Development of a program to assess and treat distress in glaucoma patients using an automated EHR-derived AI algorithm

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PROJECT SUMMARY/ABSTRACT Glaucoma is a disease that results in irreversible blindness and due to its chronic, progressive nature, imposes a psychosocial burden on patients. Appropriately, the focus of ophthalmologists is on controlling the disease to prevent vision loss. Yet, patient’s psychosocial distress during and after therapy has not been routinely addressed and is another important target of care. Psychosocial distress (i.e., anxiety, depression) negatively impacts all outcomes in glaucoma and is associated with poor follow-up and medication adherence, worse vision-related quality-of-life and disease severity, and faster rates of visual field progression. Direct assessment and treatment of psychosocial distress is likely to improve glaucoma outcomes. While uncommon in glaucoma clinics, psychosocial distress screening has been occurring with some consistency in other medical settings (e.g., oncology) for more than a decade, leading to referrals for intervention and improvements in psychosocial distress and subsequently overall health. Our overarching scientific premise is that a screening program for psychosocial distress (i.e., anxiety, depression) in glaucoma clinics would enhance the patient’s adherence to medical recommendations, and quality-of-life, ultimately leading to improvements in vision-related outcomes (e.g., visual field progression). Patient-reported outcome measures are the gold standard measures of distress, however are not routinely collected in patients with glaucoma due to perceived time and cost burdens. To remedy this, the PI proposes an automated pre-screening framework, motivated by preliminary analyses that demonstrate that distress can be reliably identified using predictive modeling based on glaucoma clinical risk factors from electronic health records (EHR) data. This predictive model will be developed in aim 1 using an existing EHR database, the Duke Glaucoma Registry, and will yield automated risk estimates of distress that can be used to inform clinical decision making, regarding the administration of a distress survey; therefore, limiting distress assessment to a subset of high-risk patients. Secondary aims will focus on external validation of the automated technique, and gauging acceptability to distress screening in a glaucoma clinic (aim 2), and the refinement of a behavioral intervention to improve coping skills for distress in patients with glaucoma (aim 3). This research will positively impact patient well- being in glaucoma, serving as an evidence-based assessment of a distress screening program. The proposal also details a training plan to help the PI transition from a postdoctoral scholar to an independent researcher. The mentored phase of the award will be supervised by the primary mentor, Dr. Felipe Medeiros, and multidisciplinary mentoring team including Dr. Tamara Somers (Psychiatry and Behavioral Sciences), Dr. David Page (Biostatistics & Bioinformatics), and Dr. Kevin Weinfurt (Population Health Sciences). Performing the proposed research, formal coursework, and mentored career development will provide the PI with highly sought-after skills and experiences to help ensure a successful transition to independence.