Assessing Actigraphy-Determined Movement Variability as a Novel Objective Marker of Suicidal Ideation and Behavior Risk in Veterans and Its Role in Integrated Suicide Risk Assessment

评估体动记录仪确定的运动变异性作为退伍军人自杀意念和行为风险的新客观标记及其在综合自杀风险评估中的作用

• 批准号: 10038802
• 负责人: ERIC G. SMITH
• 金额: --
• 依托单位: EDITH NOURSE ROGERS MEMORIAL VETERANS HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10038802
• 关键词: Acute; Admission activity; Alcohols; Alzheimer' s Disease; Anxiety; Characteristics; Clinical; Complement; Consent; Data; Devices; Diagnosis; Diagnostic; Distress; Drug Metabolic Detoxication; Enrollment; Equipment and supply inventories; Evaluation; Exclusion Criteria; Feeling suicidal; Health system; Healthcare; Hospitalization; Hour; Inpatients; Intention; Investigation; K-Series Research Career Programs; Letters; Machine Learning; Massachusetts; Measurement; Measures; Mental Depression; Mental Health; Methods; Military Hospitals; Movement; Nonlinear Dynamics; Parkinson Disease; Participant; Patient Self-Report; Patients; Perception; Performance; Pharmaceutical Preparations; Predictive Value; Prevention program; Principal Investigator; Procedures; Prospective cohort study; Provider; Psyche structure; Psychiatry; Psychotic Disorders; Recording of previous events; Reporting; Research; Research Design; Research Personnel; Risk; Risk Assessment; Risk Behaviors; Role; Safety; Sampling; Schizoaffective Disorders; Schizophrenia; Sensitivity and Specificity; Severities; Signal Transduction; Specificity; Standardization; Structure; Subgroup; Suicide; Suicide prevention; Symptoms; Testing; Time; Variant; Veterans; Visual; Wrist; actigraphy; analog; base; dysphoria; high risk; high risk population; hospital readmission; ideation; improved; inclusion criteria; indexing; monitoring device; movement analysis; neuropsychiatry; novel; psychiatric symptom; public health relevance; reducing suicide; regression trees; resilience; risk prediction; secondary analysis; suicidal; suicidal behavior; suicidal morbidity; suicidal risk; tool

 DESCRIPTION (provided by applicant): BACKGROUND AND AIMS: Suicide prevention is a top VHA priority. Suicide prevention in every health system is hampered by difficulties with predicting the risk of suicidal behavior, due to low base rates leading to very low positive predictive values. Very recently, machine learning regression tree methods have succeeded in better identifying a group at particularly high risk of suicide post-discharge from military hospitals. This advance is greatly needed, since the first months to year post-discharge has been repeatedly shown to be one of the very highest-risk periods for suicide that is known. Nevertheless, suicide and suicidal behavior risk prediction post-discharge (and at any other time) is still extremely challenging. For instance, this study's Principal Investigator has found that, in the relatively recent past, a large majority (73%) of VHA patients with depression denied suicidal ideation even when asked within 7 days of their suicide death. A clear need exists to develop measures of suicidal behavior risk that are not heavily dependent on patient self-report. Recently, our Co-Investigators conducted nonlinear dynamic analysis of movement data from non-Veteran inpatients and identified a signal that was correlated more strongly to suicidal ideation than any other characteristic tested. RESEARCH DESIGN: A prospective cohort study of 115-300 Veterans will be conducted to determine if the previously-identified specific actigraphy-based measurements highly associated with suicidal ideation in non- Veterans will predict suicidal ideation, suicidal behavior, and/or rehospitalizatin in Veterans. METHODS: An analysis of 115-300 Veterans admitted to the Bedford, Massachusetts VAMC acute psychiatry unit will be conducted. The primary analysis will focus upon 75-200 Veterans with current suicidal ideation or recent suicidal behavior (SI/SB) who do not have a primary psychotic disorder, Alzheimer's, or Parkinson's disease, and who are not undergoing alcohol detoxification. A separate analysis will be conducted of 40-100 patients undergoing alcohol detoxification, half with SI/SB and half without SI/SB. Participants will wear a small, unobtrusive, wristwatch-like actigraph on their nondominant wrist, and complete self-rated and clinician- rated assessments of suicidal ideation, as well as self-rated assessments of the severity of other psychiatric symptoms. A Resiliency Index (RI) will be calculated using nonlinear dynamic analysis of the amplitude of movements over time frames from 6 minutes - 2 hours. These time frames are the periods for which a clear structure to the movement data is evident, with patients with suicidal ideation showing less variation in amplitude than patients without suicidal ideation. If medications given for alcohol detoxification are determined to not interfere with the RI, then a secondary analysis will examine the entire sample of 115-300 Veterans. One Aim will focus upon determining whether the original Resiliency Index or alternative movement data indices, such as one based on the change in the movement data over the hospitalization, predicts the presence and severity of suicidal ideation among Veteran inpatients. This aim will also examine the sensitivity and specificity of the RI for detecting the presence of any suicidal ideation, and of substantial suicidal ideation. (In non- Veterans, the RI showed a sensitivity of 72% and a specificity of 100% for detecting any suicidal ideation, and 86% and 88%, respectively, for detecting substantial ideation). The second Aim will determine whether the RI predicts subsequent suicidal behavior or rehospitalization over the next 1 month, 4 months, or 1 year after discharge, alone or combined with data about symptom severity, past history, and the present hospitalization. IMPACT: This study will contribute substantially to the VHA's high priority efforts to reduce suicide and suicidal behavior among Veterans. The approach studied here potentially likely particular value for suicidal behavior risk assessment in that it is not dependent on patient self-report of symptoms. This study is strongly supported by the VHA Suicide Prevention Program as a novel and potentially highly beneficial approach to suicidal behavior risk assessment, alone or combined with other readily available information.

 描述（由申请人提供）： 背景和目标：预防自杀是 VHA 的首要任务。最近，机器学习回归树的出现，导致自杀行为风险预测困难，从而阻碍了自杀预防。方法已成功地更好地识别出军队医院出院后自杀风险特别高的群体，这一进展是非常必要的，因为出院后的头几个月到一年已被反复证明是风险最高的时​​期之一。对于自杀来说然而，众所周知，出院后（以及任何其他时间）的自杀和自杀行为风险预测仍然极具挑战性，例如，本研究的首席研究员发现，在最近的过去，大多数人（73%）都存在自杀倾向。的VHA 抑郁症患者即使在自杀死亡后 7 天内也否认有自杀意念，显然需要制定不严重依赖于患者自我报告的自杀行为风险衡量标准。研究设计：将对 115-300 名退伍军人进行一项前瞻性队列研究。以确定先前确定的与非退伍军人高度相关的基于体动记录的特定测量是否可以预测退伍军人的自杀意念、自杀行为和/或再住院方法：对马萨诸塞州贝德福德 VAMC 急诊室收治的 115-300 名退伍军人进行分析。精神病学部门将针对 75-200 名目前或近期有自杀意念的退伍军人进行主要分析。没有原发性精神障碍、阿尔茨海默病或帕金森病且未接受酒精戒毒的自杀行为 (SI/SB) 将对 40-100 名接受酒精戒毒的患者进行单独分析，其中一半患有 SI/SB。一半没有 SI/SB 的参与者将佩戴 SI/SB。 在他们的非惯用手上安装小型、不显眼的手表状活动记录仪，并对自杀意念进行完整的自我评估和临床医生评估，以及对其他精神症状严重程度的自我评估。通过对 6 分钟至 2 小时的时间范围内的运动幅度进行非线性动态分析来计算这些时间范围是运动数据清晰结构明显的时期，有自杀意念的患者表现出较小的变化。如果确定用于酒精解毒的药物不会干扰 RI，则二次分析将检查 115-300 名退伍军人的整个样本，重点是确定原始弹性指数或另一种运动数据指数，例如基于住院期间运动数据变化的指数，可以预测退伍军人住院患者自杀意念的存在和严重程度。这一目标还将检查 RI 的敏感性和特异性。用于检测是否存在自杀意念和实质性自杀意念（在非退伍军人中，RI 检测任何自杀意念的敏感性为 72%，特异性为 100%，分别为 86% 和 88%）。用于检测实质性意念）。第二个目标将确定 RI 是否预测未来 1 个月、4 个月或 1 个月内的后续自杀行为或再次住院。出院后一年，单独或结合有关症状严重程度、既往病史和目前住院治疗的数据。 影响：这项研究将对 VHA 减少退伍军人自杀和自杀行为的高度优先工作做出贡献。自杀行为风险评估的价值在于它不依赖于患者自我报告的症状。这项研究得到了 VHA 自杀预防计划的大力支持，作为自杀行为风险评估的一种新颖且可能非常有益的方法，可以单独使用或与其他方法结合使用。随时可用的信息。