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，企图自杀并接受医院治疗的人 一年内自杀的风险很高。自杀行为的警告信号（WS）的识别和验证- 近期风险因素-是国家优先事项。确定一个人是否处于危险之中， 在急诊科内的急性护理环境中的决定（例如，是否接纳病人）和危机 线（例如，是否派遣移动的危机小组）。然而，很少有人研究“什么时候”个人是 处于近期风险或WS（即，几分钟、几小时、一天之内）的自杀企图。临床上-和 理论驱动的研究解决了我们对自杀企图WS理解的关键差距。我们寻求 a）发现新的自杀企图候选警告信号，B）验证并产生第一个风险 估计，对于这些候选人和WS在最近的理论公式中提出，c）比较风险- WS的估计值，以确定目前在急性护理环境中的风险评估中优先考虑的风险是否 保证，和d）开发新的算法，以检测患者的特定WS内容的语言信号， 叙事访谈我们提出了一个多地点的混合方法研究，目前将招募400名成年人 在上中西部的两个学术医疗中心因自杀未遂住院。受试者将 要求他们尝试用自己的话讲述故事的叙述，并且还要经过详细的半结构化 访谈以获得关于尝试当天和前一天的假设WS的系统数据。我们将 发现潜在的小说WS候选人使用主题的叙事故事编码的专家使用定性 方法（目标1）。接下来，我们将验证先验和新的候选WS（Aim 2）。病例交叉 将使用方法学，使用受试者作为自身对照的受试者内设计。半结构化 访谈数据通过比较当天假设的WS的存在/强度进行分析。 尝试（高风险病例期）至前一天（低风险控制期）。最后，我们将开发和测试 检测特定WS内容的语言信号的算法（探索性目标3）。自然语言 语言的处理和深度学习模型将用于检测叙述中的WS。WS用于 自杀企图是非常困难的研究，由于实际的挑战，检查小时， 在自杀之前该项目采用创新的定性和定量方法来解决这一问题 以严格的方式挑战。本研究旨在提供有科学依据的WS， 决策制定、患者/家庭教育和自动风险识别。