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: 9033542
• 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 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9033542
• 关键词: Acute; Admission activity; Alcohols; Alzheimer' s Disease; Anxiety; Characteristics; Clinical; Complement; Consent; Data; Data Analyses; 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; Patient risk; 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; Trees; Variant; Veterans; Visual; Wrist; actigraphy; analog; base; dysphoria; high risk; high risk population; ideation; improved; inclusion criteria; indexing; monitoring device; movement analysis; neuropsychiatry; novel; psychiatric symptom; public health relevance; reducing suicide; resilience; 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）： 背景和目的：预防自杀是最高的VHA优先事项。由于基本率低导致非常低的预测值，因此难以预测自杀行为的风险，因此在预测自杀行为的风险方面受到了阻碍。最近，机器学习回归树方法已经成功地更好地识别了一个从军事医院自杀后的高风险的群体。这项进展是巨大的，因为在入院后的头几个月屡屡被证明是自杀的最高风险时期之一。然而，自杀和自杀行为风险预测后（以及在任何其他时间）仍然非常挑战。例如，这项研究的主要研究者发现，在最近的过去，大多数（73％）的VHA 抑郁症患者即使在自杀死亡后的7天内被问及自杀构想。明确的需求是建立对自杀行为风险的测量，而自杀行为风险并非严重取决于患者的自我报告。最近，我们的共同投资者对非维特人​​住院患者的运动数据进行了非线性动态分析，并确定了与自杀构想相比，该信号比任何其他测试的特征更为强烈。研究设计：将对115-300名退伍军人进行一项前瞻性队列研究，以确定非退伍军人中与自杀的思想高度相关的基于特定的特定行动学的测量是否会预测退伍军人的自杀构想，自杀行为和/或再现质量。方法：将对马萨诸塞州贝德福德市的115-300名退伍军人进行分析。主要分析将集中于75-200名退伍军人，这些退伍军人具有当前的自杀念头或最近的自杀行为（SI/SB），他们没有原发性精神病，阿尔茨海默氏病或​​帕金森氏病，并且没有接受酒精排毒。将对接受酒精排毒的40-100例患者进行单独分析，一半，Si/SB，一半没有Si/Sb。参与者会穿 小巧，不引人注目的，腕表般的腕部手术，以及对自杀念头的完全自评和临床评估的评估，以及对其他精神病症状严重性的自评估。将使用从6分钟到2小时的时间帧的运动放大器对运动放大器的非线性动态分析来计算弹性指数（RI）。这些时间范围是移动数据清晰结构的时期，自杀想法的患者比没有自杀想法的患者显示出放大器的变化较小。如果确定提供酒精解毒的药物不干扰RI，则次级分析将检查整个115-300名退伍军人的样本。一个目的将集中在确定原始的弹性指数或替代运动数据指数（例如基于住院移动数据的变化）中是否可以预测自杀想法在退伍军人住院患者中的存在和严重性。该目标还将检查RI的敏感性和特异性，以检测任何自杀思想的存在以及实质性的自杀思想。 （在非退伍军人中，RI的灵敏度为72％，特异性为100％，用于检测任何自杀想法，分别为检测实质性想法的敏感性和86％和88％的敏感性）。第二个目标将决定RI是否预测子序列。在接下来的1个月，4个月或1年后，单独或与有关症状严重程度，过去病史和当前住院的数据结合使用后，自杀行为或重新住院。影响：这项研究将有助于VHA在减少退伍军人中自杀和自杀行为的高度优先努力。在此研究的方法可能是自杀行为风险评估可能有可能的特殊价值，因为它不取决于患者的症状自我报告。 VHA自杀预防计划是一种新颖且潜在的非常有益的行为风险评估，或与其他随时可用的信息相结合的一种新颖且潜在的非常有益的方法。