The prediction of study-emergent suicidal ideation in bipolar disorder: a pilot study using ecological momentary assessment data.
The prediction of study-emergent suicidal ideation in bipolar disorder: a pilot study using ecological momentary assessment data.
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DOI：
10.1111/bdi.12218
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发表时间：
2014-11
影响因子：
5.4
通讯作者：
Depp CA
中科院分区：
文献类型：
--
作者：
Thompson WK;Gershon A;O'Hara R;Bernert RA;Depp CA
双相情感障碍与临床上重要状态的特异质前体相关，如自杀意念。生态瞬时评估（EMA）-高频率的数据收集在一个主题的通常环境-提供了发展的时间，个性化的预测风险状态的潜力。本研究测试EMA数据预测临床医生评定的自杀意念中个体症状变化的能力。35名被诊断为间歇性双相情感障碍的成年人在他们的家庭环境中使用日记完成了为期八周的评估时间轴的日常情感测量。自杀意念每月一次在亲自访视时使用抑郁症状量表-临床医生评定。我们使用了一种新的应用程序的功能线性模型（FLM），以产生前瞻性预测自杀意念在人的临床医生评估的基础上，密集采样的轨迹的日常影响。在研究期间，6名参与者记录了8例自杀意念评分> 0的情况。利用消极和积极影响的轨迹，交叉验证的预测达到了88%的敏感性和95%的特异性，自杀意念升高一周前，在人的临床医生评估。该模型大大优于仅使用研究访视时获得的横截面数据的预测模型。使用EMA数据与FLM预测模型大大提高了研究中出现的自杀意念预测的准确性。采用密集采样的纵向EMA数据的预测算法可以灵敏地检测自杀意念的警告信号，以促进改善自杀风险评估和及时提供预防性干预措施。
Bipolar disorder is associated with idiosyncratic precursors of clinically important states such as suicidal ideation. Ecological momentary assessment (EMA)—high frequency data collection in a subject's usual environment—provides the potential for development of temporal, individualized prediction of risk states. The present study tested the ability of EMA data to predict individual symptom change in clinician-rated suicidal ideation. Thirty-five adults diagnosed with inter-episode bipolar disorder completed daily measures of affect in their home environments using diaries administered over an eight-week assessment timeline. Suicidal ideation was assessed monthly at in-person visits using the Inventory of Depressive Symptomatology–Clinician Rated. We used a novel application of functional linear models (FLMs) to generate prospective predictions of suicidal ideation at in-person clinician assessments based on intensively sampled trajectories of daily affect. Eight instances of suicidal ideation scores > 0 were recorded during the study period on six participants. Utilizing trajectories of negative and positive affect, cross-validated predictions attained 88% sensitivity with 95% specificity for elevated suicidal ideation one week prior to in-person clinician assessment. This model strongly outperformed prediction models using cross-sectional data obtained at study visits alone. Utilizing EMA data with FLM prediction models substantially increases the accuracy of prediction of study-emergent suicidal ideation. Prediction algorithms employing intensively sampled longitudinal EMA data could sensitively detect warning signs of suicidal ideation to facilitate improved suicide risk assessment and the timely delivery of preventative interventions.
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