Mixed methods examination of warning signs within 24 hours of suicide attempt in hospitalized adults

住院成人自杀未遂 24 小时内警告信号的混合方法检查

• 批准号: 10710712
• 负责人: COURTNEY L BAGGE
• 金额: $ 80.18万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10710712
• 关键词: Academic Medical Centers; Accident and Emergency department; Address; Admission activity; Adult; Algorithms; Caring; Cause of Death; Clinical; Code; Collaborations; Crossover Design; Data; Development; Distal; Education; Emergency treatment; Event; Family; Formulation; Healthcare; Hospitalization; Hospitals; Hour; Incidence; Individual; Interview; Language; Learning; Life; Linguistics; Methodology; Methods; Midwestern United States; Modeling; National Institute of Mental Health; Natural Language Processing; Patients; Prevention; Process; Provider; Recording of previous events; Research; Risk; Risk Assessment; Risk Estimate; Risk Factors; Sampling; Signal Transduction; Site; Structure; Suicide; Suicide attempt; Suicide prevention; Testing; Time; Training; Transcript; Validation; Work; acute care; candidate validation; clinical decision-making; clinical risk; community setting; deep learning model; design; detection method; experience; high risk; improved; innovation; member; mortality risk; novel; patient safety; programs; prospective; recruit; suicidal; suicidal act; suicidal behavior; suicidal risk; suicide rate; support tools; systematic review; tool; validation studies

Suicide is a leading cause of death, and individuals who attempt suicide and receive hospital treatment are at high risk for suicide within a year. The identification and validation of warning signs (WS) for suicidal behavior – near-term risk factors– is a national priority. Determining if an individual is at risk now drives high-impact decisions in acute care settings within emergency departments (e.g., whether to admit a patient) and crisis lines (e.g., whether to send a mobile crisis team). Yet, there has been little research on ‘when’ individuals are at near-term risk or WS (i.e., within minutes, hours, a day) for suicide attempts. This clinically- and theoretically-driven study addresses critical gaps in our understanding of WS for suicide attempts. We seek to a) discover novel warning signs candidates for suicide attempts, b) validate, and generate the first risk estimates, for these candidates and WS put forward in recent theoretical formulations, c) compare risk- estimates of WS to determine if those currently prioritized in risk assessments in acute care settings is warranted, and d) develop new algorithms to detect linguistic signals of specific WS content in patients' narrative interviews. We propose a multi-site mixed-methods study that will recruit 400 adults currently hospitalized for a suicide attempt in two academic medical centers in the Upper Midwest. Subjects will be asked to tell the narrative story of their attempt in their own words, and also undergo a detailed semi-structured interview to obtain systematic data about hypothesized WS on the day of the attempt and the day prior. We will discover potential novel WS candidates using subjects’ narrative stories coded by experts using qualitative methodology (Aim 1). Next, we will validate a priori and novel candidate WS (Aim 2). Case-crossover methodology will be used, a within-subjects design that uses subjects as their own control. The semi-structured interview data are analyzed through comparisons of the presence/intensity of hypothesized WS on the day of the attempt (high-risk case period) to the day prior (lower risk control period). Finally, we will develop and test an algorithm to detect linguistic signals of specific WS content (Exploratory Aim 3). Natural language processing and deep learning models of language will be used to detect WS within the narratives. WS for suicide attempts are extraordinarily difficult to study due the practical challenge of examining the hours preceding an act of suicide. The project uses innovative qualitative and quantitative methods to address this challenge in a rigorous fashion. The study is designed to provide scientifically grounded WS to inform clinical decision-making, patient/family education, and automated risk identification.

自杀是死亡的主要原因，试图自杀和接受医院治疗的个人处于 一年内自杀的高风险。对自杀行为的警告标志（WS）的识别和验证 - 近期风险因素 - 是国家优先事项。确定个人现在是否处于危险之中 急诊部门的急性护理环境中的决定（例如，是否承认患者）和危机 线（例如，是否发送移动危机团队）。但是，关于“何时”的人几乎没有研究 以近期风险或WS（即在几分钟，小时，一天之内）进行自杀企图。这个临床和 理论驱动的研究解决了我们对WS自杀尝试的理解中的关键差距。我们寻求 a）发现新颖的警告标志是自杀企图的候选人，b）验证并产生第一种风险 对于这些候选人和WS在最近的理论公式中提出的估计，c）比较风险 - WS的估算值以确定当前在急性护理环境中的风险评估中优先考虑的估计值是 保证和d）开发新算法以检测患者特定WS含量的语言信号 叙事采访。我们提出了一项多站点的混合方法研究，该研究将招募400名成年人 在中西部上部的两个学术医疗中心进行自杀企图住院。受试者将是 被要求用自己的话说讲述他们尝试的叙述，并经历详细的半结构 访谈以获取有关假设WS在尝试当天和前一天的系统数据。我们将 使用专家使用定性编码的主题编码的主题叙事故事来发现潜在的小说WS候选人 方法论（目标1）。接下来，我们将验证先验和新颖的候选WS（AIM 2）。案例交叉 将使用方法，这是一种主题内设计，将主体用作自己的控制。半结构化 通过比较假设WS的存在/强度来分析访谈数据 尝试（高风险案例期）到前一天（较低的风险控制期）。最后，我们将开发和测试 一种检测特定WS含量的语言信号的算法（探索性目的3）。自然语言 语言的处理和深度学习模型将用于检测叙事中的WS。 WS 由于检查小时的实际挑战，自杀企图非常难以研究 在自杀行为之前。该项目使用创新的定性和定量方法来解决这个问题 严格的方式挑战。该研究旨在提供科学扎根的WS，以告知临床 决策，患者/家庭教育和自动化风险标识。