University of Chicago, 5841 S Maryland, Chicago, IL 60637.
Departments of Medicine and Public Health Sciences, The University of Chicago Biological Sciences, Chicago, Illinois, USA.
J Clin Psychiatry. 2017 Nov/Dec;78(9):1376-1382. doi: 10.4088/JCP.16m10922.
Current suicide risk screening and measurement are inefficient, have limited measurement precision, and focus entirely on suicide-related items. For this study, a psychometric harmonization between related suicide, depression, and anxiety symptom domains that provides a more balanced and complete spectrum of suicidal symptomatology was developed. The objective of this article is to describe the results of the early stages of computerized adaptive testing development for a suicide scale and pave the way for the final stage of validation.
Data from psychiatric outpatients at the University of Pittsburgh and a community health clinic were collected from January 2010 through June 2012. 789 participants were enrolled in the calibration phase; 70% were female, and 30% were male. The rate of major depressive disorder as diagnosed by DSM-5 was 47%. The item bank contained 1,008 items related to depression, anxiety, and mania, including 11 suicide items. Data were analyzed using a bifactor model to identify a core dimension between suicidal ideation, depression, anxiety, and mania items. A computerized adaptive test was developed via simulation from the actual complete item responses in 308 subjects.
111 items were identified that provided an extension of suicidality assessment to include statistically related responses from depression and anxiety domains that are syndromally associated with suicidality. All items had high loadings on the primary suicide dimension (average = 0.67; range, 0.49-0.88). Analyses revealed that a mean of 10 items (5-20) had a correlation of 0.96 with the 111-item scale, with a precision of 5 points on a 100-point scale metric. Preliminary validation data based on 290 clinician interviews revealed a 52-fold increase in the likelihood of current suicidal ideation across the range of the Computerized Adaptive Test Suicide Scale (CAT-SS).
The CAT-SS is able to accurately measure the latent suicide dimension with a mean of 10 items in approximately 2 minutes. Further validation against an independent clinician-administered assessment of suicide risk (ideation and attempts) and prediction of suicidal behavior is underway.
目前的自杀风险筛查和测量效率低下,测量精度有限,且完全集中在与自杀相关的项目上。本研究旨在开发一种与自杀、抑郁和焦虑症状领域相关的心理测量学协调,为自杀症状提供一个更平衡、更完整的谱。本文的目的是描述自杀量表计算机化自适应测试开发的早期阶段的结果,并为最终验证阶段铺平道路。
本研究的数据来自匹兹堡大学精神病门诊和社区健康诊所 2010 年 1 月至 2012 年 6 月期间的患者。789 名参与者参加了校准阶段;其中 70%为女性,30%为男性。根据 DSM-5 诊断的重性抑郁障碍的发生率为 47%。项目库包含 1008 个与抑郁、焦虑和躁狂相关的项目,包括 11 个自杀项目。采用双因素模型对数据进行分析,以确定自杀意念、抑郁、焦虑和躁狂项目之间的核心维度。通过对 308 名受试者的实际完整项目反应进行模拟,开发了一种计算机化自适应测试。
确定了 111 个项目,这些项目扩展了自杀评估,包括与自杀相关的抑郁和焦虑领域的统计相关反应,这些反应与自杀综合征相关。所有项目在主要自杀维度上的负荷均较高(平均=0.67;范围,0.49-0.88)。分析表明,平均 10 个项目(5-20 个)与 111 项量表的相关系数为 0.96,在 100 分制量表的精度为 5 分。基于 290 次临床医生访谈的初步验证数据显示,在计算机化自适应测试自杀量表(CAT-SS)的整个范围内,当前自杀意念的可能性增加了 52 倍。
CAT-SS 能够在大约 2 分钟内准确测量潜在的自杀维度,平均使用 10 个项目。正在进行针对自杀风险(意念和尝试)的独立临床评估的进一步验证,并预测自杀行为。