Distinguishing Clinical and Genetic Risk of Suicidal Ideation from Attempts to Inform Prevention

区分自杀意念的临床和遗传风险与告知预防的尝试

• 批准号: 10292968
• 负责人: Douglas Ruderfer
• 金额: $ 54.37万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10292968
• 关键词: Affect; Algorithms; American; Behavior; Cause of Death; Cessation of life; Clinical; Clinical Informatics; Code; Country; Data; Diagnostic; Electronic Health Record; Family Study; Feeling suicidal; Genetic; Genetic Risk; Genetic Variation; Genetic study; Genomics; Genotype; Goals; Healthcare Systems; Heritability; Hospitals; Individual; Investigation; Life Expectancy; Literature; Machine Learning; Medical Genetics; Mental Depression; Mental disorders; Modeling; Natural Language Processing; Neurotic Disorders; Patients; Performance; Persons; Phenotype; Population; Predictive Factor; Prevalence; Prevention; Preventive; Provider; Psyche structure; Public Health; Publishing; Recording of previous events; Reporting; Research; Research Personnel; Resources; Risk; Risk Factors; Role; Running; Sampling; Schizophrenia; Signal Transduction; Structure; Suicide; Suicide attempt; Suicide prevention; Symptoms; Testing; Thinking; Time; United Kingdom; United States; Work; algorithmic methodologies; base; biobank; clinical phenotype; clinical predictors; clinical risk; depressive symptoms; design; epidemiology study; genetic analysis; genetic architecture; genetic association; genetic risk factor; high risk; ideation; improved; member; open source; outcome prediction; phenotyping algorithm; portability; predictive modeling; prevent; psychiatric comorbidity; risk stratification; structured data; suicidal; suicidal behavior; suicidal risk; suicide rate; trait

One hundred and twenty-three Americans die by suicide every day, and 800,000 individuals die from suicide globally every year. Five times as many people attempt suicide and 10-25 times as many contemplate suicide every year. Rates of suicidal thoughts or ideation and suicidal behaviors are increasing. Suicidal ideation alone causes mental and physical harm and is associated with worsened statuses of other illnesses. Suicidal ideation is often documented by clinical providers in their notes but has been shown to only be included in diagnostic or billing codes 3% of the time. Historical suicide attempts are also under- captured by billing codes alone. Improving identification of those with suicidal ideation and attempts might enhance prevention through earlier contact with those at risk. A growing literature shows that clinical predictive models with longitudinal electronic health records (EHR) can predict suicide attempts with good performance. These models have also been used by groups like ours to improve power of large-scale genetic analyses of suicide attempt risk. The investigators used their validated models to identify the signal for suicide attempt, a “phenotype”, to run genetic analyses showing suicide attempt risk is 4% heritable. This team also showed that suicide attempt risk was significantly genetically correlated with depressive symptoms, neurotic symptoms, and schizophrenia. The investigators propose to validate a phenotype of suicidal ideation and to improve their existing phenotypes of attempt risk to power large-scale genetic analyses across two major biobanks, Vanderbilt’s BioVU and the UK Biobank. They will use natural language processing (NLP) and analytics on Vanderbilt’s EHR to develop and test a phenotype of suicidal ideation. They will use NLP to improve capture of cases of suicide attempt to refine existing algorithms. They will apply these phenotypes at scale to BioVU. Their Stanford team members will use patient-reported suicidal ideation histories in another major biobank, UK Biobank, to independently run genetic analyses of suicidal ideation risk in a different population. They will further analyze clinical and genetic risk factors to better understand who will transition from suicidal ideation to suicide attempt. The project combines expertise in clinical informatics, machine learning, and large-scale genomics, as well as domain-specific expertise in suicide risk research. Spanning two major biobanks across two countries, the algorithms and methods developed have maximal portability, facilitating next-step investigations. Successful identification of suicidal ideation and attempt risk might inform clinical prevention. Better understanding of risk factors that predict who will proceed from suicidal ideation to suicidal behaviors would help allocate prevention resources to those who need them most.

每天有123名美国人死于自杀，80万人死于自杀 全球每年都有自杀事件。5倍于此的人企图自杀，10-25倍于 每年都会考虑自杀。自杀念头、念头和自杀行为的比率正在上升。 自杀念头本身就会造成精神和身体上的伤害，并与其他人的状况恶化有关 疾病。自杀意念经常被临床提供者记录在他们的笔记中，但已经被证明是 仅在3%的情况下包含在诊断代码或计费代码中。历史上的自杀企图也在- 仅由帐单代码捕获。改善对有自杀意念和自杀未遂的人的识别可能 通过更早地与高危人群接触来加强预防。 越来越多的文献表明，具有纵向电子健康记录的临床预测模型 (EHR)可以预测自杀企图，表现良好。这些模型也被团体使用 像我们一样，提高大规模自杀未遂风险基因分析的能力。调查人员使用了 他们经过验证的模型可以识别自杀未遂的信号，一种“表型”，用于进行基因分析 有自杀倾向的风险有4%的遗传性。该研究小组还表明，自杀未遂的风险是 与抑郁症状、神经症症状和精神分裂症有显著的遗传相关性。 研究人员建议验证自杀意念的一种表型，并改进现有的自杀意念。 尝试风险的表型推动了两个主要生物库的大规模遗传分析，Vanderbilt的 BioVU和英国生物库。他们将使用自然语言处理(NLP)和分析范德比尔特的 EHR开发和测试自杀意念的表型。他们将使用NLP来提高对 试图改进现有算法的自杀企图。他们将把这些表型大规模应用于BioVU。他们的 斯坦福大学的研究小组成员将在英国的另一个主要生物库中使用患者报告的自杀意念史 Biobank，在不同人群中独立运行自杀意念风险的遗传分析。他们会 进一步分析临床和遗传风险因素，以更好地了解谁将从自杀意念中过渡 自杀未遂。 该项目结合了临床信息学、机器学习和大规模基因组学方面的专业知识， 以及自杀风险研究领域的专业知识。横跨两个主要的生物库 所开发的算法和方法具有最大的可移植性，便于下一步 调查。成功识别自杀意念和自杀未遂风险可能有助于临床预防。 更好地理解预测谁会从自杀念头发展到自杀行为的危险因素 将有助于将预防资源分配给最需要的人。