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.

抽象的自杀是导致死亡的主要原因，而且还在持续增加，每年有超过 47,000 例可预防的自杀死亡。尽管我们在使用电子健康记录 (EHR) 和其他预测自杀意念和行为的因素，我们可靠预测自杀死亡的能力接近于零。从医疗保健的角度来看，预测自杀死亡是很棘手的。我们知道自杀的发生率。与自杀死亡（每年约 0.01%-0.02%）相比，自杀行为更为常见（每年约 4%-5%）。从本质上讲，只有一小部分有自杀行为的人会继续自杀。了解这些最高风险的人对于指导预防工作和预防工作至关重要。此外，超过一半的自杀死亡是在没有事先发生的情况下发生的。尝试，甚至考虑到诊断代码中缺乏尝试记录，这些“突然”。病例表明一个或多个高危人群更难以准确预测和预防。纳入自杀死亡的遗传数据可能会显着改善遗传因素的预测；使用广泛的基因数据，全州范围的 EHR 可以预测近 50% 的自杀死亡风险。犹他州自杀遗传风险研究 (USGRS) 可用的资源、人口和家庭数据，我们我们的主要重点是使用机器学习来解决这一关键的知识差距。此外，我们的大型自杀死亡研究资源将开发预测自杀死亡的模型。还允许我们对犹他州约 9,000 例自杀者中自杀死亡的差异进行建模。通过人口统计和环境数据、家庭数据以及 20 年的纵向 EHR 数据来了解死亡情况 USGRS 目前还拥有超过 6,000 个 DNA，在基因组资助期间将增加到约 10,000 个。犹他州自杀事件中有超过 5,000 起，我们掌握了广泛的分子数据，可以对自杀之间的关联进行测试使用 EHR 数据识别出的亚型，以及以多基因风险评分表示的“遗传表型”。 USGRS 还拥有来自 5 个年龄/性别匹配的犹他州的人口统计数据、家庭数据和纵向 EHR 数据对每次自杀死亡进行人口控制，以便对非致命企图与自杀死亡进行比较。此外，我们将与西奈山医学院的同事合作，他们目前正在开发 EHR 和多基因风险模型来研究药物使用障碍、焦虑和重度抑郁症他们将扩大这项工作以包括西奈山 BioMe 生物储存库的 37,510 名参与者。我们将为我们的模型开发和测试提供自杀企图的额外资源。另外研究与自杀死亡与尝试使用我们的资源相关的多基因风险评分，Mount 西奈 BioMe，并与范德比尔特大学合作，访问其生物库并了解自杀企图在英国生物银行。通过对收集的新犹他州自杀者进行基因分型，可以进行独立验证在整个项目中，与通过以下方式获得的大型数据集中的尝试案例进行了额外的比较 PsychEMERGE 联盟。