Verbal Response Latency as a Behavioral Indicator of Diminished Wish to Live in a Clinical Sample of Active Duty Army Personnel with Recent Suicidal Ideation.

OBJECTIVE: Research on risk factors of suicide attempt has yielded little improvement in imminent risk detection for clinicians, due in part to the inherent limitations of self-report methodologies. Therefore, objective behavioral indicators of suicide risk that can be implemented practically with little cost in clinical settings are needed. METHOD: The current study examined verbal response latency, measured as the length of time to answer a question asking about reasons for living (i.e., What are your reasons for living or not killing yourself?), as a potential indicator of suicide risk among 97 active duty Army personnel presenting to an emergency department or behavioral health clinic for current suicide ideation and/or a recent suicide attempt. RESULTS: Verbal response latency was significantly correlated with diminished wish to live at the participant level but was not significantly correlated with wish to die or overall severity of suicidal ideation. CONCLUSION: Verbal response latency may serve as an objective indicator of suicide risk. HighlightsResponse latency to a life construct may be an objective indicator of suicide riskDelayed response latency is indicative of diminished wish to livePathological mechanisms may manifest within dyadic interactions via verbal behaviors.

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the low base rate and underpowered statistical approaches. The objective of the current study was to use machine learning to examine predictors of suicidal behaviors among high-risk suicidal Soldiers who received outpatient mental health services in a randomized controlled trial of Brief Cognitive Behavioral Therapy for Suicide Prevention (BCBT) compared to treatment as usual (TAU). Self-report measures of clinical and demographic variables, administered prior to the start of outpatient treatment to 152 participants with recent suicidal thoughts and/or behaviors were analyzed using machine learning software to identify the best combination of variables for predicting suicide attempts during or after treatment. Worst-point suicidal ideation, history of multiple suicide attempts, treatment group (i.e., BCBT or TAU), suicidogenic cognitions, and male sex were found, in combination, correctly classified 30.8% of patients who attempted suicide during the two-year follow-up period. This combination has higher sensitivity than many models that have previously been used to predict suicidal behavior. Overall, this study provides a combination of variables that can be assessed clinical to help identify high-risk suicidal individuals.