Pathak Manoj, Demirel Shaban, Gardiner Stuart K
Department of Mathematics and Statistics, Murray State University, Murray, KY, USA.
Devers Eye Institute, Legacy Health, 1225 NE 2nd Ave, Portland, OR, USA.
Transl Vis Sci Technol. 2015 Feb 10;4(1):8. doi: 10.1167/tvst.4.1.8. eCollection 2015 Feb.
We have shown previously that a nonlinear exponential model fits longitudinal series of mean deviation (MD) better than a linear model. This study extends that work to investigate the mode (linear versus nonlinear) of change for pointwise sensitivities.
Data from 475 eyes of 244 clinically managed participants were analyzed. Sensitivity estimates at each test location were fitted using two-level linear and nonlinear mixed effects models. Sensitivity on the last test date was forecast using a model fit from the earlier test dates in the series. The means of the absolute prediction errors were compared to assess accuracy, and the root means square (RMS) of the prediction errors were compared to assess precision.
Overall, the exponential model provided a significantly better fit ( < 0.05) to the data at the majority of test locations (69%). The exponential model fitted the data significantly better at 85% of locations in the upper hemifield and 58% of locations in the lower hemifield. The rate of visual field (VF) deterioration in the upper hemifield was more rapid (mean, -0.21 dB/y; range, -0.28 to -0.13) than in the lower hemifield (mean, -0.14 dB/y; range, -0.2 to -0.09).
An exponential model may more accurately track pointwise VF change, at locations damaged by glaucoma. This was more noticeable in the upper hemifield where the VF changed more rapidly. However, linear and exponential models were similar in their ability to forecast future VF status.
The VF progression appears to accelerate in early glaucoma patients.
我们之前已经表明，非线性指数模型比线性模型更能拟合平均偏差（MD）的纵向系列数据。本研究扩展了该工作，以调查逐点敏感度的变化模式（线性与非线性）。
对244名临床管理参与者的475只眼睛的数据进行了分析。使用两级线性和非线性混合效应模型拟合每个测试位置的敏感度估计值。使用该系列早期测试日期拟合的模型预测最后测试日期的敏感度。比较绝对预测误差的均值以评估准确性，比较预测误差的均方根（RMS）以评估精确性。
总体而言，指数模型在大多数测试位置（69%）对数据的拟合明显更好（<0.05）。指数模型在85%的上半视野位置和58%的下半视野位置对数据的拟合明显更好。上半视野的视野（VF）恶化速度比下半视野更快（平均，-0.21 dB/年；范围，-0.28至-0.13）（平均，-0.14 dB/年；范围，-0.2至-0.09）。
指数模型可能更准确地跟踪青光眼受损部位的逐点VF变化。这在上半视野中更明显，其中VF变化更快。然而，线性和指数模型在预测未来VF状态的能力方面相似。
早期青光眼患者的VF进展似乎加速。
