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，尝试自杀并接受医院治疗的人处于危险之中 一年内自杀的风险很高。自杀行为警告信号 (WS) 的识别和验证 – 近期风险因素——是国家优先事项。确定个人是否处于危险之中现在会产生巨大影响 急诊科内急症护理环境中的决定（例如，是否收治患者）和危机 线路（例如，是否派遣流动危机小组）。然而，关于个人“何时” 有自杀企图的近期风险或 WS（即几分钟内、几小时内、一天内）。这在临床上和 理论驱动的研究解决了我们对自杀未遂的 WS 理解中的关键差距。我们力求 a) 发现自杀未遂的新警告信号，b) 验证并生成第一个风险 估计，对于这些候选者和 WS 在最近的理论表述中提出的，c）比较风险- WS 的估计，以确定目前在急症护理环境中风险评估中优先考虑的那些是否是 d) 开发新算法来检测患者特定 WS 内容的语言信号 叙述性访谈。我们提出一项多地点混合方法研究，目前将招募 400 名成年人 因自杀未遂而在中西部北部的两个学术医疗中心住院。科目将是 要求用自己的话讲述他们的尝试的叙事故事，并进行详细的半结构化 进行访谈，以获得有关尝试当天和前一天假设的 WS 的系统数据。我们将 使用由专家使用定性编码的主题叙述故事来发现潜在的新颖的 WS 候选者 方法（目标 1）。接下来，我们将验证先验且新颖的候选 WS（目标 2）。案例交叉 将使用一种受试者内部设计方法，将受试者用作自己的控制。半结构化 通过比较当天假设的 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（低风险控制期）。最后我们将进行开发和测试 一种检测特定 WS 内容的语言信号的算法（探索性目标 3）。自然语言 语言处理和深度学习模型将用于检测叙述中的 WS。 WS 为 由于检查时间的实际挑战，自杀企图的研究异常困难 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 以严谨的方式进行挑战。该研究旨在提供有科学依据的 WS 来为临床提供信息 决策、患者/家庭教育和自动风险识别。