Faculty Bibliography

Importance/UNASSIGNED:Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data with machine-learning approaches in psychiatry may help advance the understanding of suicide risk. Objective/UNASSIGNED:To examine sex-specific risk profiles for death from suicide using machine-learning methods and data from the population of Denmark. Design, Setting, and Participants/UNASSIGNED:A case-cohort study nested within 8 national Danish health and social registries was conducted from January 1, 1995, through December 31, 2015. The source population was all persons born or residing in Denmark as of January 1, 1995. Data were analyzed from November 5, 2018, through May 13, 2019. Exposures/UNASSIGNED:Exposures included 1339 variables spanning domains of suicide risk factors. Main Outcomes and Measures/UNASSIGNED:Death from suicide from the Danish cause of death registry. Results/UNASSIGNED:A total of 14 103 individuals died by suicide between 1995 and 2015 (10 152 men [72.0%]; mean [SD] age, 43.5 [18.8] years and 3951 women [28.0%]; age, 47.6 [18.8] years). The comparison subcohort was a 5% random sample (n = 265 183) of living individuals in Denmark on January 1, 1995 (130 591 men [49.2%]; age, 37.4 [21.8] years and 134 592 women [50.8%]; age, 39.9 [23.4] years). With use of classification trees and random forests, sex-specific differences were noted in risk for suicide, with physical health more important to men's suicide risk than women's suicide risk. Psychiatric disorders and possibly associated medications were important to suicide risk, with specific results that may increase clarity in the literature. For example, stress disorders among unmarried men older than 30 years were important factors for suicide risk in the presence of depression (risk, 0.54). Generally, diagnoses and medications measured 48 months before suicide were more important indicators of suicide risk than when measured 6 months earlier. Individuals in the top 5% of predicted suicide risk appeared to account for 32.0% of all suicide cases in men and 53.4% of all cases in women. Conclusions and Relevance/UNASSIGNED:Despite decades of research on suicide risk factors, understanding of suicide remains poor. In this study, the first to date to develop risk profiles for suicide based on data from a full population, apparent consistency with what is known about suicide risk was noted, as well as potentially important, understudied risk factors with evidence of unique suicide risk profiles among specific subpopulations.

While emotion dysregulation is associated with many psychological disorders, including posttraumatic stress disorder (PTSD), it remains uncertain whether pre-existing emotion dysregulation increases individual risk for prospectively developing PTSD in the aftermath of trauma exposure. Thus, the objective of the current study was to determine whether emotion dysregulation could prospectively predict the development of chronic PTSD symptoms following a traumatic event above and beyond other known associated factors, including depressive symptoms, baseline PTSD symptoms, total traumas experienced, and exposure to interpersonal trauma. Participants (N = 135) were recruited from the emergency department (ED) at Grady Memorial Hospital in Atlanta and follow-up assessments were conducted at 1-, 3-, 6-, and 12-months following trauma exposure. Latent Growth Mixture Modeling was used to identify PTSD symptom trajectories based on symptoms assessed at 1, 3, 6, and 12 months; three trajectories emerged: "chronic", "recovery", and "resilient". For the present study, probability of chronic PTSD symptoms was used as the outcome variable of interest. Linear regression modeling showed that emotion dysregulation was significantly associated with probability of developing chronic PTSD symptoms (p = 0.001) and accounted for an additional 7% of unique predictive variance when controlling for trauma exposure, baseline PTSD, and depressive symptoms. Our findings suggest that emotion dysregulation can be used as both a predictor of chronic PTSD and as a treatment target. Thus, identifying individuals with high levels of emotion dysregulation at the time of trauma and implementing treatments designed to improve emotion regulation could aid in decreasing the development of chronic PTSD among these at-risk individuals.

OBJECTIVE/UNASSIGNED:Although several reports have documented heightened systemic inflammation in posttraumatic stress disorder (PTSD), few studies have assessed whether inflammatory markers serve as prospective biomarkers for PTSD risk. The present study aimed to characterize whether peripheral immune factors measured in blood samples collected in an emergency department immediately after trauma exposure would predict later chronic development of PTSD. METHODS/UNASSIGNED:Participants (N=505) were recruited from a hospital emergency department and underwent a 1.5-hour assessment. Blood samples were drawn, on average, about 3 hours after trauma exposure. Follow-up assessments were conducted 1, 3, 6, and 12 months after trauma exposure. Latent growth mixture modeling was used to identify classes of PTSD symptom trajectories. RESULTS/UNASSIGNED:Three distinct classes of PTSD symptom trajectories were identified: chronic (N=28), resilient (N=160), and recovery (N=85). Multivariate analyses of covariance revealed a significant multivariate main effect of PTSD symptom trajectory class membership on proinflammatory cytokines. Univariate analyses showed a significant main effect of trajectory class membership on plasma concentrations of proinflammatory tumor necrosis factor α (TNFα) and interferon-γ (IFNγ). Concentrations of proinflammatory TNFα and IFNγ were significantly lower in individuals in the chronic PTSD class compared with those in the recovery and resilient classes. There were no significant differences in interleukin (IL) 1β and IL-6 concentrations by PTSD symptom trajectory class. Anti-inflammatory and other cytokines, as well as chemokines and growth factor concentrations, were not associated with development of chronic PTSD. CONCLUSIONS/UNASSIGNED:Overall, the study findings suggest that assessing the proinflammatory immune response to trauma exposure immediately after trauma exposure, in the emergency department, may help identify individuals most at risk for developing chronic PTSD in the aftermath of trauma.

Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiology. This heterogeneity impacts the field's ability to characterize, predict, and remediate maladaptive responses to trauma. Machine learning (ML) approaches are increasingly utilized to overcome this foundational problem in characterization, prediction, and treatment selection across branches of medicine that have struggled with similar clinical realities of heterogeneity in etiology and outcome, such as oncology. In this article, we review and evaluate ML approaches and applications utilized in the areas of posttraumatic stress, stress pathology, and resilience research, and present didactic information and examples to aid researchers interested in the relevance of ML to their own research. The examined studies exemplify the high potential of ML approaches to build accurate predictive and diagnostic models of posttraumatic stress and stress pathology risk based on diverse sources of available information. The use of ML approaches to integrate high-dimensional data demonstrates substantial gains in risk prediction even when the sources of data are the same as those used in traditional predictive models. This area of research will greatly benefit from collaboration and data sharing among researchers of posttraumatic stress disorder, stress pathology, and resilience.

OBJECTIVE:Adjustment to chronic disability is a topic of considerable focus in the rehabilitation sciences and constitutes an important public health problem given the adverse outcomes associated with maladjustment. While existing literature has established an association between disability onset and elevated rates of depression, resilience and alternative patterns of adjustment have received substantially less empirical inquiry. The current study sought to model heterogeneity in mental health responding to disability onset in later life while exploring the impact of socioeconomic resources on these latent patterns of adaptation. METHOD/METHODS:= 3,204) were followed across four measurement points representing a 6-year period. RESULTS:Four trajectories of depressive symptoms were identified: resilience (56.5%), emerging depression (17.2%), remitting depression (13.4%), and chronic depression (12.9%). Socioeconomic resources were then analyzed as predictors of trajectory membership. Prior education and financial assets at the time of disability onset robustly predicted class membership in the resilient class compared to all other classes. CONCLUSION/CONCLUSIONS:The course of adjustment in response to disability onset is heterogeneous. Our results confirm the presence of multiple pathways of adjustment surrounding late-life disability, with the most common outcome being near-zero depressive symptoms for the duration of the study. Socioeconomic resources strongly predicted membership in the resilient class compared with all other classes, indicating that such resources may play a protective role during the stress of physical disability onset. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Background/UNASSIGNED:Exposure to a traumatic event leads to posttraumatic stress disorder (PTSD) in 10-20% of exposed individuals. Predictors of risk are needed to target early interventions to those who are most vulnerable. The objective of the study was to test whether a noninvasive mobile device that measures a physiological biomarker of autonomic nervous system activation could predict future PTSD symptoms. Methods/UNASSIGNED:Skin conductance response (SCR) was collected during a trauma interview in the emergency department within hours of exposure to trauma in 95 individuals. Trajectories of PTSD symptoms over 12 months post-trauma were identified using Latent Growth Mixture Modeling. Results/UNASSIGNED:<0.00001). Conclusions/UNASSIGNED:The current study is the first prospective study of PTSD showing SCR in the immediate aftermath of trauma predicts subsequent development of chronic PTSD. This finding points to an easily obtained, and neurobiologically informative, biomarker in emergency departments that can be disseminated to predict the development of PTSD.