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Yukako Taketani, Hiroshi Murata, Yuri Fujino, Chihiro Mayama, Ryo Asaoka; How Many Visual Fields Are Required to Precisely Predict Future Test Results in Glaucoma Patients When Using Different Trend Analyses?. Invest. Ophthalmol. Vis. Sci. 2015;56(6):4076-4082. doi: https://doi.org/10.1167/iovs.14-16341.
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© ARVO (1962-2015); The Authors (2016-present)
To evaluate the minimum number of visual field (VF) tests required to precisely predict future VF results using ordinary least squares linear regression (OLSLR), quadratic regression, exponential regression, logistic regression, and M-estimator robust regression model.
Series of 15 VFs (Humphrey Field Analyzer 24-2 SITA standard) were analyzed from 247 eyes of 155 open-angle glaucoma patients. Future point-wise (PW) VF results and mean VF sensitivities were predicted with varying numbers of VFs in each regression method.
In PW-OLSLR, as expected, the minimum absolute prediction error was obtained using the maximum number of VFs in the regression (14 VFs); mean absolute prediction error was equal to 2.4 ± 0.9 dB. Ten VFs were required to reach the 95% confidence interval (CI) of the minimum absolute prediction error. Prediction errors associated with the exponential and quadratic regression models were significantly larger than those from PW-OLSLR, whereas errors from logistic regression were not significantly smaller than those from PW-OLSLR; however, the absolute prediction error from the M-estimator robust regression model was significantly smaller than those associated with PW-OLSLR (P < 0.01, paired Wilcoxon test). Like PW-OLSLR, 10 VFs were needed to obtain the minimum absolute prediction error of mean VF sensitivity, but there were no significant differences in errors using the different regression methods.
Approximately 10 VFs, are needed to achieve an accurate prediction of PW VF sensitivity and mean sensitivity. Prediction error of PW VF sensitivity can be significantly minimized using the M-estimator robust regression model compared with conventional OLSLR.
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