Leveraging CLEERE axial length data to improve myopia treatment

点击翻译按钮获取中文摘要

Project Summary/Abstract The dual objectives of myopia research are to prevent myopia onset and, barring that, to reduce myopia progression. The field currently has no way to communicate the effectiveness of the treatments being designed to slow myopia progression other than citing average effects from clinical trials. Clinicians want to be able to demonstrate to children and their parents the impact that a treatment is having on an individual child’s myopia. To do this, percentiles of the expected growth of the eye are necessary. Additionally, the goal of preventing or delaying onset requires being able to predict who will become myopic. Models currently use basic information from one time point to make a simple prediction of more likely than not to develop myopia. Clinicians need an improved model that can assign specific probabilities to individual children in order to properly identify those whose risk of future myopia justifies starting a preventive treatment. The CLEERE Study has the largest longitudinal cohort of ocular development across a variety of race/ethnic groups that will aid in answering these questions. CLEERE provided one of the first reports on the protective effect of time outdoors, but its effects on axial length have yet to be described. Using these data to accomplish the following aims will improve the care of these children. Specific Aim 1: To analyze axial length data and address critical clinical questions in myopia control, namely whom to treat and whether treatment is beneficial. Specific Aim 2: To determine the effect of time spent outdoors/sports activity on axial elongation before and after myopia onset. Specific Aim 3: To improve predictive models by determining whether the trajectory of change in axial length or other ocular components in non-myopic children is a better predictor of the risk of onset of myopia than spherical equivalent refractive error at a single point in time. This NEI Research Grant for Vision-related Secondary Data Analysis (PAR 22-141) will incorporate statistical techniques not routinely employed in vision research to leverage this large existing dataset to more fully explore how to give clinicians the tools they need to effectively treat and manage children’s refractive error.