Singular and combined effects of transcranial infrared laser stimulation and exposure therapy on pathological fear: a randomized clinical trial.

BACKGROUND: Preclinical findings suggest that transcranial infrared laser stimulation (TILS) improves fear extinction learning and cognitive function by enhancing prefrontal cortex (PFC) oxygen metabolism. These findings prompted our investigation of treating pathological fear using this non-invasive stimulation approach either alone to the dorsolateral PFC (dlPFC), or to the ventromedial PFC (vmPFC) in combination with exposure therapy. METHODS: Volunteers with pathological fear of either enclosed spaces, contamination, public speaking, or anxiety-related bodily sensations were recruited for this randomized, single-blind, sham-controlled trial with four arms: (a) Exposure + TILS_vmPFC (n = 29), (b) Exposure + sham TILS_vmPFC (n = 29), (c) TILS_dlPFC alone (n = 26), or (d) Sham TILS _dlPFC alone (n = 28). Post-treatment assessments occurred immediately following treatment. Follow-up assessments occurred 2 weeks after treatment. RESULTS: A total of 112 participants were randomized [age range: 18-63 years; 96 females (85.71%)]. Significant interactions of Group × Time and Group × Context indicated differential treatment effects on retention (i.e. between time-points, averaged across contexts) and on generalization (i.e. between contexts, averaged across time-points), respectively. Among the monotherapies, TILS_dlPFC outperformed SHAM_dlPFC in the initial context, b = -13.44, 95% CI (-25.73 to -1.15), p = 0.03. Among the combined treatments, differences between EX + TILS_vmPFC and EX + SHAM_vmPFC were non-significant across all contrasts. CONCLUSIONS: TILS to the dlPFC, one of the PFC regions implicated in emotion regulation, resulted in a context-specific benefit as a monotherapy for reducing fear. Contrary to prediction, TILS to the vmPFC, a region implicated in fear extinction memory consolidation, did not enhance exposure therapy outcome.

Associations of polygenic risk scores with posttraumatic stress symptom trajectories following combat deployment.

BACKGROUND: Identification of genetic risk factors may inform the prevention and treatment of posttraumatic stress disorder (PTSD). This study evaluates the associations of polygenic risk scores (PRS) with patterns of posttraumatic stress symptoms following combat deployment. METHOD: US Army soldiers of European ancestry (n = 4900) provided genomic data and ratings of posttraumatic stress symptoms before and after deployment to Afghanistan in 2012. Latent growth mixture modeling was used to model posttraumatic stress symptom trajectories among participants who provided post-deployment data (n = 4353). Multinomial logistic regression models tested independent associations between trajectory membership and PRS for PTSD, major depressive disorder (MDD), schizophrenia, neuroticism, alcohol use disorder, and suicide attempt, controlling for age, sex, ancestry, and exposure to potentially traumatic events, and weighted to account for uncertainty in trajectory classification and missing data. RESULTS: Participants were classified into low-severity (77.2%), increasing-severity (10.5%), decreasing-severity (8.0%), and high-severity (4.3%) posttraumatic stress symptom trajectories. Standardized PTSD-PRS and MDD-PRS were associated with greater odds of membership in the high-severity v. low-severity trajectory [adjusted odds ratios and 95% confidence intervals, 1.23 (1.06-1.43) and 1.18 (1.02-1.37), respectively] and the increasing-severity v. low-severity trajectory [1.12 (1.01-1.25) and 1.16 (1.04-1.28), respectively]. Additionally, MDD-PRS was associated with greater odds of membership in the decreasing-severity v. low-severity trajectory [1.16 (1.03-1.31)]. No other associations were statistically significant. CONCLUSIONS: Higher polygenic risk for PTSD or MDD is associated with more severe posttraumatic stress symptom trajectories following combat deployment. PRS may help stratify at-risk individuals, enabling more precise targeting of treatment and prevention programs.

Posttraumatic stress disorder symptom trajectories in a 16-month COVID-19 pandemic period.

COVID-19 pandemic presents an unheralded opportunity to better understand trajectories of posttraumatic stress disorder (PTSD) symptoms across a prolonged period of social disruption and stress. We tracked PTSD symptoms among trauma-exposed individuals in the United States and sought to identify population-based variability in PTSD symptom trajectories and understand what, if any, early pandemic experiences predicted membership in one trajectory versus others. As part of a longitudinal study of U.S. residents during the pandemic, participants who reported at least one potentially traumatic experience in their lifetime (N = 1,206) at Wave 1 (April 2020) were included in the current study. PTSD symptoms were assessed using the PCL-5 at four time points extending to July 2021. Latent growth mixture modeling was used to identify heterogeneous symptom trajectories. Trajectory membership was regressed on experiences from the early stage of the pandemic as measured using the Epidemic-Pandemic Impacts Inventory in a model that controlled for variables with documented associations to PTSD trajectories, including age, sex, income, and trauma history. Four trajectories were identified, categorized as resilient (73.0%), recurring (13.3%), recovering (8.3%), and chronic (5.5%). Emotional and physical health problems and positive changes associated with the early phase of the pandemic were each significant predictors of trajectory membership over and above all other variables in the model. Predictors primarily differentiated the resilient trajectory from each of the other three trajectories. Distinct PTSD symptom trajectories during the COVID-19 pandemic suggest a need for targeted efforts to help individuals at most risk for ongoing distress.

Pretreatment Posttraumatic Stress Disorder Symptom Network Metrics Predict the Strength of the Association Between Node Change and Network Change During Treatment.

Network analysis has been increasingly applied in an effort to understand complex interactions among symptoms in posttraumatic stress disorder (PTSD). Although methods that initially focused on identifying central symptoms in cross-sectional networks have been extended to longitudinal data that can reveal the relative roles of acute symptoms in the emergence of the PTSD syndrome, the association between network metrics and symptom change during treatment have yet to be explored in PTSD. To address this gap, we estimated pretreatment PTSD symptom networks in a sample of patients from a multisite clinical trial for women with full or subthreshold PTSD and substance use. We tested the hypothesis that node metrics calculated in the pretreatment network would be predictive of the strength of the association between a symptom's change and the change in the severity of all other symptoms through the course of treatment. A symptom node's strength and predictability in the pretreatment network were each strongly correlated with the association between that symptom's change and overall change across the symptom network, r(15) = .79, p < .001 and r(15) = .75, p < .001, respectively, whereas a symptom's mean severity at pretreatment was not, r(15) = .27, p = .292. These findings suggest that a node's centrality prior to treatment engagement is a predictor of its association with overall symptom change throughout the treatment process.

Bias-contingent attention bias modification and attention control training in treatment of PTSD: a randomized control trial.

BACKGROUND: Randomized control trials (RCTs) comparing attention control training (ACT) and attention bias modification (ABM) in posttraumatic stress disorder (PTSD) have shown mixed results. The current RCT extends the extant literature by comparing the efficacy of ACT and a novel bias-contingent-ABM (BC-ABM), in which direction of training is contingent upon the direction of pre-treatment attention bias (AB), in a sample of civilian patients with PTSD. METHODS: Fifty treatment-seeking civilian patients with PTSD were randomly assigned to either ACT or BC-ABM. Clinician and self-report measures of PTSD and depression, as well as AB and attention bias variability (ABV), were acquired pre- and post-treatment. RESULTS: ACT yielded greater reductions in PTSD and depressive symptoms on both clinician-rated and self-reported measures compared with BC-ABM. The BC-ABM condition successfully shifted ABs in the intended training direction. In the ACT group, there was no significant change in ABV or AB from pre- to post-treatment. CONCLUSIONS: The current RCT extends previous results in being the first to apply ABM that is contingent upon AB at pre-treatment. This personalized BC-ABM approach is associated with significant reductions in symptoms. However, ACT produces even greater reductions, thereby emerging as a promising treatment for PTSD.

Effect of cognitive bias modification-memory on depressive symptoms and autobiographical memory bias: two independent studies in high-ruminating and dysphoric samples.

Memory bias is a risk factor for depression. In two independent studies, the efficacy of one CBM-Memory session on negative memory bias and depressive symptoms was tested in vulnerable samples. We compared positive to neutral (control) CBM-Memory trainings in highly-ruminating individuals (N = 101) and individuals with elevated depressive symptoms (N = 100). In both studies, participants studied positive, neutral, and negative Swahili words paired with their translations. In five study-test blocks, they were then prompted to retrieve either only the positive or neutral translations. Immediately following the training and one week later, we tested cued recall of all translations and autobiographical memory bias; and also measured mood, depressive symptoms, and rumination. Retrieval practice resulted in training-congruent recall both immediately after and one week after the training. Overall, there was no differential decrease in symptoms or difference in autobiographical memory bias between the training conditions. In the dysphoric but not in the high-ruminating sample, the positive training resulted in positive autobiographical bias only in dysphoric individuals with positive pre-existing bias. We conclude that one session of positive retrieval-based CBM-Memory may not be enough to yield symptom change and affect autobiographical memory bias in vulnerable individuals.

Exposure-based therapy changes amygdala and hippocampus resting-state functional connectivity in patients with posttraumatic stress disorder.

BACKGROUND: Recent research suggests that posttraumatic stress disorder (PTSD) is associated with altered amygdala and hippocampal resting-state functional connectivity (rsFC). However, less research has examined whether Prolonged Exposure (PE), a first line exposure-based treatment for PTSD, has the potential to alter resting state neural networks. METHODS: A total of 24 patients with PTSD and 26 matched trauma-exposed healthy controls (TEHCs) underwent resting-state functional magnetic resonance imaging (fMRI) at baseline. PTSD patients were scanned a second time after completing 10-session PE in which patients narrated a detailed trauma account (imaginal exposure) and confronted trauma reminders (in vivo exposure) to extinguish trauma-related fear responses. TEHC were scanned again following a 10-week waiting period. Seed regions of interest (ROIs) included centromedial amygdala (CMA), basolateral amygdala (BLA), and the hippocampus. RESULTS: Post- versus pretreatment comparisons indicated increased rsFC of the BLA and CMA with the orbitofrontal cortex (OFC), and hippocampus-medial prefrontal cortex (mPFC) among patients with PTSD, but not among TEHC participants. CONCLUSIONS: Enhanced amygdala and hippocampus rsFC with prefrontal cortical regions following PE could underlie improved capacity for inhibition and re-evaluation of threat, and heightened memory encoding and retrieval ability, respectively. These findings encourage further investigation of this circuitry as a therapeutic target in PTSD.

Concurrent Treatment with Prolonged Exposure for Co-Occurring Full or Subthreshold Posttraumatic Stress Disorder and Substance Use Disorders: A Randomized Clinical Trial.

BACKGROUND: To test whether an integrated prolonged exposure (PE) approach could address posttraumatic stress disorder (PTSD) symptoms effectively in individuals with co-occurring substance use disorders (SUD), we compared concurrent treatment of PTSD and SUD using PE (COPE) to relapse prevention therapy (RPT) for SUD and an active monitoring control group (AMCG). METHODS: We conducted a randomized 12-week trial with participants (n = 110; 64% males; 59% African Americans) who met Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision criteria for full or subthreshold PTSD and SUD. Participants were randomly assigned to COPE (n = 39), RPT (n = 43), or AMCG (n = 28). RESULTS: At the end-of-treatment, COPE and RPT demonstrated greater reduction in PTSD symptom severity relative to AMCG (COPE-AMCG = -34.06, p < 0.001; RPT-AMCG = -22.58, p = 0.002). Although the difference between COPE and RPT was not significant in the complete sample, the subset of participants with full (vs. subthreshold) PTSD demonstrated significantly greater reduction of PTSD severity in COPE relative to RPT. Both treatments were superior to AMCG in reducing the days of primary substance use (COPE-AMCG = -0.97, p = 0.01; RPT-AMCG = -2.07, p < 0.001). Relative to COPE, RPT showed significantly more improvement in SUD outcome at end-of-treatment (RPT-COPE = -1.10, p = 0.047). At 3-month follow-up, COPE and RPT maintained their treatment gains and were not significantly different in PTSD severity or days of primary substance use. CONCLUSION: COPE and RPT reduced PTSD and SUD severity in participants with PTSD + SUD. Findings suggest that among those with full PTSD, COPE improves PTSD symptoms more than a SUD-only treatment. The use of PE for PTSD was associated with significant decreases in PTSD symptoms without worsening of substance use.

Altered resting state functional connectivity of fear and reward circuitry in comorbid PTSD and major depression.

BACKGROUND: Individuals with comorbid posttraumatic stress disorder and major depressive disorder (PTSD-MDD) often exhibit greater functional impairment and poorer treatment response than individuals with PTSD alone. Research has not determined whether PTSD-MDD is associated with different network connectivity abnormalities than PTSD alone. METHODS: We used functional magnetic resonance imaging (fMRI) to measure resting state functional connectivity (rs-FC) patterns of brain regions involved in fear and reward processing in three groups: patients with PTSD-alone (n = 27), PTSD-MDD (n = 21), and trauma-exposed healthy controls (TEHCs, n = 34). Based on previous research, seeds included basolateral amygdala (BLA), centromedial amygdala (CMA), and nucleus accumbens (NAcc). RESULTS: Regardless of MDD comorbidity, PTSD was associated with decreased connectivity of BLA-orbitalfrontal cortex (OFC) and CMA-thalamus pathways, key to fear processing, and fear expression, respectively. PTSD-MDD, compared to PTSD-alone and TEHC, was associated with decreased connectivity across multiple amygdala and striatal-subcortical pathways: BLA-OFC, NAcc-thalamus, and NAcc-hippocampus. Further, while both the BLA-OFC and the NAcc-thalamus pathways were correlated with MDD symptoms, PTSD symptoms correlated with the amygdala pathways (BLA-OFC; CMA-thalamus) only. CONCLUSIONS: Comorbid PTSD-MDD may be associated with multifaceted functional connectivity alterations in both fear and reward systems. Clinical implications are discussed.

PTSD REMISSION AFTER PROLONGED EXPOSURE TREATMENT IS ASSOCIATED WITH ANTERIOR CINGULATE CORTEX THINNING AND VOLUME REDUCTION.

BACKGROUND: Brain structures underlying posttraumatic stress disorder (PTSD) have been a focus of imaging studies, but associations between treatment outcome and alterations in brain structures remain largely unexamined. We longitudinally examined the relation of structural changes in the rostral anterior cingulate cortex (rACC), a previously identified key region in the PTSD fear network, to outcome of prolonged exposure (PE) treatment. METHOD: The sample included 78 adults (53 women): 41 patients with PTSD and 37 trauma-exposed healthy volunteers (TE-HCs). Patients underwent a 10-week course of PE treatment and completed pre- and posttreatment assessments and magnetic resonance imaging (MRI) structural scans. TE-HCs also underwent assessment and MRI at baseline and 10 weeks later. PE remitters (n = 11), nonremitters (n = 14), and TE-HCs, were compared at baseline on demographic and clinical characteristics and ACC structure. Remitters, nonremitters, and TE-HCs were compared for pre- to posttreatment clinical and structural ACC change, controlling for potential confounding variables. RESULTS: There were no baseline differences in structure between PTSD and TE-HCs or remitters and nonremitters. Following treatment, PTSD remitters exhibited cortical thinning and volume decrease in the left rACC compared with PTSD nonremitters and TE-HCs. CONCLUSIONS: These results, while in need of replication, suggest that PE treatment for PTSD, by extinguishing maladaptive trauma associations, may promote synaptic plasticity and structure change in rACC. Future research should explore possible underlying mechanisms.

At the crossroads: the intersection of substance use disorders, anxiety disorders, and posttraumatic stress disorder.

The co-occurrence of substance use disorders with anxiety disorders and/or posttraumatic stress disorder has been widely documented and when compared to each disorder alone, consistently linked to increased risk for a host of negative outcomes including greater impairment, poorer treatment response, and higher rates of symptom relapse. This article focuses on recent advances in the understanding and effective treatment of this common and highly complex comorbidity. Prevalence and epidemiological data are introduced, followed by a review of contemporary models of etiology and associative pathways. Conceptualizations of effective treatment approaches are discussed alongside evidence from the past decade of clinical research trials. Highlighted are ongoing questions regarding the benefit of sequential, parallel, and integrated approaches and the necessity of further investigation into the mechanisms underlying treatment efficacy. Lastly, recent contributions from neuroscience research are offered as a promising bridge for the development and testing of novel, interdisciplinary treatment approaches.

Isradipine augmentation of virtual reality cue exposure therapy for tobacco craving: a triple-blind randomized controlled trial.

Preclinical research with rodents suggests that the L-type calcium channel blocker isradipine can enhance long-term extinction of conditioned place preference for addictive substances when it is administered in conjunction with extinction training. Although isradipine alone, which is FDA-approved for hypertension, has not shown a direct effect on craving in human drug users, its potential to augment behavioral treatments designed to reduce craving remains unknown. We conducted a triple-blind, randomized placebo-controlled pilot clinical trial of isradipine combined with a novel virtual reality cue exposure therapy (VR-CET) approach with multimodal cues that targeted craving. After 24 hours of abstinence, 78 adults with an ongoing history of daily cigarette use received isradipine (n = 40) or placebo (n = 38) and reported craving levels after each of 10 trials of VR-CET. Consistent with pre-registered hypotheses, the isradipine group had significantly lower mean craving across cue exposure trials at the medication-free 24-hour follow-up (d = -0.42, p = 0.046). There were no serious adverse events; however, side effects such as headache and dizziness occurred more frequently in the isradipine group. The findings of the current study support follow-up clinical trials that specifically test the efficacy of isradipine-augmented VR-CET for reducing smoking relapse rates after an initial quit attempt. clinicaltrials.gov: NCT03083353.

Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample.

Importance: Given that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined. Objective: To assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care. Design, Setting, and Participants: This prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023. Main Outcome and Measures: Suicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records. Results: The study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt. Conclusions and Relevance: In this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.

Project harmony: A systematic review and network meta-analysis of psychotherapy and pharmacologic trials for comorbid posttraumatic stress, alcohol, and other drug use disorders.

We conducted a systematic review and network meta-analyses (NMA) of psychotherapy and pharmacologic treatments for individuals with co-occurring posttraumatic stress disorder (PTSD) and alcohol or other drug use disorder (AOD). A comprehensive search spanning 1995-2019 yielded a pool of 39 studies for systematic review, including 24 randomized controlled trials for the NMA. Study interventions were grouped by target of treatment (PTSD + AOD, PTSD-only, and AOD-only) and approach (psychotherapy or medication). Standardized mean differences (SMD) from the NMA yielded evidence that at the end of treatment, integrated, trauma-focused therapy for PTSD + AOD was more effective at reducing PTSD symptoms than integrated, non-trauma-focused therapy (SMD = -0.30), AOD-focused psychotherapy (SMD = -0.29), and other control psychotherapies (SMD = -0.43). End-of-treatment alcohol use severity was less for AOD medication compared to placebo medication (SMD = -0.36) and trauma-focused therapy for PTSD + placebo medication (SMD = -0.67), and less for trauma-focused psychotherapy + AOD medication compared to PTSD medication (SMD = -0.53), placebo medication (SMD = -0.50), and trauma-focused psychotherapy + placebo medication (SMD = -0.81). Key limitations include the small number of studies in the NMA for pharmacologic treatments and the lack of demographic diversity apparent in the existing literature. Findings suggest room for new studies that can address limitations in study sample composition, sample sizes, retention, and apply new techniques for conducting comparative effectiveness in PTSD + AOD treatment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Polygenic risk for suicide attempt is associated with lifetime suicide attempt in US soldiers independent of parental risk.

BACKGROUND: Suicide is a leading cause of death worldwide. Whereas some studies have suggested that a direct measure of common genetic liability for suicide attempts (SA), captured by a polygenic risk score for SA (SA-PRS), explains risk independent of parental history, further confirmation would be useful. Even more unsettled is the extent to which SA-PRS is associated with lifetime non-suicidal self-injury (NSSI). METHODS: We used summary statistics from the largest available GWAS study of SA to generate SA-PRS for two non-overlapping cohorts of soldiers of European ancestry. These were tested in multivariable models that included parental major depressive disorder (MDD) and parental SA. RESULTS: In the first cohort, 417 (6.3 %) of 6573 soldiers reported lifetime SA and 1195 (18.2 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.26, 95%CI:1.13-1.39, p < 0.001] per standardized unit SA-PRS]. In the second cohort, 204 (4.2 %) of 4900 soldiers reported lifetime SA, and 299 (6.1 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.20, 95%CI:1.04-1.38, p = 0.014]. A combined analysis of both cohorts yielded similar results. In neither cohort or in the combined analysis was SA-PRS significantly associated with NSSI. CONCLUSIONS: PRS for SA conveys information about likelihood of lifetime SA (but not NSSI, demonstrating specificity), independent of self-reported parental history of MDD and parental history of SA. LIMITATIONS: At present, the magnitude of effects is small and would not be immediately useful for clinical decision-making or risk-stratified prevention initiatives, but this may be expected to improve with further iterations. Also critical will be the extension of these findings to more diverse populations.

Improving Exposure Therapy: Rationale and Design of an International Consortium.

The Exposure Therapy Consortium (ETC) was established to advance the science and practice of exposure therapy. To encourage participation from researchers and clinicians, this article describes the organizational structure and activities of the ETC. Initial research working group experiences and a proof-of-principle study underscore the potential of team science and larger-scale collaborative research in this area. Clinical working groups have begun to identify opportunities to enhance access to helpful resources for implementing exposure therapy effectively. This article discusses directions for expanding the consortium's activities and its impact on a global scale.

Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment.

Importance: Military deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience. Objective: To develop and validate a machine learning (ML) model to predict postdeployment PTSD. Design, Setting, and Participants: This diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022. Main Outcomes and Measures: Posttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse. Results: This study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion. Conclusions and Relevance: In this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.

Genetic, environmental, and behavioral correlates of lifetime suicide attempt: Analysis of additive and interactive effects in two cohorts of US Army soldiers.

Recently developed measures of genetic liability to suicide attempt may convey unique information regarding an individual's risk of suicidal behavior. We calculated a polygenic risk score for suicide attempt (SA-PRS) for soldiers of European ancestry who participated in the Army STARRS New Soldier Study (NSS; n = 6573) or Pre/Post Deployment Study (PPDS; n = 4900). Multivariable logistic regression models were fit within each sample to estimate the association of SA-PRS with lifetime suicide attempt (LSA), and to examine whether SA-PRS displayed additive or interactive effects with environmental and behavioral risk/protective factors (lifetime trauma burden, childhood maltreatment, negative urgency impulsivity, social network size, perceived mattering, and dispositional optimism). Age, sex, and within-ancestry variation were included as covariates. Observed prevalence of LSA was 6.3% and 4.2% in the NSS and PPDS samples, respectively. In the NSS model, SA-PRS and environmental/behavioral factors displayed strictly additive effects on odds of LSA. Results indicated an estimated 21% increase in odds of LSA per 1 SD increase in SA-PRS [adjusted odds ratio (AOR; 95% CI) = 1.21 (1.09-1.35)]. In PPDS, the effect of SA-PRS varied by reports of optimism [AOR = 0.85 (0.74-0.98) for SA-PRS x optimism effect]. Individuals reporting low and average optimism had 37% and 16% increased odds of LSA per 1 SD increase in SA-PRS, respectively, whereas SA-PRS was not associated with LSA in those reporting high optimism. Overall, results suggested the SA-PRS had predictive value over and above several environmental and behavioral risk factors for LSA. Moreover, elevated SA-PRS may be more concerning in the presence of environmental and behavioral risk factors (e.g., high trauma burden; low optimism). Given the relatively small effect magnitudes, the cost and incremental benefits of utilizing SA-PRS for risk targeting must also be considered in future work.

Alcohol, cannabis, and nicotine use have distinct associations with COVID-19 pandemic-related experiences: An exploratory Bayesian network analysis across two timepoints.

BACKGROUND: Substance use trends during the COVID-19 pandemic have been extensively documented. However, relatively less is known about the associations between pandemic-related experiences and substance use. METHOD: In July 2020 and January 2021, a broad U.S. community sample (N = 1123) completed online assessments of past month alcohol, cannabis, and nicotine use and the 92-item Epidemic-Pandemic Impacts Inventory, a multidimensional measure of pandemic-related experiences. We examined links between substance use frequency, and pandemic impact on emotional, physical, economic, and other key domains, using Bayesian Gaussian graphical networks in which edges represent significant associations between variables (referred to as nodes). Bayesian network comparison approaches were used to assess the evidence of stability (or change) in associations between the two timepoints. RESULTS: After controlling for all other nodes in the network, multiple significant edges connecting substance use nodes and pandemic-experience nodes were observed across both time points, including positive- (r range 0.07-0.23) and negative-associations (r range -0.25 to -0.11). Alcohol was positively associated with social and emotional pandemic impacts and negatively associated with economic impacts. Nicotine was positively associated with economic impact and negatively associated with social impact. Cannabis was positively associated with emotional impact. Network comparison suggested these associations were stable across the two timepoints. CONCLUSION: Alcohol, nicotine, and cannabis use had unique associations to a few specific domains among a broad range of pandemic-related experiences. Given the cross-sectional nature of these analyses with observational data, further investigation is needed to identify potential causal links.

Development and validation of a machine learning model using electronic health records to predict trauma- and stressor-related psychiatric disorders after hospitalization with sepsis.

A significant minority of individuals develop trauma- and stressor-related disorders (TSRD) after surviving sepsis, a life-threatening immune response to infections. Accurate prediction of risk for TSRD can facilitate targeted early intervention strategies, but many existing models rely on research measures that are impractical to incorporate to standard emergency department workflows. To increase the feasibility of implementation, we developed models that predict TSRD in the year after survival from sepsis using only electronic health records from the hospitalization (n = 217,122 hospitalizations from 2012-2015). The optimal model was evaluated in a temporally independent prospective test sample (n = 128,783 hospitalizations from 2016-2017), where patients in the highest-risk decile accounted for nearly one-third of TSRD cases. Our approach demonstrates that risk for TSRD after sepsis can be stratified without additional assessment burden on clinicians and patients, which increases the likelihood of model implementation in hospital settings.