Suicide is a leading cause of death that continues to increase, with over 47,000 preventable suicide deaths per year in the U.S. Although we have made great strides in using electronic health records (EHR) and other factors to predict suicidal ideation and behavior, our ability to reliably predict suicide death is close to zero. From a healthcare standpoint, predicting suicide deaths is tricky. We know that the incidence of suicide behaviors is far more common (~4%-5% per year) compared to suicide death (~0.01%-0.02% per year). Essentially, only a small fraction of those who engage in suicidal behaviors will go on to die by suicide. Knowledge of who these highest risk individuals are is critically important in directing prevention efforts and development of future targeted interventions. In addition, well over half of suicide deaths occur with no prior attempts, even accounting for lack of documentation of attempts in diagnostic codes. These ?out of the blue? cases suggest one or more high-risk groups even more elusive to accurate prediction and prevention. Including genetic data of suicide deaths may offer substantial predictive improvement; genetic factors account for close to 50% of the risk of suicide death. Using the extensive genetic data, statewide longitudinal EHR resources, demographic, and familial data available to the Utah Suicide Genetic Risk Study (USGRS), we are uniquely poised to address this critical knowledge gap. Our primary focus will be to use machine learning methods develop models that predict suicide deaths. In addition, our large suicide death research resource will also allow us to model differences of suicide deaths with vs. without prior attempts. Of the ~9,000 Utah suicide deaths with demographics and environmental data, familial data, and 2 decades of longitudinal EHR data, the USGRS also currently has DNA from >6,000, which will increase to ~10,000 during the award period. Genome- wide molecular data is in hand for over 5,000 of these Utah suicides, allowing for tests of association of suicide subtypes identified using EHR data with ?genetic phenotypes? represented by polygenic risk scores. The USGRS also has demographics, familial data, and longitudinal EHR data from 5 age/sex- matched Utah population controls for each suicide death, allowing for comparisons of non-lethal attempts to suicide deaths. In addition, we will collaborate with colleagues at the Mount Sinai School of Medicine, who are currently developing EHR and polygenic risk models to study substance use disorder, anxiety, and major depressive disorder in 37,510 participants in the Mount Sinai BioMe Biorepository. They will expand this work to include suicidality to provide an additional resource of suicide attempt for our model development and testing. We will additionally study polygenic risk scores associated with suicide death vs. attempt using our resources, Mount Sinai BioMe, and a collaboration with Vanderbilt University for access to their Biobank and to suicide attempts in the UK Biobank.. Independent validation will be possible through genotyping of new Utah suicides collected throughout the project, with additional comparisons to attempt cases in large datasets available through the PsychEMERGE consortium.
We will develop and validate models to predict suicide death, as distinct from suicide attempt, using electronic health records (EHR), demographics, environmental stresses, familial risks, and genetic differences. We will use 9,000 Utah suicides with longitudinal EHR data (>5,000 of these with molecular data growing to 10,000 by the end of the study), Utah population controls, collaborations with Mount Sinai Medical School and Vanderbilt University, and validation/replication with the large EHR and molecular data resources in the PsychEMERGE Consortium.
