Dataset Information

Ocular and systemic risk factors associated with recurrent disc hemorrhage in primary open-angle glaucoma.

ABSTRACT:

Purpose

To evaluate the risk factors associated with recurrent disc hemorrhage (DH), defined for the present study as at least 3 occurrences of DH in primary open-angle glaucoma (POAG).

Methods

A total of 178 POAG patients (89 eyes showing at least 3 occurrences of DH and 89 age-matched control eyes with a minimum of 10 years' follow-up without DH) were included in a retrospective, case-control study. Ocular factors were evaluated by a retrospective chart review, and systemic factors were evaluated by a telephone survey. Associations between factors and recurrent DH were investigated by logistic regression analysis. The Kaplan-Meier survival analysis and Cox proportional-hazards regression models were used to evaluate glaucoma progression and to identify the factors predictive of glaucoma progression.

Results

Univariate regression analysis revealed the association of recurrent DH with low baseline intraocular pressure (IOP) [odds ratio (OR), 0.88; 95% confidential interval (CI), 0.80-0.98; P = 0.014], lower percentage reduction of IOP (OR, 0.96; 95% CI, 0.93-0.99; P = 0.020), cold extremities (OR, 2.80; 95% CI, 1.03-7.60; P = 0.043), prone or lateral decubitus sleeping position (OR, 2.14; 95% CI, 1.13-4.03; P = 0.019), and sleeping disorders (OR, 2.33; 95% CI, 1.05-5.15; P = 0.037). Multivariate regression analysis revealed that a lower percentage reduction in IOP (OR, 0.96; 95% CI, 0.93-1.00; P = 0.046) increased the risk of recurrent DH. The control group exhibited a greater cumulative probability of non-progression than the recurrent DH group (P = 0.01, by log-rank test). The Cox proportional-hazards regression model showed that recurrent DH was associated with glaucoma progression [hazard ratio (HR), 1.88; 95% CI; 1.66-3.05; P = 0.01.

Conclusions

Among the ocular and systemic factors, only lower-percentage reduction of IOP in POAG was associated with recurrent DH. DH recurrence is associated with glaucoma progression and may be dependent on IOP.

SUBMITTER: Seol BR

PROVIDER: S-EPMC6746379 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Ocular and systemic risk factors associated with recurrent disc hemorrhage in primary open-angle glaucoma.

Seol Bo Ram BR Jeoung Jin Wook JW Park Ki Ho KH

PloS one 20190916 9

<h4>Purpose</h4>To evaluate the risk factors associated with recurrent disc hemorrhage (DH), defined for the present study as at least 3 occurrences of DH in primary open-angle glaucoma (POAG).<h4>Methods</h4>A total of 178 POAG patients (89 eyes showing at least 3 occurrences of DH and 89 age-matched control eyes with a minimum of 10 years' follow-up without DH) were included in a retrospective, case-control study. Ocular factors were evaluated by a retrospective chart review, and systemic fact ...[more]

PMID: 31525246

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Project description:PurposePrimary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness in the United States and worldwide. Although deep learning methods have been proposed to diagnose POAG, these methods all used a single image as input. Contrastingly, glaucoma specialists typically compare the follow-up image with the baseline image to diagnose incident glaucoma. To simulate this process, we proposed a Siamese neural network, POAGNet, to detect POAG from optic disc photographs.DesignThe POAGNet, an algorithm for glaucoma diagnosis, is developed using optic disc photographs.ParticipantsThe POAGNet was trained and evaluated on 2 data sets: (1) 37 339 optic disc photographs from 1636 Ocular Hypertension Treatment Study (OHTS) participants and (2) 3684 optic disc photographs from the Sequential fundus Images for Glaucoma (SIG) data set. Gold standard labels were obtained using reading center grades.MethodsWe proposed a Siamese network model, POAGNet, to simulate the clinical process of identifying POAG from optic disc photographs. The POAGNet consists of 2 side outputs for deep supervision and uses convolution to measure the similarity between 2 networks.Main outcome measuresThe main outcome measures are the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity.ResultsIn POAG diagnosis, extensive experiments show that POAGNet performed better than the best state-of-the-art model on the OHTS test set (area under the curve [AUC] 0.9587 versus 0.8750). It also outperformed the baseline models on the SIG test set (AUC 0.7518 versus 0.6434). To assess the transferability of POAGNet, we also validated the impact of cross-data set variability on our model. The model trained on OHTS achieved an AUC of 0.7490 on SIG, comparable to the previous model trained on the same data set. When using the combination of SIG and OHTS for training, our model achieved superior AUC to the single-data model (AUC 0.8165 versus 0.7518). These demonstrate the relative generalizability of POAGNet.ConclusionsBy simulating the clinical grading process, POAGNet demonstrated high accuracy in POAG diagnosis. These results highlight the potential of deep learning to assist and enhance clinical POAG diagnosis. The POAGNet is publicly available on https://github.com/bionlplab/poagnet.

Dataset Information

Ocular and systemic risk factors associated with recurrent disc hemorrhage in primary open-angle glaucoma.

Purpose

Methods

Results

Conclusions

Publications

Ocular and systemic risk factors associated with recurrent disc hemorrhage in primary open-angle glaucoma.

Similar Datasets

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