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Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors.

ABSTRACT: Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

SUBMITTER: Schultebraucks K

PROVIDER: S-EPMC8589682 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:OBJECTIVE:The primary objective was to explore differences in mental health problems (MHP) between serving Canadian Armed Forces (CAF) components (Regular Force (RegF); Reserve Force (ResF)) with an Afghanistan deployment and to assess the contribution of both component and deployment experiences to MHP using covariate-adjusted prevalence difference estimates. Additionally, mental health services use (MHSU) was descriptively assessed among those with a mental disorder. DESIGN:Data came from the 2013 CAF Mental Health Survey, a cross-sectional survey of serving personnel (n=72?629). Analyses were limited to those with an Afghanistan deployment (population n=35?311; sampled n=4854). Logistic regression compared MHP between RegF and ResF members. Covariate-adjusted prevalence differences were computed. PRIMARY OUTCOME MEASURE:The primary outcomes were MHP, past-year mental disorders, identified using the WHO's Composite International Diagnostic Interview, and past-year suicide ideation. RESULTS:ResF personnel were less likely to be identified with a past-year anxiety disorder (adjusted OR (AOR)=0.72 (95% CI 0.58 to 0.90)), specifically both generalised anxiety disorder and panic disorder, but more likely to be identified with a past-year alcohol abuse disorder (AOR=1.63 (95% CI 1.04 to 2.58)). The magnitude of the covariate-adjusted disorder prevalence differences for component was highest for the any anxiety disorder outcome, 2.8% (95% CI 1.0 to 4.6); lower for ResF. All but one deployment-related experience variable had some association with MHP. The 'ever felt responsible for the death of a Canadian or ally personnel' experience had the strongest association with MHP; its estimated covariate-adjusted disorder prevalence difference was highest for the any (of the six measured) mental disorder outcome (11.2% (95% CI 6.6 to 15.9)). Additionally, ResF reported less past-year MHSU and more past-year civilian MHSU. CONCLUSIONS:Past-year MHP differences were identified between components. Our findings suggest that although deployment-related experiences were highly associated with MHP, these only partially accounted for MHP differences between components. Additional research is needed to further investigate MHSU differences between components.

Project description:PurposeWe noted incidental findings on chest computed tomography (CT) imaging of expiratory central airway collapse (ECAC) in dyspneic patients after military deployment to southwest Asia (mainly Iraq and Afghanistan). We developed a standardized chest CT protocol with dynamic expiration to enhance diagnostic reliability and investigated demographic, clinical, and deployment characteristics possibly associated with ECAC.Materials and methodsWe calculated ECAC in 62 consecutive post-9/11 deployers with dyspnea who underwent multi-detector chest CT acquisition. ECAC was defined as ≥70% reduction in the cross-sectional tracheal area at dynamic expiration. We compared demographics (age, smoking, body mass index), comorbid conditions (gastroesophageal reflux, obstructive sleep apnea [OSA]), and clinical findings (air trapping, forced expiratory volume in 1 second percent predicted) in deployers with and without ECAC. We examined associations between ECAC and forced expiratory volume in 1 second percent predicted, air trapping, OSA, deployment duration, and blast exposure.ResultsAmong 62 consecutive deployers with persistent dyspnea, 37% had ECAC. Three had severe (>85%) collapse. Those with ECAC were older (mean age 46 vs. 40 y, P=0.02), but no other demographic or clinical characteristics were statistically different among the groups. Although not statistically significant, ECAC odds were 1.5 times higher (95% confidence interval: 0.9, 2.5) for each additional year of southwest Asia deployment. Deployers with ECAC had 1.6 times greater odds (95% confidence interval: 0.5, 4.8) of OSA.ConclusionsFindings suggest that ECAC is common in symptomatic southwest Asia deployers. Chest high-resolution CT with dynamic expiration may provide an insight into the causes of dyspnea in this population, although risk factors for ECAC remain to be determined. A standardized semiquantitative approach to CT-based assessment of ECAC should improve reliable diagnosis in dyspneic patients.

Project description:Background:Previously we reported a genetic risk score significantly improved PTSD prediction among a trauma-exposed civilian population. In the current study, we sought to assess this prediction among a trauma-exposed military population. Methods:We examined current PTSD diagnosis and PTSD symptom severity among a random sample of 1042 community-based US military veterans. Main effects and interaction effects were assessed for PTSD genetic risk by trauma exposure using cross-product terms for PTSD x trauma exposures, including combat, lifetime trauma, and adverse childhood exposures. The PTSD risk variants studied were within genetic loci previously associated with PTSD, including CRHR1, CHRNA5, RORA, and FKBP5 genetic variants, which were used to calculate a total PTSD genetic risk score (range=0-8, mean=3.6, SD=1.4). Results:Based on DSM-5 PTSD criteria, 7.1% of veterans (95% CI=5.6-8.8) met criteria for current PTSD. The PTSD genetic risk count was significantly higher among PTSD cases vs non-cases (3.92 vs 3.55, p=0.027). Since the PTSD genetic risk score was not significant in the PTSD diagnosis model, we assessed this association using PTSD symptom severity. Because these symptom data were skewed (mean=9.54, SD=12.71, range=0-76), we used negative binomial regression to assess this outcome. This symptom model included a PTSD genetic risk score, demographic factors, trauma exposures, current insomnia, current depression, concussion history, and attention-deficit disorder, expressed as incident rate ratios (IRR), which is an estimate of one-unit increase in PTSD severity, given other variables are held constant. Variables in the final model included age and sex (both p<0.001), PTSD genetic risk (IRR=1.02, p=0.028), warzone tours (IRR=0.94, p=0.003), childhood abuse (IRR=1.50, p<0.0001), current depression (IRR=1.89, p<0.0001), current insomnia (IRR=2.58, p<0.0001), low social support (IRR=1.19, p<0.0001), attention-deficit disorder (IRR=1.51, p<0.0001), agreeable personality (IRR=0.77, p<0.0001), and concussion (IRR=1.38, p<0.0001). Significant interactions were detected for combat and lifetime trauma exposure by PTSD genetic risk (both p<0.0001), suggesting that the impact of trauma exposures on PTSD severity was lower when the PTSD genetic risk was higher. Conclusion:Both warzone and non-warzone factors predicted current PTSD symptoms among veterans, including a PTSD genetic risk score. Interaction effects were detected for combat exposure and lifetime trauma by genetic risk score for PTSD symptoms, suggesting that PTSD symptom manifestation was more dependent on PTSD risk variants than the level of trauma or combat exposure. This suggests that controlling for other factors, the absence of genetic risk variants may confer PTSD resilience. Further research is planned.

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Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors.

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