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

• 批准号: 10239191
• 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
• 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/10239191
• 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; Machine Learning; 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; medical schools; model development; polygenic risk score; population based; predictive modeling; sample collection; sex; socioeconomics; suicidal behavior; suicidal morbidity; suicidal risk

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 自杀是死亡的主要原因，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 因素来预测自杀意念和行为，我们可靠地预测自杀死亡的能力接近于零。 从医疗保健的角度来看，预测自杀死亡是棘手的。我们知道自杀率 自杀行为（每年约4%-5%）比自杀死亡（每年约0.01%-0.02%）要常见得多。 从本质上讲，只有一小部分有自杀行为的人会继续自杀。 了解哪些人是这些风险最高的人，对于指导预防工作至关重要， 制定未来有针对性的干预措施。此外，超过一半的自杀死亡发生在没有事先 尝试，甚至考虑到诊断代码中缺乏尝试记录。这些“突如其来”的 病例提示一个或多个高危人群更难以准确预测和预防。 包括自杀死亡的遗传数据可能提供实质性的预测改善;遗传因素解释 自杀死亡风险的近50%。使用广泛的遗传数据，全州纵向EHR 资源，人口统计学和家庭数据可用于犹他州自杀遗传风险研究（USGRS），我们是 以解决这一关键的知识差距。我们的主要重点将是使用机器学习 方法开发预测自杀死亡的模型。此外，我们庞大的自杀死亡研究资源将 这也使我们能够模拟有与没有先前尝试的自杀死亡的差异。犹他州约9,000人自杀 死亡与人口统计学和环境数据，家族数据和20年的纵向EHR数据， USGRS目前也有DNA从> 6，000，这将增加到约10，000在奖励期间。基因组- 犹他州5,000多名自杀者的广泛的分子数据已经掌握，可以进行自杀相关性的测试 使用EHR数据识别亚型，其中“遗传表型”由多基因风险评分表示。的 USGRS还拥有来自5个年龄/性别匹配的犹他州的人口统计学、家族数据和纵向EHR数据 对每例自杀死亡进行人口控制，以便将非致命企图与自杀死亡进行比较。在 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，以研究物质使用障碍、焦虑和重性抑郁 在西奈山BioMe生物储存库的37，510名参与者中进行了研究。他们将扩大这项工作， 自杀倾向，为我们的模型开发和测试提供额外的自杀企图资源。我们将 蒙特还利用我们的资源研究与自杀死亡与自杀企图相关的多基因风险评分 Sinai BioMe，以及与范德比尔特大学的合作，以访问他们的生物银行和自杀企图 在英国生物银行。通过对收集到的新的犹他州自杀者进行基因分型，独立验证将成为可能 在整个项目中，通过对大型数据集中的尝试案例进行额外的比较， PsychEMERGE财团。