Client preferences, therapy activities and preference-activity match as predictors of therapy outcome.

OBJECTIVE: This study investigated whether distinct types of psychotherapy activities, the client's preference towards these activities prior to therapy, and the degree of match between client preferences and therapy activities, served as predictors of treatment outcomes. METHODS: A total of 621 clients (Mage = 42 years, 71.7% female) received individual psychotherapy by 54 psychologists. Associations between activity preferences, therapy activities, and preference-activity match as predictors, and symptom change and treatment dropout as outcomes were analyzed using multilevel longitudinal and logistic modelling and polynomial regression models with response surface analysis. RESULTS: No type of therapy activity or activity preference significantly predicted symptom change in therapy, while higher levels of inward orientation therapy activities predicted an increased risk of dropout. Moreover, matching and higher levels of inward orientation and affect expression activities predicted an increased risk of dropout, and matching and higher levels of outward orientation activities predicted a decreased risk of dropout. Finally, a preference-activity mismatch in affect suppression predicted an increased risk of dropout from therapy, both at higher and lower levels of affect suppression. CONCLUSION: Distinct types of therapy activity preferences may, especially when (mis)matched with similar levels of the same therapy activities, differentially predict particular dropout from therapy.Trial registration: ClinicalTrials.gov identifier: NCT05630560.

Stressful life events exhibit complex patterns of associations with depressive symptoms in two population-based samples using network analysis.

BACKGROUND: Stressful life events (SLEs) constitute key risk factors for depression. However, previous studies examining associations between SLEs and depression have been limited by focusing on single events, combining events into broad categories, and/or ignoring interrelationships between events in statistical analyses. Network analysis comprises a set of statistical methods well-suited for assessing relationships between multiple variables and can help surpass several limitations of previous studies. METHODS: We applied network analysis using mixed graphical models combining two large-scale population-based samples and >34,600 randomly sampled adults to investigate the associations between SLEs and current depressive symptoms in the general population. RESULTS: Numerous SLEs were uniquely associated with specific symptoms. Strong pairwise links were observed between SLEs during the past year and individual symptoms, e.g., between having experienced illness or injury and sleeping problems, having been degraded or humiliated and feeling blue, and between financial problems and hopelessness and being worried and anxious. Several SLEs, such as financial problems, sexual abuse, and having been degraded or humiliated, were associated with symptoms across more than one timepoint. More recent SLEs were generally more strongly associated with depressive symptoms. Several life events were strongly interrelated, such as multiple forms of abuse, and financial problems, unemployment, divorce, and serious illness or injury. LIMITATIONS: Limitations include a retrospective SLE measure, cross-sectional data, a brief self-report measure of depressive symptoms, and possible attrition bias in the sample. CONCLUSIONS: Our findings may have implications for public health efforts seeking to improve population mental health.

Prediction models of suicide and non-fatal suicide attempt after discharge from a psychiatric inpatient stay: A machine learning approach on nationwide Danish registers.

INTRODUCTION: To develop machine learning models capable of predicting suicide and non-fatal suicide attempt as separate outcomes in the first 30 days after discharge from a psychiatric inpatient stay. METHODS: Prospective cohort study using nationwide Danish registry data. We included individuals who were 18 years or older, and all discharges from psychiatric hospitalizations in Denmark from 1995 to 2018. We trained predictive models using 10-fold cross validation on 80% of the data and did testing on the remaining 20%. RESULTS: The best model for predicting non-fatal suicide attempt was an ensemble of predictions from gradient boosting (XGBoost) and categorical boosting (catBoost). The ROC-AUC for predicting suicide attempt was 0.85 (95% CI: 0.84-0.85). At a risk threshold of 4.36%, positive predictive value (PPV) was 11.0% and sensitivity was 47.2%. The best model for predicting suicide was an ensemble of predictions from random forest, XGBoost and catBoost. For suicide, the ROC-AUC was 0.71 (95% CI: 0.70-0.73). At a risk threshold of 0.15%, PPV was 0.34% and sensitivity was 56.0%. The most contributing predictors differed when predicting suicide and suicide attempt, indicating that separate models are needed. The ensemble model was fair across sex and age, and more so than the penalized logistic regression model. CONCLUSIONS: We achieved good performance for predicting suicide attempts and demonstrated a clinical application of ensemble models. Our results indicate a difference in predictive performance for models predicting suicide and suicide attempt, respectively. Thus, we recommend that suicide and suicide attempt are treated as two separate endpoints, in particular for clinical application. We demonstrated that the ensemble model is fairer across sex and age compared with a penalized logistic regression, and therefore we recommend the use of well-tested ensembles despite a more complex explainability.

Risk factors, comorbidity and social impairment of ICD-11 PTSD and complex PTSD in Danish treatment-seeking military veterans.

PURPOSE: While a number of studies have investigated risk factors and comorbidities of ICD-11 post-traumatic stress disorder (PTSD) and complex PTSD (CPTSD) in various trauma exposed samples, few studies have been conducted in military samples. Existing studies with military samples have included rather small samples. The aim of the present study was to identify risk factors and comorbidities of ICD-11 PTSD and CPTSD in a large sample of previously deployed, treatment-seeking soldiers and veterans. METHODS: Previously deployed, treatment-seeking Danish soldiers and veterans (N = 599), recruited from the Military Psychology Department of the Danish Defense, completed the International Trauma Questionnaire (ITQ), as well as questionnaires of common mental health difficulties, trauma exposure, functioning and demographics. Multivariate multinomial logistic regression analysis explored differences in self-reported exposure to adversity and health outcomes between those meeting ICD-11 criteria for probable PTSD, CPTSD and no trauma disorder. RESULTS: A total of 13.0% met probable ICD-11 criteria for PTSD and 31.4% for CPTSD. Risk factors for CPTSD (compared to those with no trauma disorder) included exposure to warfare or combat, longer duration since the traumatic event and being single. Those with CPTSD were more likely than those with PTSD or no trauma disorder to endorse symptoms of depression, anxiety, stress, use of psychotropic medication, and suicide attempts. CONCLUSION: CPTSD is a more common and debilitating condition compared to PTSD in treatment-seeking soldiers and veterans. Further research should focus on testing existing and novel interventions for CPTSD in the military.

Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models.

Military personnel deployed to war zones are at increased risk of mental health problems such as posttraumatic stress disorder (PTSD) or depression. Early pre- or post-deployment identification of those at highest risk of such problems is crucial to target intervention to those in need. However, sufficiently accurate models predicting objectively assessed mental health outcomes have not been put forward. In a sample consisting of all Danish military personnel who deployed to war zones for the first (N = 27,594), second (N = 11,083) and third (N = 5,161) time between 1992 and 2013, we apply neural networks to predict psychiatric diagnoses or use of psychotropic medicine in the years following deployment. Models are based on pre-deployment registry data alone or on pre-deployment registry data in combination with post-deployment questionnaire data on deployment experiences or early post-deployment reactions. Further, we identified the most central predictors of importance for the first, second, and third deployment. Models based on pre-deployment registry data alone had lower accuracy (AUCs ranging from 0.61 (third deployment) to 0.67 (first deployment)) than models including pre- and post-deployment data (AUCs ranging from 0.70 (third deployment) to 0.74 (first deployment)). Age at deployment, deployment year and previous physical trauma were important across deployments. Post-deployment predictors varied across deployments but included deployment exposures as well as early post-deployment symptoms. The results suggest that neural network models combining pre- and early post-deployment data can be utilized for screening tools that identify individuals at risk of severe mental health problems in the years following military deployment.

What we talk about when we talk about trauma: Content overlap and heterogeneity in the assessment of trauma exposure.

The accurate definition and assessment of trauma exposure is the foundation for replicable studies of mental health problems following trauma exposure. However, scales developed to assess trauma exposure might vary widely in terms of item content; overlap; and specifications of trauma intensity, frequency, duration, and timing. We compared eight frequently used self-report measures of trauma exposure to address content overlap and measurement heterogeneity. Combined, these measures assess 44 disparate exposures. Mean overlap across scales was moderate (M = 0.41, range: 0.25-0.48 across scales). Pairwise overlap between scales ranged from .19 to .59. We found 18 exposures (40.9%) that were included in one scale and three exposures (6.8%) that were included in all eight scales. Four of the included scales assess trauma frequency, five assess intensity or perceived danger, two assess duration, and four assess timing. The implications of measurement heterogeneity for clinical research as well as for comparability and replication of trauma-related research are discussed.

Resting-state EEG functional connectivity predicts post-traumatic stress disorder subtypes in veterans.

Objective. Post-traumatic stress disorder (PTSD) is highly heterogeneous, and identification of quantifiable biomarkers that could pave the way for targeted treatment remains a challenge. Most previous electroencephalography (EEG) studies on PTSD have been limited to specific handpicked features, and their findings have been highly variable and inconsistent. Therefore, to disentangle the role of promising EEG biomarkers, we developed a machine learning framework to investigate a wide range of commonly used EEG biomarkers in order to identify which features or combinations of features are capable of characterizing PTSD and potential subtypes.Approach. We recorded 5 min of eyes-closed and 5 min of eyes-open resting-state EEG from 202 combat-exposed veterans (53% with probable PTSD and 47% combat-exposed controls). Multiple spectral, temporal, and connectivity features were computed and logistic regression, random forest, and support vector machines with feature selection methods were employed to classify PTSD. To obtain robust results, we performed repeated two-layer cross-validation to test on an entirely unseen test set.Main results. Our classifiers obtained a balanced test accuracy of up to 62.9% for predicting PTSD patients. In addition, we identified two subtypes within PTSD: one where EEG patterns were similar to those of the combat-exposed controls, and another that were characterized by increased global functional connectivity. Our classifier obtained a balanced test accuracy of 79.4% when classifying this PTSD subtype from controls, a clear improvement compared to predicting the whole PTSD group. Interestingly, alpha connectivity in the dorsal and ventral attention network was particularly important for the prediction, and these connections were positively correlated with arousal symptom scores, a central symptom cluster of PTSD.Significance. Taken together, the novel framework presented here demonstrates how unsupervised subtyping can delineate heterogeneity and improve machine learning prediction of PTSD, and may pave the way for better identification of quantifiable biomarkers.

Trajectories of disability in low back pain.

INTRODUCTION: Low back pain (LBP) is the leading course of years lived with disability. Unfortunately, not much knowledge exists about distinct trajectories of recovery from disability after LBP and their potential psychological predictors. OBJECTIVES: Hence, the aim of the present study was to identify trajectories of functional disability in LBP and their potential baseline psychological predictors. METHODS: A 1-year consecutive cohort (N = 1048) of patients with LBP referred to the Spine Centre if they have not improved satisfactorily from a course of treatment in primary care after 1 to 2 months were assessed by self-report questionnaires at their first visit and at 6- and 12-month follow-up. Data from patients who responded to the Roland Morris Disability Questionnaire at least twice (N = 747) were used to assess trajectories of functional disability by Latent Growth Mixture Modeling. The following measures were used as baseline predictors of the trajectories: Pain Intensity Numerical Rating Scales, Pain Catastrophizing Scale, Tampa Scale for Kinesiophobia, and Hospital Anxiety and Depression Scale. RESULTS: Four distinct trajectories were identified: high-stable (22.0%), high-decreasing (20.4%), medium-stable (29.7%), and low-decreasing (27.9%). Using the low-decreasing trajectory as reference, baseline pain intensity, depressive symptoms, and pain-catastrophizing predicted membership of all 3 symptomatic trajectories. However, using the high-decreasing trajectory as reference, age, baseline pain intensity, and depression were predictors of the high-stable trajectory. CONCLUSION: In particular, the finding of a high-stable trajectory characterized by high levels of baseline psychological distress is of potential clinical importance because psychological distress may be targeted by cognitive behavioral therapeutic approaches.

Changes in perceived social support and PTSD symptomatology among Danish army military personnel.

PURPOSE: Previous research has identified social support to be associated with risk of posttraumatic stress disorder (PTSD) symptoms among military personnel. While the lack of social support influences PTSD symptomatology, it is unknown how changes in perceived social support affect the PTSD symptom level in the aftermath of deployment. Furthermore, the influence of specific sources of social support from pre- to post-deployment on level of PTSD symptoms is unknown. We aim to examine how changes in perceived social support (overall and from specific sources) from pre- to 2.5 year post-deployment are associated with the level of post-deployment PTSD symptoms. METHODS: Danish army military personnel deployed to Afghanistan in 2009 and 2013 completed questionnaires at pre-deployment and at 2.5 year post-deployment measuring perceived social support and PTSD symptomatology and sample characteristics of the two cohorts. Data were analyzed using univariate and multivariate nominal logistic regression. RESULTS: Negative changes in perceived social support from pre- to post-deployment were associated with both moderate (OR 1.99, CI 1.51-2.57) and high levels (OR 2.71, CI 1.94-3.78) of PTSD symptoms 2.5 year post-deployment (adjusted analysis). Broadly, the same direction was found for specific sources of social support and level of PTSD symptoms. In the adjusted analyses, pre-deployment perceived social support and military rank moderated the associations. CONCLUSIONS: Deterioration in perceived social support (overall and specific sources) from pre- to 2.5 year post-deployment increases the risk of an elevated level of PTSD symptoms 2.5 year post-deployment.

PTSD and complex PTSD in treatment-seeking Danish soldiers: a replication of Folke et al. (2019) using the International Trauma Questionnaire.

Background: While empirical support for the ICD-11 distinction between posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD) is growing, empirical research into the ICD-11 model of CPTSD in military populations is scarce and inconsistent. Objective: To replicate a study from our own group identifying distinct classes based on CPTSD symptoms using the International Trauma Questionnaire (ITQ) and to identify predictors and functional outcomes associated with a potential distinction between PTSD and CPTSD. Method: Formerly deployed treatment-seeking Danish soldiers (N = 294) completed the ITQ and self-report measures of traumatic life events prior to treatment. Latent profile analysis (LPA) was used to extract classes based on CPTSD symptoms. Results: LPA revealed four classes; (1) high CPTSD symptoms ('CPTSD', 28.7%); (2) high PTSD symptoms and lower DSO symptoms ('PTSD', 23.5%); (3) high DSO symptoms ('DSO', 17.3%); and (4) low symptoms ('Low Symptoms', 30.5%). In comparison to the PTSD-class, CPTSD-class membership was not predicted by traumatic events in adult life and in childhood. The CPTSD class was more often single/divorced/widowed compared to the PTSD class. Moreover, the CPTSD class more often used psychotropic medicine compared to the DSO-class and Low Symptoms-class. Conclusion: Using the ITQ, this study yields empirical support for the ICD-11 model of CPTSD within a clinical sample of veterans. The results replicate findings from our previous study that also identified distinct profiles of ICD-11 PTSD and CPTSD.

Antecedentes: Si bien, el soporte empírico de la clasificación de la CIE-11 para la distinción entre el trastorno de estrés postraumático (TEPT) y TEPT complejo (TEPTC) está creciendo, la investigación empírica sobre el modelo del TEPTC según la CIE-11 en poblaciones militares es escasa e inconsistente.Objetivo: Replicar un estudio de nuestro propio grupo identificando distintas clases basadas en los síntomas del TEPTC utilizando el Cuestionario Internacional de Trauma (ITQ por sus siglas en inglés) e identificar los predictores y las consecuencias funcionales asociadas a una posible distinción entre TEPT y TEPTC.Método: Los soldados daneses que estuvieron en despliegue y en búsqueda de tratamiento (N= 294) completaron el cuestionario de la ITQ y medidas de auto-reporte en relación a eventos traumáticos a lo largo de la vida antes del tratamiento. Se utilizó el análisis de perfil latente (APL) para extraer clases basadas en los síntomas del TEPTC.Resultados: El APL reveló cuatro clases; (1) síntomas elevados de TEPTC ('TEPTC', 28.7%); (2) síntomas elevados de TEPT y síntomas más bajos de Alteraciones en la Auto-Organización (DSO por sus siglas en inglés) ('TEPT', 23.5%); (3) síntomas elevados de DSO ('DSO', 17.3%); y (4) síntomas bajos ('Síntomas bajos', 30.5%). En comparación con la clase de TEPT, la afiliación a la clase del TEPTC no estuvo predicha por eventos traumáticos en la adultez y en la infancia. La clase TEPTC era más frecuentemente soltero/divorciado/viudo, en comparación con la clase TEPT. Además, la clase TEPTC utilizó con mayor frecuencia medicamentos psicotrópicos en comparación con la clase DSO y la clase de Síntomas bajos.Conclusiones: Utilizando el cuestionario ITQ, este estudio proporciona apoyo empírico para el modelo de TEPTC de la CIE-11 dentro de una muestra clínica de veteranos. Los resultados replican los hallazgos de nuestro estudio anterior, que tambien identificó distintos perfiles de TEPT y TEPTC según la clasificación de la CIE-11.

Forecasting military mental health in a complete sample of Danish military personnel deployed between 1992-2013.

OBJECTIVE: Mental health problems (MHP) are a relatively common consequence of deployment to war zones. Early identification of those at risk of post-deployment MHP would improve prevention efforts. However, screening instruments based on linear models have not been successful. Machine learning (ML) has shown promise for providing the methodological frame for better prognostic models. METHODS: The study population was all Danish military personnel deployed for the first time between January 1, 1992 and December 31, 2013. From extensive registry data, 21 pre- or at-deployment predictors comprising early adversity, social, clinical and demographic variables were used to predict psychiatric contacts (psychiatric diagnosis and/or use of psychotropic medicine) occurring within 6.5 years after homecoming. Four supervised ML methods (penalized logistic regression, random forests, support vector machines and gradient boosting machines) were compared in ability to classify those with high risk of post-deployment MHP and those without. RESULTS: Of 27594 subjects, 2175 (8%) had a psychiatric contact. All four ML methods applied had performances well above chance (Area under the Receiver-operating Curve 0.62-0.68). Positive predictive value for the best model was 0.16. A range of pre-deployment factors were found to be predictive of post-deployment psychiatric contacts. CONCLUSIONS: ML methods can be useful in early identification of soldiers with high risk of MPH in the years following their first deployment. However, performances were modest and positive predictive values were low, limiting the applicability of the models for pre-deployment screening. Future studies should include neurobiological data and deployment experiences to increase accuracy of the models.

Measuring Social Support among Soldiers with the Experienced Post-Deployment Social Support Scale (EPSSS): A Rasch-Based Construct Validity Study.

Perceived social support following deployment is a known buffer against post-deployment adverse mental health outcomes. Given contextual sensitivity of social support measures, scales that address specific social support needs of soldiers in the first months after home coming should be developed and validated. In a sample of 553 soldiers deployed to Afghanistan at two different time points (2009 and 2013, respectively), we selected items for and tested the construct validity of an 8-item measure of experienced post-deployment social support (experienced post-deployment social support scale; EPSSS). Within the item response theory framework, we used Rasch models (RM) to conduct item analysis with an emphasis on testing for differential item functioning (DIF) across background variables such as previous deployments and cohort. In short, we found that the scale did not fit the Rasch model, but with exclusion of two items, a 6-item version of the scale did fit an extended graphical loglinear Rasch model (GLLRM) with only one instance of DIF, for which the score can be adjusted. We also demonstrated that when applied as a scale, the DIF will not affect the results substantially. Hence, we conclude that the constructed 6-item EPSSS can be validly applied without score correction to assess the level of social support in Danish soldiers after home coming.

Deployment experiences and mental health problems as predictors of post-deployment unemployment length: a prospective, register-based study among Danish soldiers.

OBJECTIVES: To test responses of formerly deployed soldiers (FDS) to a questionnaire on deployment experiences in combination with screening levels of post-traumatic stress disorder (PTSD) and depression approximately 6 months after homecoming as predictors of the subsequent probability of gaining employment when unemployed within 5 years post-deployment. DESIGN, SETTING AND PARTICIPANTS: Danish FDS responders (n=3935) and non-responders (n=3046) to a 6-month post-deployment screening questionnaire after returning from a first-ever deployment to Kosovo, Iraq or Afghanistan (2002 to 2012) were included in the study and followed in public registers from 6 months to 5 years post-deployment. PRIMARY AND SECONDARY OUTCOME MEASURES: We tested Cox regression models including deployment experiences (1a), screening levels of PTSD and depressive symptoms (1b), and their combination (2) for FDS responders. For all FDS, a secondary model included a measure on whether they responded to the questionnaire (3). RESULTS: Neither the deployment experiences (1a) of exposure to danger and combat (HR=1.00, 95% CI=0.97 to 1.03) and witnessing consequences of war (HR=1.01, 95% CI=0.96 to 1.06), or the screening levels (1b) of PTSD (HR=1.06, 95% CI=0.84 to 1.33) and depressive symptoms (HR=0.82, 95% CI=0.64 to 1.06) were significant predictors of transitioning from unemployment to employment. Similar results were found for the combined model (2). A tendency among non-respondents (3) to have a lower probability of transitioning from unemployment to employment was found (HR=0.90, 95% CI=0.81 to 1.00). CONCLUSION: Deployment experiences, PTSD and depressive symptoms, as measured at 6-month screening questionnaire, did not predict differences in the probability of gaining employment when unemployed within 5 years post-deployment. However, the findings suggest that those with the least probability of transitioning from unemployment to employment can be found among the non-responders to the post-deployment screening questionnaire.

Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning.

BACKGROUND: Posttraumatic stress disorder (PTSD) is a relatively common consequence of deployment to war zones. Early postdeployment screening with the aim of identifying those at risk for PTSD in the years following deployment will help deliver interventions to those in need but have so far proved unsuccessful. OBJECTIVE: This study aimed to test the applicability of automated model selection and the ability of automated machine learning prediction models to transfer across cohorts and predict screening-level PTSD 2.5 years and 6.5 years after deployment. METHODS: Automated machine learning was applied to data routinely collected 6-8 months after return from deployment from 3 different cohorts of Danish soldiers deployed to Afghanistan in 2009 (cohort 1, N=287 or N=261 depending on the timing of the outcome assessment), 2010 (cohort 2, N=352), and 2013 (cohort 3, N=232). RESULTS: Models transferred well between cohorts. For screening-level PTSD 2.5 and 6.5 years after deployment, random forest models provided the highest accuracy as measured by area under the receiver operating characteristic curve (AUC): 2.5 years, AUC=0.77, 95% CI 0.71-0.83; 6.5 years, AUC=0.78, 95% CI 0.73-0.83. Linear models performed equally well. Military rank, hyperarousal symptoms, and total level of PTSD symptoms were highly predictive. CONCLUSIONS: Automated machine learning provided validated models that can be readily implemented in future deployment cohorts in the Danish Defense with the aim of targeting postdeployment support interventions to those at highest risk for developing PTSD, provided the cohorts are deployed on similar missions.

Trajectories of depression symptoms from pre- to post- deployment: Does previous trauma predict symptom increase?

BACKGROUND: A significant minority of individuals experience depression following military deployment. The course of depression symptoms varies over time and across individuals; several factors including combat exposure influence depressions incidence and course. Importantly, previous trauma, especially in childhood, have been found increase the risk of post-deployment depression. METHODS: In a prospective sample of 530 soldiers deployed to Afghanistan in 2009, we used latent growth mixture modeling (LGMM) to estimate trajectories of depression symptoms from before through 6.5 years after deployment. In a multinomial logistic regression model, we tested if childhood and adult life trauma predicted trajectory membership in combination with combat exposure and neuroticism. RESULTS: We identified a large trajectory of few depression symptoms from before through 6.5 years after deployment (Low-stable, 86.5%), a trajectory with somewhat elevated symptoms (Medium-fluctuating, 4.0%), and a trajectory with few symptoms before deployment and a steep increase to a severe symptom level 6.5 years after deployment (Low-increasing, 9.4%). The Low-increasing trajectory was predicted by lower rank and childhood trauma, while the Medium-fluctuating trajectory was predicted by neuroticism, adult life trauma, and post-deployment PTSD symptoms. LIMITATIONS: Attrition and use of self-report measures for depression and trauma. CONCLUSIONS: Depression symptoms follow a heterogeneous course from before through 6.5 years after deployment with 9.4% experiencing symptom increase, resulting in severe symptoms 6.5 years after deployment. Trajectories are differentially predicted by rank, childhood and adult life trauma as well as neuroticism and PTSD symptoms, illustrating the clinical importance of taking individual differences of symptom course into account.

ICD-11 PTSD and complex PTSD in treatment-seeking Danish veterans: a latent profile analysis.

Background: The WHO International Classification of Diseases, 11th version (ICD-11), includes a trauma-related diagnosis of complex posttraumatic stress disorder (CPTSD) distinct from posttraumatic stress disorder (PTSD). Results from previous studies support the validity of this distinction. However, no studies to date have evaluated the ICD-11 model of PTSD and CPTSD in treatment-seeking military veterans. Objective: To determine if the distribution of symptoms in treatment-seeking Danish veterans was consistent with the ICD-11 PTSD and CPTSD symptom profiles. Based on previous studies, we hypothesized that separate classes representing PTSD and CPTSD would be found that membership of a potential CPTSD-class would be predicted by a larger number of childhood traumas, and that a potential distinction between PTSD and CPTSD would be supported by differences in sociodemographic and functional outcomes. Method: Participants (N = 1,541) were formerly deployed Danish soldiers who completed proxy measures of ICD-11 PTSD and disturbances in self-organization (DSO) symptoms, along with self-report measures of traumatic life events, prior to starting treatment at the Military Psychology Department of the Danish Defence. Results: All hypotheses were supported. Latent profile analysis (LPA) revealed separate classes representing PTSD and CPTSD. In comparison to the PTSD-class, membership of the CPTSD-class was predicted by more childhood traumatic experiences, and members of this class were more likely being single/divorced/widowed and more likely to use psychotropic medication. Besides a PTSD-class and a CPTSD-class, LPA revealed a Low Symptoms-class, a Moderate DSO-class, a Hyperarousal-class, and a High DSO-class, with clear differences in functional outcomes between classes. Conclusion: Findings replicate previous studies supporting the distinction between ICD-11 PTSD and CPTSD. In addition, there seem to be groups of treatment-seeking military veterans that do not fulfil full criteria for a trauma-related disorder. Further research should explore subsyndromal PTSD and CPTSD profiles in veterans and other populations.

Antecedentes: La Clasificación Internacional de Enfermedades de la OMS, 11ª versión (CIE-11), incluye un diagnóstico relacionado con el trauma para el trastorno de estrés postraumático complejo (TEPT-C) distinto del trastorno de estrés postraumático (TEPT). Los resultados de los estudios anteriores respaldan la validez de esta distinción. Sin embargo, ningún estudio hasta la fecha ha evaluado el modelo CIE-11 del TEPT y TEPT-C en veteranos militares en busca de tratamiento.Objetivo: Determinar si la distribución de los síntomas en los veteranos daneses en busca tratamiento fue consistente con los perfiles de los síntomas del TEPT y TEPT-C de la CIE-11. Basados en estudios previos, planteamos la hipótesis de que se encontrarían clases separadas que representan el TEPT y TEPT-C, que la pertenencia a la clase potencial del TEPT-C sería predicha por un mayor número de traumas infantiles, y que una posible distinción entre el TEPT y TEPT-C sería apoyada por las diferencias en los resultados sociodemográficos y funcionales.Método: Los participantes (N = 1,541) fueron soldados daneses desplegados anteriormente, que completaron medidas indirectas del TEPT CIE-11 y la alteración en los síntomas de autoorganización (DSO en su sigla en inglés), junto con medidas de autoinforme de los eventos traumáticos de la vida, antes de comenzar el tratamiento en el Departamento de Psicología Militar de la Defensa Danesa.Resultados: Todas las hipótesis fueron sustentadas. El análisis de perfil latente (APL) reveló clases separadas que representan el TEPT y TEPT-C. En comparación con la clase del TEPT, la pertenencia a la clase del TEPT-C fue predicha por más experiencias traumáticas infantiles, y los miembros de esta clase eran más propensos a ser solteros/divorciados/viudos y más propensos a usar medicamentos psicotrópicos. Además de una clase del TEPT y una clase del TEPT-C, el APL reveló una clase de Síntomas Bajos, una clase de DSO Moderado, una clase de Hiperactivación y una clase de DSO Alto, con claras diferencias en los resultados funcionales entre las clases.Conclusión: Los resultados replican los estudios previos que respaldan la distinción entre el TEPT y TEPT-C del CIE-11. Adicionalmente, parece haber grupos de veteranos militares en busca de tratamiento que no cumplen con los criterios completos para un trastorno relacionado con el trauma. Los estudios futuros deben explorar los perfiles del TEPT y TEPT-C sub-sindrómico en veteranos y otras poblaciones.

Post-traumatic stress following military deployment: Genetic associations and cross-disorder genetic correlations.

BACKGROUND: Post-traumatic stress disorder (PTSD) is a complex psychiatric disorder that occurs with relatively high frequency after deployment to warzones (∼10%). While twin studies have estimated the heritability to be up to 40%, thus indicating a considerable genetic component in the etiology, the biological mechanisms underlying risk and development of PTSD remain unknown. METHODS: Here, we conduct a genome-wide association study (GWAS; N = 2,481) to identify genome regions that associate with PTSD in a highly homogenous, trauma-exposed sample of Danish soldiers deployed to war and conflict zones. We perform integrated analyses of our results with gene-expression and chromatin-contact datasets to prioritized genes. We also leverage on other large GWAS (N>300,000) to investigate genetic correlations between PTSD and other psychiatric disorders and traits. RESULTS: We discover, but do not replicate, one region, 4q31, close to the IL15 gene, which is genome-wide significantly associated with PTSD. We demonstrate that gene-set enrichment, polygenic risk score and genetic correlation analyses show consistent and significant genetic correlations between PTSD and depression, insomnia and schizophrenia. LIMITATIONS: The limited sample size, the lack of replication, and the PTSD case definition by questionnaire are limitations to the study. CONCLUSIONS: Our results suggest that genetic perturbations of inflammatory response may contribute to the risk of PTSD. In addition, shared genetic components contribute to observed correlations between PTSD and depression, insomnia and schizophrenia.

Low-level cognitive ability in young adulthood and other risk factors of depression in an observational cohort study among deployed Danish soldiers.

PURPOSE: Evidence exists of an association between pre-morbid lower cognitive ability and higher risk of hospitalization for depressive disorder in civilian cohorts. The purpose of this study was to examine the relationship of cognitive ability at conscription with post-deployment depression and the influence of (1) baseline factors: age, gender, and pre-deployment educational level, (2) deployment-related factors: e.g., war-zone stress and social support, and (3) co-morbid PTSD. METHODS: An observational cohort study linking conscription board registry data with post-deployment self-report data. The study population consisted of Danish Army military personnel deployed to different war zones from 1997 to 2015. The association between cognitive ability at conscription and post-deployment depression was analyzed using repeated-measure logistic regression models. RESULTS: Study population totaled 9716 with a total of 13,371 deployments. Low-level cognitive ability at conscription was found to be weakly associated with post-deployment probable depression after adjustment for more important risk factors like gender, education, and deployment-related factors [odds ratio (OR) 0.93, 95% confidence interval (CI) 0.88-0.99]. The co-occurrence rate with PTSD was nearly 60%. When adding co-morbid PTSD as an independent variable, the association between cognitive ability and probable depression became insignificant, OR 0.95, CI 0.89-1.02. CONCLUSIONS: Low cognitive ability at conscription is a risk factor for depression among returning military personnel, but unimportant compared to gender, education, and deployment-related factors. Part of this effect may be related to co-morbid PTSD. Use of cognitive ability score as an isolated selection tool cannot be recommended because of low predictive performance.

Trajectories of posttraumatic stress symptoms after whiplash: A prospective cohort study.

BACKGROUND: Posttraumatic stress disorder (PTSD) symptoms are highly prevalent after whiplash and associated with pain-related symptoms. While mutual maintenance between pain and PTSD has been suggested, knowledge on individual differences in the course of these symptoms is needed. The present study aimed to identify trajectories of PTSD symptoms following whiplash and test predictors and functional outcomes of such trajectories. METHODS: In a prospective cohort design with assessments at baseline (<4 weeks), 3 months, and 6 months post-injury (n = 229, whiplash grade I-III), we identified PTSD-trajectories using Latent Growth Mixture Modeling. Predictors (pain, fear-avoidance-beliefs, pain-catastrophizing, depression, age, and gender) were tested using multinomial logistic regression, and group mean differences in physical and psychosocial pain-related disability at 6 months were tested as outcomes after controlling for baseline levels. RESULTS: Three trajectories were identified: "Resilient" (75.1%) with little or no PTSD symptoms over time, "Recovering" (10.0%) with high initial PTSD symptom levels, then decreasing substantially, and "Chronic" (14.9%) with high initial PTSD symptom levels and a small increase over time. Initial higher pain and depression levels predicted the recovering and chronic trajectories, while the latter had more pain-related disability at 6 months compared to both other trajectories. CONCLUSIONS: Three trajectories were identified, with the chronic trajectory suggesting that a significant subset of people does not recover from PTSD symptoms. This class also reported more pain-related disability. Pain and depression predicted membership, but did, however, not succeed in differentiating between the two high-starting trajectories, suggesting that targeting PTSD symptoms may be important to ensure recovery. SIGNIFICANCE: Distinct recovery patterns after whiplash were identified with a significant subgroup reporting elevated and slightly increasing PTSD symptoms over time, highlighting both recovery variability and the presence of PTSD symptoms in a significant subgroup of individuals with whiplash. This subgroup also displayed enhanced pain-related disability over time compared to the recovering and resilient subgroups, thereby linking PTSD symptoms to functional pain outcomes over time. These findings suggest that clinicians should be attentive of potential PTSD symptoms in whiplash patients.

Perceived danger during deployment: a Rasch validation of an instrument assessing perceived combat exposure and the witnessing of combat consequences in a war zone.

The potential stressors associated with military deployment are related to an increased risk of adverse mental health outcomes. Perceived exposure to combat has been found to be proportional to the severity of post-deployment posttraumatic stress disorder (PTSD). However, other perceived adversities during deployment, such as witnessing danger, distress, and hardship in the war zone, have been less systematically studied, but might play an equally substantial role for post-deployment mental health. The development and validation of scales that assess these related constructs are needed to distinguish their contribution to post-deployment risk of PTSD. We evaluated the validity of 10 items measuring perceived danger distributed to all deployed personnel with the Danish Defense since 1998. We hypothesize two scales: Exposure to Danger and Combat (EDC) and Witnessing Consequences of War (WCW). Two military cohorts deployed to Afghanistan in 2009 (Cohort 1, N = 276) and 2013 (Cohort 2, N = 273) were included. Questionnaire data was collected six months after homecoming, including deployment experiences and post-deployment reactions. We tested the construct validity of the 10 items of perceived danger with Rasch models (RM), focusing specifically on presence of subscales, and differential item functioning (DIF) across cohorts. We confirmed the existence of two separate subscales, EDCS and WCWS, both with adequate reliability. None of the subscales fitted a pure RM, but adequate fit was found for graphical log-linear RMs with evidence of DIF for the ECDS. However, adjusting the score to account for DIF had practically no effect, suggesting that the total non-adjusted mean score can be used in future cohort comparisons. Perceived exposure to combat and danger and witnessing consequences of war are related, but essentially distinct, concepts, each providing unique information about deployment adversities. Future studies should evaluate their shared and unique contribution to the risk of post-deployment PTSD.

La percepción de exposición al combate ha resultado ser proporcional a la severidad del trastorno por estrés post-traumático (TEPT) posterior al despliegue militar. Sin embargo, otras adversidades percibidas durante el despliegue, tales como experimentar peligro, angustia, y dificultades en la zona de guerra, han sido menos sistemáticamente estudiadas, pero podrían jugar un rol sustancialmente equivalente para el impacto en la salud mental posterior al despliegue militar. Se requiere desarrollar y validar escalas que evalúen estos constructos relacionados para distinguir su contribución al riesgo de desarrollar TEPT posterior al despliegue. El objetivo es evaluar la validez de 10 ítems que miden el peligro percibido distribuido a todo el personal desplegado con la Defensa Danesa desde 1998. Se hipotetizan dos escalas; Exposición a Peligro y Combate (EDC) y Exposición a Consecuencias de Guerra (WCW). Se incluyó a dos cohortes militares, que se desplegaron en Afganistán el año 2009 (Cohorte 1, N=276), y el 2013 (Cohorte 2, N=273). Se recopiló datos a partir de cuestionarios seis meses después de regreso al país de origen, incluyendo experiencias dentro del despliegue y reacciones posteriores a éste. Se probó en las cohortes la validez de constructo de los 10 ítems de peligro percibido con modelos Rasch (RM), poniendo especial énfasis en la presencia de subescalas, e Ítem de funcionamiento diferencial (DIF). Se confirmó la existencia de dos subescalas separadas, EDC y WCW, ambas con fiabilidad adecuada. Ninguna de las subescales correspondía a un modelo Rasch puro, pero sí se encontró una relación adecuada para un modelo Rasch logarítmico-lineal con evidencia de DIF para EDC. No obstante, el ajustar el puntaje para explicar el IFD prácticamente no tuvo efectos, sugiriendo que la puntuación media total no ajustada puede ser utilizada en futuras comparaciones de cohortes. La exposición percibida al combate y el peligro y exposición a consecuencias de guerra son dos conceptos relacionados, pero esencialmente distintos, cada uno proveyendo información única sobre los efectos adversos del despliegue militar. Futuros estudios deberían evaluar su contribución compartida y única al riesgo de desarrollar TEPT posterior al despliegue militar.