 5939 eyes with a final reliable VF. For each eye, all VFs, except the final one, were used to fit the models. Then, the difference between the final VF MD value and each model's estimate for the final VF MD value was used to calculate model error. We aggregated the error for each model across all eyes to compare model performance. The results were further broken down into eye-level reliability subgroups to track performance as reliability levels fluctuate.
The standard method, used in the Humphrey Field Analyzer (HFA), was the worst performing model with an average residual that was 0.69 dB higher than the average from the unfiltered method, and 0.79 dB higher than that of the weighted and corrected methods. The weighted method was the best performing model, beating the standard model by as much as 1.75 dB in the 40% to 50% eye-level reliability subgroup. However, its average 95% prediction interval was relatively large at 7.67 dB.
Including all VFs in the trend estimation has more predictive power for future reliable VFs than excluding unreliable VFs. Correcting for VF reliability further improves model accuracy.
The VF correction methods described in this paper may allow clinicians to catch VF worsening at an earlier stage.
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