Data-driven Development of Clinically Translatable EHR-Based Models to Estimate Severe Mood Episode Risk for Young People with Bipolar Disorder

About This Grant

Project Summary Bipolar disorder (BD) is among the deadliest and most costly psychiatric disorders in young people due to its severe and recurrent mood episodes of depression and mania which disrupt functioning, substantially increase the risk for suicide and premature death, and frequently require emergency or inpatient care. As each subsequent mood episode worsens prognosis, prevention of severe mood events in young people with BD is central to mitigating its enormous personal and societal burden. However, prevention is hindered by the lack of widely deployable tools to identify which affected individuals are at risk of a severe mood crisis event within a specific interval and which can guide individualized care. Through this mentored K23 award, the candidate, a PhD-prepared psychiatric nurse practitioner, will build upon her background in early intervention for BD, data- driven analytic approaches, and qualitative methods. Her program of training and research are designed to leverage real-world data and advanced analytic machine learning methods to efficiently identify young individuals with BD at risk for severe mood events and develop a deployment-focused clinical decision support intervention in partnership with clinicians and patients that could be rapidly translated to clinical care (NIMH Strategic Objectives 4.1 and 4.2). Through planned training activities, the candidate will gain a strong skillset in advanced predictive analytics and machine learning using electronic health record (EHR) and administrative data, mixed methods for stakeholder engaged intervention development, embedded health systems research, and BD clinical epidemiology. She will leverage robust, longitudinal health system data from two learning healthcare systems in the Mental Health Research Network, HealthPartners and Kaiser Permanente Northern California, and engagement with clinicians and patients where care is delivered. In Aim 1, rigorous machine learning methods will be used to estimate risk of severe mood crisis events, as indicated by mood-related inpatient hospitalization or emergency visits, over six-month intervals based on rich longitudinal EHR and claims data in a large sample of over 13,200 young patients with BD aged 15-39 years. In Aim 2, to maximize the translational impact of the models, clinicians and patients will be engaged, using a modified Delphi approach and qualitative interviews, in development and evaluation of a clinical decision support tool to guide personalized prevention and early intervention for BD mood crises. This research is a critical step in the candidate's long-term goal of leveraging data-driven approaches to improve individualized, patient-centered delivery of mental health services for individuals in the early course of BD and other serious mental illnesses. Her clinical and research background, expert mentoring team, and embedded research environment ideally positions her to accomplish the research and training aims, building the foundation for a next-step R01 that will externally validate and rigorously evaluate the risk prediction models and decision support tool developed in this proposal.