The growing literature conceptualizing mental disorders like posttraumatic stress disorder (PTSD) as networks of interacting symptoms faces three key challenges. Prior studies predominantly used (a) small samples with low power for precise estimation, (b) nonclinical samples, and (c) single samples. This renders network structures in clinical data, and the extent to which networks replicate across data sets, unknown. To overcome these limitations, the present cross-cultural multisite study estimated regularized partial correlation networks of 16 PTSD symptoms across four data sets of traumatized patients receiving treatment for PTSD (total N = 2,782). Despite differences in culture, trauma type, and severity of the samples, considerable similarities emerged, with moderate to high correlations between symptom profiles (0.43–0.82), network structures (0.62–0.74), and centrality estimates (0.63–0.75). We discuss the importance of future replicability efforts to improve clinical psychological science and provide code, model output, and correlation matrices to make the results of this article fully reproducible.

There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder’s salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ≥95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC=.77) did not differ from predicting from all available information (AUC=.78). Predicting from ASD symptoms was not better then chance (AUC =.60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC=.71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information.

The present study adds important knowledge about the development of psychological distress and pain after whiplash injury. The finding, that PCS and FA mediated the effect of PTSS on pain intensity is a novel finding with important implications for prevention and management of whiplash-associated disorders. WHAT DOES THIS STUDY ADD?: The study confirms the mechanisms as outlined in the fear-avoidance model and the mutual maintenance model. The study adds important knowledge of pain-catastrophizing and fear-avoidance beliefs as mediating mechanisms in the effect of post-traumatic stress on pain intensity. Hence, cognitive behavioural techniques targeting avoidance behaviour and catastrophizing may be beneficial preventing the development of chronic pain.

The long-term course of readjustment problems in military personnel has not been evaluated in a nationally representative sample. The National Vietnam Veterans Longitudinal Study (NVVLS) is a congressionally mandated assessment of Vietnam veterans who underwent previous assessment in the National Vietnam Veterans Readjustment Study (NVVRS). OBJECTIVE To determine the prevalence, course, and comorbidities of war-zone posttraumatic stress disorder (PTSD) across a 25-year interval. DESIGN, SETTING, AND PARTICIPANTS The NVVLS survey consisted of a self-report health questionnaire (n = 1409), a computer-assisted telephone survey health interview (n = 1279), and a telephone clinical interview (n = 400) in a representative national sample of veterans who served in the Vietnam theater of operations (theater veterans) from

In conclusion, a significant proportion of individuals report high levels of dissociation alongside their PTSD, which constitutes a dissociative-PTSD subtype. Further investigation is needed to identify which factors may increase or decrease the likelihood of membership in a dissociative-PTSD subtype group compared to a severe PTSD only group.

While longitudinal posttraumatic stress responses are known to be heterogeneous, little is known about predictors of those responses. We investigated if locus of control (LOC) and coping style are associated with long-term PTSD-trajectories after exposure to combat. Six hundred and seventy five Israeli soldiers with or without combat stress reaction (CSR) from the Lebanon war were assessed 1, 2, and 20 years after the war. Combat exposure, LOC, and coping style were then investigated as covariates of the trajectories of resilience, recovery, delayed onset, and chronicity. Symptomatic trajectories in the CSR and the non-CSR group were significantly associated to varying degrees with perceived life threat during combat (ORs: 1.76-2.53), internal LOC (0.77-0.87), emotional coping style (0.28-0.34), and low use of problem-focused coping (2.12-3.11). In conclusion, assessment of LOC and coping can aid prediction of chronic PTSD outcomes of combat exposure.

The results confirm earlier findings of stress response heterogeneity following military deployment and highlight the impact of predeployment, perideployment, and postdeployment risk factors in predicting PTSD symptomatology and late-onset PTSD symptoms.

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