Abstract：
BACKGROUND: Although the effect sizes are modest, insomnia is consistently associated with suicidal thoughts and behaviors. Subgroup analyses can efficiently identify for whom insomnia is most relevant to suicidal ideation. To improve clinical case identification, the present study sought to identify subclusters of lifetime suicidal ideators for whom insomnia was most closely related to current suicidal ideation.
METHODS: Data on N = 4750 lifetime suicidal ideators were extracted from the Military Suicide Research Consortium\'s Common Data Elements. Data on sociodemographic characteristics, severity and history of suicidal thoughts and behaviors, and related clinical characteristics were clustered by unsupervised machine learning algorithms. Robust Poisson regression estimated cluster by insomnia associations with current suicidal ideation.
RESULTS: Three clusters were identified: a modest symptom severity cluster (N = 1757, 37.0 %), an elevated severity cluster (N = 1444 30.4 %), and a high severity cluster (N = 1549 32.6 %). In Cluster 1, insomnia was associated with current suicidal ideation (PRR 1.29 [1.13-1.46]) and remained significant after adjusting for sociodemographic and clinical covariates. In Cluster 2, insomnia was associated with current suicidal ideation (PRR 1.14 [1.01-1.30]), but not after adjusting for sociodemographic and clinical covariates. In Cluster 3, insomnia was associated with current suicidal ideation (PRR 1.12 [1.03-1.21]) and remained significant after adjusting for sociodemographic covariates, but not clinical covariates.
CONCLUSIONS: Cross-sectional design, lack of diagnostic data, non-representative sample.
CONCLUSIONS: Insomnia appears more closely related to current suicidal ideation among modest severity individuals than other subgroups. Future work should use prospective designs and more comprehensive risk factor measures to confirm these findings.
METHODS: Data on N = 4750 lifetime suicidal ideators were extracted from the Military Suicide Research Consortium\'s Common Data Elements. Data on sociodemographic characteristics, severity and history of suicidal thoughts and behaviors, and related clinical characteristics were clustered by unsupervised machine learning algorithms. Robust Poisson regression estimated cluster by insomnia associations with current suicidal ideation.
RESULTS: Three clusters were identified: a modest symptom severity cluster (N = 1757, 37.0 %), an elevated severity cluster (N = 1444 30.4 %), and a high severity cluster (N = 1549 32.6 %). In Cluster 1, insomnia was associated with current suicidal ideation (PRR 1.29 [1.13-1.46]) and remained significant after adjusting for sociodemographic and clinical covariates. In Cluster 2, insomnia was associated with current suicidal ideation (PRR 1.14 [1.01-1.30]), but not after adjusting for sociodemographic and clinical covariates. In Cluster 3, insomnia was associated with current suicidal ideation (PRR 1.12 [1.03-1.21]) and remained significant after adjusting for sociodemographic covariates, but not clinical covariates.
CONCLUSIONS: Cross-sectional design, lack of diagnostic data, non-representative sample.
CONCLUSIONS: Insomnia appears more closely related to current suicidal ideation among modest severity individuals than other subgroups. Future work should use prospective designs and more comprehensive risk factor measures to confirm these findings.
摘要：
背景：尽管效果大小适中，失眠始终与自杀念头和行为有关。亚组分析可以有效地确定失眠与自杀意念最相关的人。为了提高临床病例识别，本研究试图确定终生自杀意念亚簇，其中失眠与当前自杀意念最密切相关。
方法：从军事自杀研究联盟的共同数据元素中提取了N=4750个终生自杀意念者的数据。关于社会人口特征的数据，自杀念头和行为的严重程度和历史，和相关临床特征通过无监督机器学习算法进行聚类。稳健泊松回归通过失眠与当前自杀意念的关联来估计聚类。
结果：确定了三个簇：一个适度的症状严重程度簇（N=1757，37.0%），严重程度升高的群集（N=144430.4%），和一个高严重程度的集群(N=154932.6%)。在第1组中，失眠与当前的自杀意念（PRR1.29[1.13-1.46]）相关，并且在调整社会人口统计学和临床协变量后仍然显着。在第2组中，失眠与当前的自杀意念有关（PRR1.14[1.01-1.30]），但不是在调整社会人口统计学和临床协变量后。在第3组中，失眠与当前的自杀意念（PRR1.12[1.03-1.21]）相关，并且在调整社会人口统计学协变量后仍然显着。但不是临床协变量。
结论：横截面设计，缺乏诊断数据，不具有代表性的样本。
结论：与其他亚组相比，在轻度个体中，失眠似乎与当前的自杀意念更密切相关。未来的工作应该使用前瞻性设计和更全面的风险因素措施来确认这些发现。
方法：从军事自杀研究联盟的共同数据元素中提取了N=4750个终生自杀意念者的数据。关于社会人口特征的数据，自杀念头和行为的严重程度和历史，和相关临床特征通过无监督机器学习算法进行聚类。稳健泊松回归通过失眠与当前自杀意念的关联来估计聚类。
结果：确定了三个簇：一个适度的症状严重程度簇（N=1757，37.0%），严重程度升高的群集（N=144430.4%），和一个高严重程度的集群(N=154932.6%)。在第1组中，失眠与当前的自杀意念（PRR1.29[1.13-1.46]）相关，并且在调整社会人口统计学和临床协变量后仍然显着。在第2组中，失眠与当前的自杀意念有关（PRR1.14[1.01-1.30]），但不是在调整社会人口统计学和临床协变量后。在第3组中，失眠与当前的自杀意念（PRR1.12[1.03-1.21]）相关，并且在调整社会人口统计学协变量后仍然显着。但不是临床协变量。
结论：横截面设计，缺乏诊断数据，不具有代表性的样本。
结论：与其他亚组相比，在轻度个体中，失眠似乎与当前的自杀意念更密切相关。未来的工作应该使用前瞻性设计和更全面的风险因素措施来确认这些发现。
