Monitoring of Glaucoma Patients in Advanced Disease

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Project Summary Individuals with advanced glaucomatous damage have markedly impaired visual function resulting in a decreased quality of life. This proposal will provide the longitudinal follow-up to fill in important gaps in our knowledge about monitoring eyes with advanced open angle glaucoma (OAG). Monitoring of the disease in its advanced stages is challenging because the visual field island shrinks to such an extent that only the central visual field survives and measurement of the retinal nerve fiber layer thickness reaches a floor, after which more thinning is not detectable. The overall objectives of this application are (i) to characterize the macular structural (microvasculature and thickness) and functional changes in eyes with advanced OAG and (ii) to develop novel models that can detect and predict progression in these eyes. The central hypothesis is that novel statistical and artificial intelligence-based analyses of central visual field functional status and recently developed macular optical imaging measurements will improve monitoring of disease in advanced OAG eyes. There is a critical need for models that can predict glaucomatous progression in advanced OAG eyes and to characterize longitudinal loss of macular structure and function in order to advise clinical decision making. The central hypothesis will be tested by 3 Specific Aims. Aims 1 and 2 will develop and validate models for detection of OAG progression using the central 10 degree visual field and characterize patterns of the longitudinal changes in the central visual field and retina. Cluster-based progression methods will be applied in vulnerable and less vulnerable zones of the 10-2 visual field. Nested multivariable linear mixed effects models will be used to compare rates of macula structure (ganglion cell layer and vessel density) and functional change (in eyes with Mean Deviation <-8 dB) and to characterize the relationships between baseline patterns of visual field and structural loss and glaucoma progression while adjusting for inter-eye correlation. In Aim 3, we will apply novel deep learning techniques to macular function and recently developed optical imaging measurements to improve the prediction accuracy of glaucomatous progression in advanced disease. Complex functional and structural tests in daily use by eye care providers contain hidden information that is not fully used in the current analyses and advanced pattern recognition/machine learning-based analysis techniques can find and use that hidden information. We will use mathematically rigorous unsupervised techniques such as archetypal analysis and multimodal deep learning to discover patterns of defects and assess the risk of changes in longitudinal series of perimetric and optical imaging data from >500 patients, available in our NIH-supported glaucoma database. The proposed work is significant because it will lead to development of more effective mathematically-based, validated methods of detecting OAG progression in eyes with advanced disease. Moreover, it will reduce the cost of glaucoma care by identifying high-risk patients that require more aggressive treatment, thus decreasing disability and reducing the burden of glaucoma blindness.