Personalized prediction of response to treatment and outcomes in patients with non-infectious uveitis

About This Grant

Non-infectious uveitis (NIU) is a leading cause of visual impairment, responsible for 15% of blindness cases in the United States. It primarily affects young, working-age individuals, carrying a significant lifetime risk of visual disability and an economic burden comparable to that of diabetic retinopathy. Despite advancements in systemic immunomodulatory therapies (IMTs), the management of NIU remains suboptimal due to the heterogeneity of the disease and the lack of personalized treatment strategies. Approximately 40% of patients do not respond to first-line therapies, resulting in delays in controlling inflammation and an increased risk of complications. Additionally, access to uveitis care is limited, especially for rural and socioeconomically disadvantaged populations. These challenges highlight the need for predictive models that can guide personalized interventions and optimize patient outcomes. This K23 proposal aims to develop and validate machine learning (ML) models that predict treatment response, disease activity, and the risk of complications in patients with NIU. We will extract detailed ocular exam data from Electronic Health Records (EHRs, Aim 1) and build predictive tools that can be integrated into clinical workflows, providing clinicians with real-time, evidence-based decision- making support (Aim 2). Career Development Plan: Dr. Emami-Naeini’s career development plan includes advanced training in bioinformatics, ML, and clinical informatics, which will be essential to achieving independence as a clinician-scientist. She will complete coursework in ML and data science, attend health informatics workshops, and receive mentorship from a multidisciplinary team with expertise in uveitis, ML, and health informatics. The mentorship and training will allow her to acquire the necessary skills to develop and validate predictive models for uveitis care. Environment: UC Davis offers an exceptional environment for achieving candidate’s career goals, with access to the Clinical and Translational Science Center (CTSC), the Biomedical Informatics Program, and the Sight Outcomes Research Collaborative (SOURCE) consortium. The institutional support, infrastructure, and protected research time provided by the Department of Ophthalmology will ensure the successful execution of this project. The data generated from this K23 proposal, along with the training received during the award period, will provide the foundation for the PI’s independent career as a clinician-scientist. This work will also form the basis for future R01 applications, with the goal of integrating predictive models into the EHR systems for widespread use and personalized care for NIU patients.