Abstract:
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Primary open angle glaucoma (POAG) is a chronic progressive and potentially blinding optic neuropathy. Glaucoma damage is irreversible because nothing yet can restore the optic nerve cells once they are dead. However, the risk of blindness due to progressive visual field (VF) loss varies substantially from patient to patient. Early identification of those patients destined to rapid progressive visual loss is crucial to prevent further irreversible visual field loss. In Ocular Hypertension Treatment Study, we developed a model to dynamically predict the binary outcome of VF progression using longitudinal visual field mean deviation (MD) and pattern standard deviation (PSD) from the first eye of 277 participants who developed POAG. Specifically, a growth mixture model was fitted to identify unique subgroups (latent classes) based on the longitudinally changing patterns of visual field MD and PSD over time, and the probability of VF progression for an individual given repeated measurements up to time t can be estimated as the weighted average across latent classes, weighted by posterior probability of class membership. Performance of method was also assessed using simulation studies.
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