A new approach to measure visual field progression in glaucoma patients using variational bayes linear regression.
PURPOSE
We generated a variational Bayes model to predict visual field (VF) progression in glaucoma patients.


METHODS
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 second to 10th VFs (VF2-10), the root mean squared error (RMSE) associated with each approach then was calculated. Similarly, mean TD (mTD) of test patients' 11th VFs was predicted using VBLR and OLSLR, and the absolute prediction errors compared.


RESULTS
The RMSE resulting from VBLR averaged 3.9 ± 2.1 (SD) and 4.9 ± 2.6 dB for prediction based on the second to 10th VFs (VF2-10) and the second to fourth VFs (VF2-4), respectively. The RMSE resulting from OLSLR was 4.1 ± 2.0 (VF2-10) and 19.9 ± 12.0 (VF2-4) dB. The absolute prediction error (SD) for mTD using VBLR was 1.2 ± 1.3 (VF2-10) and 1.9 ± 2.0 (VF2-4) dB, while the prediction error resulting from OLSLR was 1.2 ± 1.3 (VF2-10) and 6.2 ± 6.6 (VF2-4) dB.


CONCLUSIONS
The VBLR more accurately predicts future VF progression in glaucoma patients compared to conventional OLSLR, especially in short VF series.
['Murata|Hiroshi|H|', 'Araie|Makoto|M|', 'Asaoka|Ryo|R|']
D001499:Bayes Theorem D018450:Disease Progression D005260:Female D005500:Follow-Up Studies D005901:Glaucoma D006801:Humans D007429:Intraocular Pressure D008297:Male D008875:Middle Aged D012189:Retrospective Studies D012607:Scotoma D058609:Visual Field Tests D014794:Visual Fields
visual field progression glaucoma variational Bayes E-M algorithm