Hiroshi Murata 1 , Makoto Araie 2 , Ryo Asaoka 1 Show Affiliations »
Abstract
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. © ARVO.
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. © ARVO.
Entities: Disease
Gene
Species
Keywords:
E-M algorithm; glaucoma; progression; variational Bayes; visual field
Mesh： See more »
Year: 2014
PMID： 25414192 DOI： 10.1167/iovs.14-14625
Source DB: PubMed Journal: Invest Ophthalmol Vis Sci ISSN： 0146-0404 Impact factor: 4.799