TY - JOUR
T1 - Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning
AU - Shuldiner, Scott R.
AU - Boland, Michael V.
AU - Ramulu, Pradeep Y.
AU - Gustavo De Moraes, C.
AU - Elze, Tobias
AU - Myers, Jonathan
AU - Pasquale, Louis
AU - Wellik, Sarah
AU - Yohannan, Jithin
N1 - Publisher Copyright:
Copyright: © 2021 Shuldiner et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2021/4
Y1 - 2021/4
N2 - Objective To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test. Design Retrospective analysis of longitudinal data. Subjects 175,786 VFs (22,925 initial VFs) from 14,217 patients who completed 5 reliable VFs at academic glaucoma centers were included. Methods Summary measures and reliability metrics from the initial VF and age were used to train MLA designed to predict the likelihood of rapid progression. Additionally, the neural network model was trained with point-wise threshold data in addition to summary measures, reliability metrics and age. 80% of eyes were used for a training set and 20% were used as a test set. MLA test set performance was assessed using the area under the receiver operating curve (AUC). Performance of models trained on initial VF data alone was compared to performance of models trained on data from the first two VFs. Main outcome measures Accuracy in predicting future rapid progression defined as MD worsening more than 1 dB/ year. Results 1,968 eyes (8.6%) underwent rapid progression. The support vector machine model (AUC 0.72 [95% CI 0.70–0.75]) most accurately predicted rapid progression when trained on initial VF data. Artificial neural network, random forest, logistic regression and naïve Bayes classifiers produced AUC of 0.72, 0.70, 0.69, 0.68 respectively. Models trained on data from the first two VFs performed no better than top models trained on the initial VF alone. Based on the odds ratio (OR) from logistic regression and variable importance plots from the random forest model, older age (OR: 1.41 per 10 year increment [95% CI: 1.34 to 1.08]) and higher pattern standard deviation (OR: 1.31 per 5-dB increment [95% CI: 1.18 to 1.46]) were the variables in the initial VF most strongly associated with rapid progression. Conclusions MLA can be used to predict eyes at risk for rapid progression with modest accuracy based on an initial VF test. Incorporating additional clinical data to the current model may offer opportunities to predict patients most likely to rapidly progress with even greater accuracy.
AB - Objective To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test. Design Retrospective analysis of longitudinal data. Subjects 175,786 VFs (22,925 initial VFs) from 14,217 patients who completed 5 reliable VFs at academic glaucoma centers were included. Methods Summary measures and reliability metrics from the initial VF and age were used to train MLA designed to predict the likelihood of rapid progression. Additionally, the neural network model was trained with point-wise threshold data in addition to summary measures, reliability metrics and age. 80% of eyes were used for a training set and 20% were used as a test set. MLA test set performance was assessed using the area under the receiver operating curve (AUC). Performance of models trained on initial VF data alone was compared to performance of models trained on data from the first two VFs. Main outcome measures Accuracy in predicting future rapid progression defined as MD worsening more than 1 dB/ year. Results 1,968 eyes (8.6%) underwent rapid progression. The support vector machine model (AUC 0.72 [95% CI 0.70–0.75]) most accurately predicted rapid progression when trained on initial VF data. Artificial neural network, random forest, logistic regression and naïve Bayes classifiers produced AUC of 0.72, 0.70, 0.69, 0.68 respectively. Models trained on data from the first two VFs performed no better than top models trained on the initial VF alone. Based on the odds ratio (OR) from logistic regression and variable importance plots from the random forest model, older age (OR: 1.41 per 10 year increment [95% CI: 1.34 to 1.08]) and higher pattern standard deviation (OR: 1.31 per 5-dB increment [95% CI: 1.18 to 1.46]) were the variables in the initial VF most strongly associated with rapid progression. Conclusions MLA can be used to predict eyes at risk for rapid progression with modest accuracy based on an initial VF test. Incorporating additional clinical data to the current model may offer opportunities to predict patients most likely to rapidly progress with even greater accuracy.
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U2 - 10.1371/journal.pone.0249856
DO - 10.1371/journal.pone.0249856
M3 - Article
C2 - 33861775
AN - SCOPUS:85104239222
SN - 1932-6203
VL - 16
JO - PloS one
JF - PloS one
IS - 4 April
M1 - e0249856
ER -