Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task.
Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task.
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DOI：
10.1017/s0033291722001003
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发表时间：
2023-07
影响因子：
6.9
通讯作者：
Interian, Alejandro
中科院分区：
文献类型：
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作者：
Myers, Catherine E.;Dave, Chintan, V;Callahan, Michael;Chesin, Megan S.;Keilp, John G.;Beck, Kevin D.;Brenner, Lisa A.;Goodman, Marianne S.;Hazlett, Erin A.;Niculescu, Alexander B.;St Hill, Lauren;Kline, Anna;Stanley, Barbara H.;Interian, Alejandro
关键词：
神经认知测试可能会推进预测近期自杀风险的目标。目前的研究考察了在去/不去(GNG)任务上的表现，以及用于提取潜在认知变量的计算建模，是否可以增强对自杀高危个体在未来90天内自杀企图的预测。136名有自杀高危风险的退伍军人之前完成了一项基于计算机的GNG任务，要求对目标刺激做出快速反应(GO)，同时对不频繁的箔刺激做出抑制反应(No-Go)；行为变量包括对箔的错误警报(抑制失败)和对目标的错过反应。我们对这些数据进行了二次分析，结果被定义为实际自杀企图(ASA)、其他与自杀相关的事件(OtherSE)，如中断/流产的企图或准备行为，或者在GNG检测后90天内两者都不(NOSE)，以检验GNG变量是否可以改善ASA预测，而不是标准的临床变量。计算模型(线性弹道累加器，LBA)也被用来解释群体差异的认知机制。在GNG上，增加的漏检率选择性地预测ASA，而增加的错误警报率预测90天内的其他SE(没有ASA)。在LBA模型中，ASA(而不是OtherSE)与目标决策效率的降低有关，这表明证据积累过程中的差异与即将到来的ASA特别相关。这些发现表明，GNG可能会改善对近期自杀风险的预测，在那些在未来90天内试图自杀的人中有不同的行为模式。计算模型表明，近期自杀未遂风险的个体在认知上存在质的差异。
Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide. 136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences. On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window). In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA. These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.