National Center for Post Traumatic Stress Disorder

Brenner et al. (2021) developed and initially tested two computerized adaptive measures of posttraumatic stress disorder (PTSD), one that provides a provisional diagnosis (PTSD computerized adaptive diagnostic screen [CAD-PTSD]) and one that estimates symptom severity (PTSD computerized adaptive severity test [CAT-PTSD]). We expanded on the initial psychometric findings by collecting data regarding test-retest reliability, incremental validity, and respondent burden. A sample of veterans (N = 156, 32% women, 36% Black, 13% identified as Spanish, Hispanic, Latino, Puerto Rican, or Cuban) recruited from three Veterans Affairs medical centers completed the CAT-PTSD and CAD-PTSD, the Clinician-Administered PTSD Scale for DSM-5, the PTSD Checklist for DSM-5 (PCL-5), Primary Care PTSD Screen for DSM-5 (PC-PTSD-5), and a battery of other self-rated scales. Fifty-three participants (34%) completed the measures a second time within 7 days (Mdays = 5.41; SD = 1.97) of their first visit. CAT-PTSD scores revealed good convergent validity (r = .78 with Clinician-Administered PTSD Scale for DSM-5 total score), discriminant validity, and test-retest reliability (r = .80). Scores on the PCL-5 and PC-PTSD-5 had similar characteristics. The CAD-PTSD demonstrated poor diagnostic efficiency, κ(.5, 0) = .40, and test-retest reliability (κ = .25), whereas previously established cut-scores for the PCL-5 and PC-PTSD-5 showed fair to good diagnostic utility and adequate to good test-retest reliability. Results suggest that the CAT-PTSD may provide a valid indicator of PTSD symptom severity, but does not offer incremental value beyond the PCL-5 and PC-PTSD-5. The CAD-PTSD was markedly inferior to the use of PCL-5 or PC-PTSD-5 cut scores. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

This article presents an overview of a novel process improvement project that aimed to enhance the utilization of prolonged exposure for treating posttraumatic stress disorder in behavioral health clinics within the Military Health System (MHS). The MHS is a geographically dispersed medical system encompassing diverse clinics and poses unique challenges to the adoption of evidence-based practices. To address these challenges, the Targeted Assessment and Context-Tailored Implementation of Change Strategies project was developed. Implemented across eight MHS clinics, this project involved conducting a comprehensive needs assessment to identify barriers, developing customized implementation plans for each site, and providing coaching calls and access to a clinic optimization toolkit for the clinic staff. We describe the development of Targeted Assessment and Context-Tailored Implementation of Change Strategies intervention components, including an implementation rubric that documents barriers to evidence-based practice utilization and proposes specific actions based on the underlying causes of these barriers. Additionally, a needs assessment interview tool and an implementation toolkit were developed to identify clinic-level challenges and support the implementation process, respectively. The needs assessment phase of the project involved conducting interviews with clinic staff and leadership, as well as reviewing clinic appointment data to discern trends and patterns in care. The insights gleaned from these interviews were instrumental in formulating tailored implementation plans for increasing the usage of prolonged exposure. Each plan was collaboratively developed with clinic leadership and subsequently put into practice at the site with the support of weekly coaching calls provided by an implementation science expert. Portions of this research project occurred during the COVID-19 pandemic, and we discuss the impact of the pandemic on the implementation of this study. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) is regarded as the state-of-the-art method for diagnosing Diagnostic and Statistical Manual of Mental Disorders, fifth edition, posttraumatic stress disorder (PTSD). To address the high demand for training in the CAPS-5, we developed the CAPS-5 Training Simulator (CAPSim), an online program in which learners administer the CAPS-5 to lifelike virtual PTSD patients and receive feedback on their performance. We created three virtual standardized PTSD patients who respond verbally to learners' spoken or typed prompts. Complexity and availability of in-course feedback varied across patients. The program was then made available to interested learners, and data on program use and user performance were collected to determine acceptability and feasibility. Over the first 4 years of the program, 4,484 users accessed the training. Of those who started the course (N = 3,022), most (83.6%) completed it. Although CAPS-5 familiarity varied, more than half of the users (58.3%) reported being moderately familiar with the CAPS-5 at baseline. Administration and scoring accuracy varied by virtual patient and by CAPS-5 item, with overall accuracy ranging from near perfect to 77.1% incorrect. Whereas the initial symptom prompts were delivered verbatim 76.2% of the time, in-sequence and additional required prompts were administered with less accuracy (46.0% and 39.2%, respectively). The CAPSim is an innovative method for training CAPS-5 learners with various skill levels. The CAPSim can identify specific administrative and scoring challenges and provide personalized feedback in a manner that appears acceptable to learners. The CAPSim has promise to meet the high demand for CAPS-5 training. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

In clinical settings targeting alcohol use disorder (AUD), it is often unclear whether a treatment option may best suit a patient's clinical needs. Clinicians providing AUD treatment are often required to predict patients' responses to guide treatment decisions. Recently, machine learning approaches have been used as tools in precision medicine to help guide these clinical decisions. However, the extent of their clinical utility in populations undergoing treatment is largely unknown. Using data from four Phase 2 randomized clinical trials affiliated with the NIAAA Clinical Investigations Group and a Phase 3 trial sponsored by the NIAAA, we developed a machine learning model to predict treatment response phenotypes derived from clustering drinking rates at the end of treatment. Harmonized data included demographics and baseline data from biological and clinical assessments. Follow-up analyses were performed to characterize treatment response phenotypes. Three clusters corresponding to mild (MSDU = 1.3), moderate (MSDU = 6.70) and severe (MSDU = 15.3) alcohol consumption were identified from end-of-treatment drinking data. Performance of the tree-based classifier using out-of-sample test data was 71% with baseline drinking included and 61% without. Exploratory analyses revealed participants clustered as mild drinkers showed reductions in drinking across treatment (MDifference = -0.731, SE = 0.114, p < 0.001) whereas participants clustered as severe had escalation in use (MDifference = 6.82, SE = 0.52, p < 0.001). Although males drank more than females at baseline (MDifference = 1.46, SE = 0.287, p < 0.001), no significant differences in consumption emerged at the end of treatment. Findings from this work indicate that alcohol use derived from patterns of consumption at the end of treatment maps onto unique treatment response trajectories for mild and severe forms of AUD. Furthermore, the identified clusters revealed sex-specific differences in alcohol consumption patterns across different phases of treatment. Overall, this highlights the utility of computational methods for deriving clinically meaningful AUD-related phenotypes across multiple studies, each with different treatments and participant characteristics.

Abstract Background: Improving early recognition and accurate diagnosis of Major Depressive Disorder (MDD) in childhood is a pressing concern. Quantitative electroencephalogram (qEEG) may be an effective, non-invasive diagnostic biomarker for MDD. Prior work by our team demonstrated decreased resting connectivity, as measured by qEEG coherence, in a heterogenous group of adolescents with MDD compared to age and gender matched health controls. This study explored qEEG coherence as a predictor of MDD diagnosis in a prospective, longitudinal sample of medication-free, adolescents with MDD vs healthy controls (HCs). Methods: Twenty-eight adolescents with MDD (Children’s Depression Rating Scale score ≥ 40), and twenty-seven age and gender matched HCs, (age 14-17, 78% female) received a baseline resting 32-channel EEG. Brain-wide coherence between channel pairs was calculated for the frequency bands (alpha, beta, theta, and delta) and compared between MDD youth and HC. Random forest classifiers were used to predict individual MDD status using baseline qEEG coherence. Models were trained and tested using 10-repeated, 10-fold cross validation and performance was evaluated with the area under the receiver operating characteristic curve (AUC-ROC). The contribution of individual predictors was assessed using permutation importance. Model significance was assessed using permutation testing (B=1000 resamples). Results: Random forest models predicted depression status with a trend-level of significance (mean AUC-ROC=0.65, p=0.08). Among the most predictive channel pairs, adolescent MDD was characterized by lower coherence in T7-P7 (p&lt;0.05), Fz-Cz, and Fp2-F8 as well as higher coherence in P4-O2 and Cz-Pz. Conclusions: This study provides preliminary evidence that multivariate patterns of qEEG may inform diagnosis of adolescent MDD. Specific aberrant patterns of coherence within the default mode network and cognitive control network were characteristic of adolescent MDD. Ongoing work will seek to replicate these findings in a larger cohort.

Patients with co-occurring chronic pain and PTSD are especially vulnerable to debilitating symptoms and frustrating experiences seeking healthcare. The complicated nature of the health conditions and their treatments present a challenge for providers to communicate their treatment recommendations and rationale. This qualitative study explored Veterans' views on communication with their providers during clinical visits related to their chronic pain and PTSD treatments, so that we may understand their experiences and identify opportunities to improve clinical communication and patient satisfaction. Semi-structured interviews with the interview guide finalized based on feedback from a Veteran engagement panel. Veterans diagnosed with chronic pain and PTSD who receive care from Veterans Health Administration. Inductive thematic analysis to characterize how Veterans describe interactions with providers treating their chronic pain and PTSD. Providers' communication influences Veterans' perceptions in three ways: (a) when Veterans expressed satisfaction with their provider, they described their gratitude for learning about their options; (b) when Veterans expressed dissatisfaction, it often represented a lack of mutual understanding or knowledge of the provider's rationale; and (c) inaccurate information on electronic medical records perpetuated a distrustful environment. Breakdowns occur when Veterans are unsure of their providers' rationale and our data underscore the importance of clear communication in establishing trust and optimizing care for Veterans with PTSD and chronic pain. As the interplay between chronic pain and PTSD symptoms takes centerstage in clinical innovations, providers must consider how best to educate their patients and explain their rationale for recommendations. Trauma-informed strategies may be especially valuable for delivering care to an inordinately vulnerable population.

Social determinants of health have been shown to have a greater impact on overall health than traditionally considered medical risk factors, especially in people with mental disorders. It is the primary responsibility of healthcare providers to ensure that persons with psychiatric disorders are actively helped in reducing the adverse effects of SDoMH on their health and healthcare. Yet, the current clinical psychiatric practice in the U.S. does not have any standardized guidelines for evaluating or addressing these critical factors even among individuals who are receiving psychiatric and other medical treatment. Although there are several barriers to implementing such interventions, there are also practical approaches to address selected social determinants of mental health and improve the well-being of our patients. In this article, we review the literature on the assessment of relevant social factors and pragmatic psychosocial strategies at the individual, family, and community levels to help reduce their adverse impact. We offer guidelines for psychiatric clinicians and case managers to improve the health and quality of life of persons with mental disorders, using sustained inter-professional collaborative efforts.