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Prediction of suicide death using EHR and polygenic risk scores

使用 EHR 和多基因风险评分预测自杀死亡

基本信息

批准号：
10451573
负责人：
Hilary Coon
金额：
$ 68.9万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10451573
关键词：
Accounting Address Anxiety Area Award Cause of Death Classification Code Cohort Studies Collaborations Collection DNA Data Data Element Development Diagnostic Discrimination Documentation Electronic Health Record Elements Feeling suicidal Future Genetic Genetic Risk Genotype Grouping Hand Healthcare Healthcare Systems Incidence Individual Intervention Knowledge Major Depressive Disorder Measures Medical Examiners Mental disorders Modeling Molecular Natural Language Processing Participant Pharmaceutical Preparations Phenotype Physicians Population Population Control Prevention Resources Risk Sampling Substance Use Disorder Suicide Suicide attempt Testing Trauma Universities Utah Validation Work biobank cohort comparison group data resource demographics deprivation early life stress environmental stressor genome-wide high risk high risk population indexing large datasets machine learning method machine learning prediction medical schools model development polygenic risk score population based predictive modeling sample collection sex socioeconomics suicidal behavior suicidal morbidity suicidal risk

项目摘要

ABSTRACT 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.

摘要