To enhance and standardize the assessment of suicidal self-directed violence (SDV) in military populations, the Military Suicide Research Consortium developed the Common Data Elements (CDEs). Previous research supported the CDEs as assessing a higher-order factor of suicidal SDV in military populations. The present study had two aims: 1) confirm the bifactor structure of the CDEs in a high-risk sample, and 2) assess the ability of the factorially derived suicidal SDV factor to predict suicide attempts and return to care for suicidal ideation over 3-month follow-up. Utilizing a sample of service members referred for a psychiatric evaluation (N = 1,044), the CDE structure was assessed with confirmatory bifactor modeling. Logistic regressions and receiver operating characteristic (ROC) analyses were used to assess the suicidal SDV risk factor's prediction of suicide attempts and return to care for suicidal ideation during follow-up (n = 758). Bifactor modeling suggested adequate fit for the overarching suicidal SDV risk factor. Logistic regressions supported the overarching suicidal SDV risk factor as a predictor of suicide attempts (OR = 4.07, p < .001) and return to care for suicidal ideation (OR = 2.81, p < .001) over follow-up. However, ROC analyses suggested that the model including the suicidal SDV risk factor was only significantly better at classifying suicide attempts over follow-up (not return to care for suicidal ideation) than the model that did not include it (AUC difference = 0.15, p < .001). Findings suggest that the shared variance assessed across CDEs better predicts future suicide attempts beyond any individual suicide-related constructs. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

---

军事自杀研究协会的共同数据要素：临床样本中的自杀意念和自杀企图的双因素分析和纵向预测能力。

为了增强和规范军事人群自杀性自发暴力（SDV）的评估，军事自杀研究联合会开发了通用数据元素（CDE）。先前的研究支持CDE评估军事人群中自杀性SDV的高阶因素。本研究有两个目标：1）确认高风险样本中CDE的双因素结构，以及2）评估因式衍生的自杀性SDV因子预测自杀企图并返回3级自杀意念的能力。一个月的随访。利用被推荐用于精神病学评估的服务人员样本（N = 1,044），通过确认性双因素模型评估了CDE结构。Logistic回归和接收者操作特征（ROC）分析用于评估自杀性SDV危险因素' 对自杀企图的预测，并在随访期间恢复对自杀意念的治疗（n = 758）。双因素建模表明，适合自杀性SDV的主要危险因素。Logistic回归支持自杀性SDV总体危险因素可作为自杀未遂的预测指标（OR = 4.07，p <.001），并在随访中恢复自杀意念（OR = 2.81，p <.001）。但是，ROC分析表明，包括自杀性SDV危险因素的模型比不包括自杀性模型的模型（AUC差异= 0.15，p <.001）。研究结果表明，跨CDE评估的共享方差可以更好地预测除任何与自杀相关的个体之外的未来自杀企图。