Faculty Bibliography

Charles Marmar will present data from a prospective longitudinal cohort study of active-duty military. The total sample size of this ongoing study is n = 1800 and currently there are pre-and post deployment data available for over 600 individuals. Outcome trajectories were modeled and four primary outcome groups were identified. Pre-deployment inflammatory markers predicted progressive symptomatology following deployment

The intensive care unit (ICU) has been portrayed as psychologically stressful, with a growing body of research substantiating elevated rates of depression, posttraumatic stress disorder (PTSD), and other psychological disruptions in populations of critical care survivors. To explain these psychopathology elevations, some have proposed a direct effect of ICU admission upon the later development of psychopathology, whereas others highlight the complex interaction between the trauma of a life-threatening illness or injury and the stressful life-saving interventions often administered in the ICU. However, the conclusion that the ICU is an independent causal factor in trauma-related psychological outcomes may be premature. Current ICU research suffers from important methodological problems including lack of true prospective data, failure to employ appropriate comparison groups, sampling bias, measurement issues, and problems with statistical methodology. In addition, the ICU literature has yet to investigate important risk and resilience factors that have been empirically validated in the broader stress-response literature. The authors propose the application of these important constructs to the unique setting of the ICU. This review focuses on multiple aspects of the important but complex research question of whether the ICU confers risk for psychological distress above and beyond the traumatic impact of the serious health events that necessitate ICU treatment. (PsycINFO Database Record

A large number of studies have identified trajectories of adjustment following acute and aversive life events. In these studies, a stable trajectory of positive health or resilience is almost always the modal outcome (Bonanno, 2004; Bonanno et al., 2011). Infurna and Luthar (2016, this issue) reported that they replicated findings from two early studies in which trajectories of subjective well-being were identified before and after divorce, widowhood, and unemployment (Galatzer-Levy, Bonanno, & Mancini, 2010; Mancini, Bonanno, & Clark, 2011) and then reanalyzed these data in such a way to conclude a decrease in the prevalence of resilience. In this commentary, we discuss three serious flaws in Infurna and Luthar's claims. First, they did not actually replicate our original analyses. They used different data, time points, and parameters. Second, the model specifications in their reanalyses were not optimal because they increased variance, reduced variability in response to the stressor, and had lower entropy, indicating that their models more poorly captured unique patterns of response. Third, their reanalyses were theoretically uninformative as they minimized both group differences and overall responses to the stressor event and thus failed to identify widely acknowledged populations, such as chronic stress reactivity.

PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

Major depressive disorder (MDD) contributes to a significant worldwide disease burden, expected to be second only to heart disease by 2050. However, accurate diagnosis has been a historical weakness in clinical psychiatry. As a result, there is a demand for diagnostic modalities with greater objectivity that could improve on current psychiatric practice that relies mainly on self-reporting of symptoms and clinical interviews. Over the past two decades, literature on a growing number of putative biomarkers for MDD increasingly suggests that MDD patients have significantly different biological profiles compared to healthy controls. However, difficulty in elucidating their exact relationships within depression pathology renders individual markers inconsistent diagnostic tools. Consequently, further biomarker research could potentially improve our understanding of MDD pathophysiology as well as aid in interpreting response to treatment, narrow differential diagnoses, and help refine current MDD criteria. Representative of this, multiplex assays using multiple sources of biomarkers are reported to be more accurate options in comparison to individual markers that exhibit lower specificity and sensitivity, and are more prone to confounding factors. In the future, more sophisticated multiplex assays may hold promise for use in screening and diagnosing depression and determining clinical severity as an advance over relying solely on current subjective diagnostic criteria. A pervasive limitation in existing research is heterogeneity inherent in MDD studies, which impacts the validity of biomarker data. Additionally, small sample sizes of most studies limit statistical power. Yet, as the RDoC project evolves to decrease these limitations, and stronger studies with more generalizable data are developed, significant advances in the next decade are expected to yield important information in the development of MDD biomarkers for use in clinical settings.

Psychophysiological measures of fear expression provide observable intermediate phenotypes of fear-related symptoms. Research Domain Criteria (RDoC) advocate using neurobiological intermediate phenotypes that provide dimensional correlates of psychopathology. Negative Valence Systems in the RDoC matrix include the construct of acute threat, which can be measured on a physiological level using potentiation of the acoustic startle reflex assessed via electromyography recordings of the orbicularis oculi muscle. Impairments in extinction of fear-potentiated startle due to high levels of fear (termed fear load) during the early phases of extinction have been observed in posttraumatic stress disorder (PTSD). The goals of the current work were to examine dimensional associations between fear-related symptoms of PTSD and fear load variables to test their validity as an intermediate phenotype. We examined extinction of fear-potentiated startle in a cohort (n=269) of individuals with a broad range of civilian trauma exposure (range 0-13 traumatic events per person, mean=3.5). Based on previously reported findings, we hypothesized that fear load would be significantly associated with intrusion and fear memories of an index traumatic event. The results indicated that early extinction was correlated with intrusive thoughts (p=0.0007) and intense physiological reactions to trauma reminders (p=0.036). Degree of adult or childhood trauma exposure, and depression severity were not associated with fear load. After controlling for age, sex, race, income, level of prior trauma, and level of fear conditioning, fear load during extinction was still significantly predictive of intrusive thoughts (p=0.004). The significance of these findings is that they support dimensional associations with symptom severity rather than diagnostic category and, as such, fear load may emerge as a transdiagnostic intermediate phenotype expressed across fear-related disorders (e.g., specific phobia, social phobia).

Our understanding of how individuals react to the loss of a close loved one comes largely from studies of spousal bereavement. The extent to which findings are relevant to other bereavements is uncertain. A major methodological limitation of current studies has been a reliance on retrospective reporting of functioning and use of samples of individuals who have self-selected for participant in grief research. To address these limitations, in the current study we applied Latent Growth Mixture Modelling (LGMM) in a prospective population-based sample to identify trajectories of depression following spousal and child bereavement in later life. The sample consisted of 2512 individual bereaved adults who were assessed once before and three times after their loss. Four discrete trajectories were identified: Resilience (little or no depression; 68.2%), Chronic Grief (an onset of depression following loss; 13.2%), Depressed-Improved (high pre-loss depression that decreased following loss; 11.2%), and Pre-existing Chronic Depression (high depression at all assessments; 7.4%). These trajectories were present for both child and spousal loss. There was some evidence that child loss in later life was associated more strongly with the Chronic Grief trajectory and less strongly with the Resilience trajectory. However these differences disappeared when covariates were included in the model. Limitations of the analyses are discussed. These findings increase our understanding of the variety of outcomes following bereavement and underscore the importance of using prospective designs to map heterogeneity of response outcomes.

BACKGROUND: Pre-deployment identification of soldiers at risk for long-term posttraumatic stress psychopathology after home coming is important to guide decisions about deployment. Early post-deployment identification can direct early interventions to those in need and thereby prevents the development of chronic psychopathology. Both hold significant public health benefits given large numbers of deployed soldiers, but has so far not been achieved. Here, we aim to assess the potential for pre- and early post-deployment prediction of resilience or posttraumatic stress development in soldiers by application of machine learning (ML) methods. METHODS: ML feature selection and prediction algorithms were applied to a prospective cohort of 561 Danish soldiers deployed to Afghanistan in 2009 to identify unique risk indicators and forecast long-term posttraumatic stress responses. RESULTS: Robust pre- and early postdeployment risk indicators were identified, and included individual PTSD symptoms as well as total level of PTSD symptoms, previous trauma and treatment, negative emotions, and thought suppression. The predictive performance of these risk indicators combined was assessed by cross-validation. Together, these indicators forecasted long term posttraumatic stress responses with high accuracy (pre-deployment: AUC=0.84 (95% CI=0.81-0.87), post-deployment: AUC=0.88 (95% CI=0.85-0.91)). LIMITATIONS: This study utilized a previously collected data set and was therefore not designed to exhaust the potential of ML methods. Further, the study relied solely on self-reported measures. CONCLUSIONS: Pre-deployment and early post-deployment identification of risk for long-term posttraumatic psychopathology are feasible and could greatly reduce the public health costs of war.

OBJECTIVE: Prospectively identifying individuals at heightened risk for depression can alleviate the disease burden of distal physical and mental health consequences after cancer onset. Our objective was to identify heterogeneous trajectories of adjustment in cancer patients, using treatment-type as a predictor. METHODS: Participants were followed for 6 years within the Health and Retirement Study (HRS), a prospective population-based cohort study. The sample consisted of 1,294 middle-aged participants who were assessed once before and 3 time points after their report of an initial cancer diagnosis. In addition to self-reported depressive symptoms, subjects indicated receipt of surgical, radiological, or chemical interventions as part of their usual oncological care. RESULTS: Four symptom trajectories were identified with Latent Growth Mixture Modeling: an increasing depression (10.5%), chronic depression (8.0%), depressed-improved (7.8%), and stable-low depression (73.7%). A conditional model using participants with available predictor data (n = 545) showed individuals in the emerging depression class were significantly more likely to have received chemo/medication therapy when compared with the remitting depression, stable-low, and chronic depression classes. Participants in the chronic and depressed-improved classes generally had worse baseline health, and the depressed-improved were also younger in age. CONCLUSION: Patients who exhibited increasing depressive symptoms had a greater probability of receiving chemo/medication therapy than any other adjustment trajectory group, although the majority of chemotherapy patients did not exhibit depressive symptom changes. These data underscore the diversity of ways that patients adjust to cancer, and suggest cancer treatment, baseline health, and age may influence long-term patterns of psychological adjustment. (PsycINFO Database Record

Background Traumatic injuries affect millions of patients each year, and resulting post-traumatic stress disorder (PTSD) significantly contributes to subsequent impairment. Aims To map the distinctive long-term trajectories of PTSD responses over 6 years by using latent growth mixture modelling. Method Randomly selected injury patients (n = 1084) admitted to four hospitals around Australia were assessed in hospital, and at 3, 12, 24 and 72 months. Lifetime psychiatric history and current PTSD severity and funxctioning were assessed. Results Five trajectories of PTSD response were noted across the 6 years: (a) chronic (4%), (b) recovery (6%), (c) worsening/recovery (8%), (d) worsening (10%) and (e) resilient (73%). A poorer trajectory was predicted by female gender, recent life stressors, presence of mild traumatic brain injury and admission to intensive care unit. Conclusions These findings demonstrate the long-term PTSD effects that can occur following traumatic injury. The different trajectories highlight that monitoring a subset of patients over time is probably a more accurate means of identifying PTSD rather than relying on factors that can be assessed during hospital admission.