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Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.
We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.
Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.
An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.
Despite a significant need, there are currently no rigorously developed empirically based models for what personal recovery from a suicidal episode looks like.
To develop a theoretical model of personal recovery after a suicidal episode, based on a comprehensive literature review and stakeholder feedback.
A scoping review of all empirical studies on this topic was conducted, followed by a thematic analysis to create a preliminary framework. Consultation-based revisions were then made based on feedback from a stakeholder panel to develop the final theoretical model.
The final model comprised seven themes: choosing life, optimising identity, understanding oneself, rediscovering meaning, acceptance, growing connectedness and empowerment (acronym ‘COURAGE’). Although there are some similarities between COURAGE and other models of personal recovery, there are components, such as ‘choosing life’ and ‘understanding oneself’, that are specific to recovery after an acute suicidal episode.
To our knowledge, this is the first study to use a comprehensive literature review with stakeholder feedback to develop a conceptual model of personal recovery after an acute suicidal episode. This model has important implications for both researchers and clinicians to consider. Looking ahead, COURAGE can inform the reconceptualisation of assessment, research and clinical care of individuals who have experienced a suicidal episode.
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.
This study examined the relationship of self-reported histories of childhood trauma to measures of affective instability in a sample of unmedicated outpatients with various personality disorders (n=174).
Childhood trauma was measured by the Childhood Trauma Questionnaire. Affective instability comprises at least two dimensions: affective lability, assessed using the Affective Lability Scale, and affective intensity, assessed using the Affective Intensity Measure.
A history of emotional abuse was the only trauma variable that significantly correlated with the affect measures in the total sample (r=.21–.30). More fine-grained analyses revealed that the relationship of emotional abuse and affective instability measures varied as a function of both gender and personality disorder type. In subjects with borderline personality disorder, the correlation for emotional abuse was greatly attenuated for both Affective Lability Scale (r=.10) and Affective Intensity Measure (r=.15) total scores.
This suggests that nontrauma-related factors may be more predominant in affective dyscontrol in individuals with borderline personality disorder.
It has been reported that borderline personality related characteristics can be observed in children, and that these characteristics are associated with increased risk for the development of borderline personality disorder. It is not clear whether borderline personality related characteristics in children share etiological features with adult borderline personality disorder. We investigated the etiology of borderline personality related characteristics in a longitudinal cohort study of 1,116 pairs of same-sex twins followed from birth through age 12 years. Borderline personality related characteristics measured at age 12 years were highly heritable, were more common in children who had exhibited poor cognitive function, impulsivity, and more behavioral and emotional problems at age 5 years, and co-occurred with symptoms of conduct disorder, depression, anxiety, and psychosis. Exposure to harsh treatment in the family environment through age 10 years predicted borderline personality related characteristics at age 12 years. This association showed evidence of environmental mediation and was stronger among children with a family history of psychiatric illness, consistent with diathesis–stress models of borderline etiology. Results indicate that borderline personality related characteristics in children share etiological features with borderline personality disorder in adults and suggest that inherited and environmental risk factors make independent and interactive contributions to borderline etiology.
Background. Functional MRI studies have begun to identify neural networks implicated in visuospatial working memory in healthy volunteers and patients with schizophrenia. The study of schizotypal personality disorder (SPD) provides regional analysis in unmedicated patients in the schizophrenia spectrum.
Method. Unmedicated patients with SPD by DSM-IV criteria and normal controls were assessed with fMRI while performing a visuospatial working-memory task. It required the subjects to retain the location of three dots located on the circumference of an imaginary circle and then respond to a query display in which one dot was presented and the subject required to press a button to indicate whether the probe dot location was previously displayed. Subject groups did not differ significantly in spatial memory scores. The exact Talairach and Tournoux coordinates of brain areas previously reported to show activation with spatial memory tasks were assessed.
Results. The majority of these locations showed BOLD response activation significantly less in patients during the memory retention period, including the left ventral prefrontal cortex, superior frontal gyrus, intraparietal cortex and posterior inferior gyrus. Regions in the right middle prefrontal and prestriate cortex showed greater activation at a trend level for patients with SPD than for normal controls. In addition, we replicated the findings of increased activation with the task in healthy volunteers in the premotor areas, ventral prefrontal cortex and parietal cortex.
Conclusions. SPD patients show decreased activation compared to healthy volunteers in key frontal regions and we also provided a partial replication of findings reported in healthy subjects.
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