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Abstract #59542 Published in IGR 16-3
A new approach to measure visual field progression in glaucoma patients using Variational Bayes linear regression
Murata H; Araie M; Asaoka RInvestigative Ophthalmology and Visual Science 2014; 0:
PURPOSE: To generate a Variational Bayes model to predict visual field (VF) progression in glaucoma patients. METHOD: This retrospective study included VF series from 911 eyes of 547 glaucoma patients as test data, and VF series from 5049 eyes of 2858 glaucoma patients as training data. Using training data, Variational Bayes linear regression (VBLR) was created to predict VF progression. The performance of VBLR was compared against ordinary least-squares linear regression (OLSLR) by predicting VFs in the test dataset. The total deviation (TD) values of test patients' 11th VFs were predicted using TD values from their 2nd to 10th VFs (VF2-10), the root mean squared error (RMSE) associated with each approach was then calculated. Similarly, mean TD (mTD) of test patients' 11th VFs was predicted using VBLR and OLSLR, and the absolute prediction errors compared. RESULTS: RMSE resulting from VBLR averaged 3.9 ± 2.1(standard deviation) dB and 5.3 ± 2.8 dB for prediction based on the 2nd to 10th VFs (VF2-10) and the 2nd to 4th VFs (VF2-4), respectively. The RMSE resulting from OLSLR was 4.1 ± 2.0 dB (VF2-10) and 19.9 ± 12.0 dB (VF2-4). The absolute prediction error (± standard deviation) for mTD using VBLR was 1.3 ± 1.4 dB (VF2-10) and 2.2 ± 2.2 dB (VF2-4), whilst the prediction error resulting from OLSLR was 1.2 ± 1.3 dB (VF2-10) and 6.2 ± 6.6 dB (VF2-4). CONCLUSION: VBLR more accurately predicts future VF progression in glaucoma patients compared with conventional OLSLR, especially in short VF series.
Ophthalmology, University of Tokyo, Tokyo, Japan.
Full articleClassification:
6.20 Progression (Part of: 6 Clinical examination methods)
6.6.2 Automated (Part of: 6 Clinical examination methods > 6.6 Visual field examination and other visual function tests)
