Evaluating a screener to quantify PTSD risk using emergency care information: a proof of concept study

Comprehensive bioinformatics analysis of co-expressed genes of post-traumatic stress disorder and major depressive disorder

BACKGROUND

Post-traumatic stress disorder (PTSD) is one of the most serious sequelae of trauma with serious impact worldwide. Studies have suggested an association between PTSD and major depressive disorder (MDD), but the underlying common mechanisms remain unclear. This study aimed to further explore the molecular mechanism between PTSD and MDD via comprehensive bioinformatics analysis.

METHODS

The microarray data of PTSD and MDD were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify the co-expressed genes associated with PTSD and MDD. Gene Set Enrichment Analysis (GSEA), enrichment analyses based on Disease Ontology (DO), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed using R software. Then, R software was used for single-sample gene set enrichment analysis (ssGSEA) and immune infiltration analysis on the co-expressed genes in the two datasets., Therefore, a logistic regression model was constructed to predict PTSD and MDD using the R language. Ultimately, this study employed PTSD and MDD models to assess alterations in the expression of target genes within the mouse hippocampus.

RESULTS

Four core genes (GNAQ, DPEP3, ICAM2, PACSIN2) were obtained through different analyses, and these genes had predictive validity for PTSD and MDD, playing an important role in the common mechanism of PTSD and MDD. The study findings reveal decreased expression levels of DPEP3, GNAQ, and PACDIN2 in PTSD samples, accompanied by an increased expression of ICAM2. In MDD samples, the expression of DPEP3 and ICAM2 is reduced, whereas GNAQ and PACDIN2 show an increase in expression.

CONCLUSIONS

This study provides a new perspective on the common molecular mechanisms of PTSD and MDD. These common pathways and core genes may provide promising clues for further experimental studies.

Development and validation of a brief screener for posttraumatic stress disorder risk in emergency medical settings

OBJECTIVE

Predicting risk of posttraumatic stress disorder (PTSD) in the acute care setting is challenging given the pace and acute care demands in the emergency department (ED) and the infeasibility of using time-consuming assessments. Currently, no accurate brief screening for long-term PTSD risk is routinely used in the ED. One instrument widely used in the ED is the 27-item Immediate Stress Reaction Checklist (ISRC). The aim of this study was to develop a short screener using a machine learning approach and to investigate whether accurate PTSD prediction in the ED can be achieved with substantially fewer items than the IRSC.

METHOD

This prospective longitudinal cohort study examined the development and validation of a brief screening instrument in two independent samples, a model development sample (N = 253) and an external validation sample (N = 93). We used a feature selection algorithm to identify a minimal subset of features of the ISRC and tested this subset in a predictive model to investigate if we can accurately predict long-term PTSD outcomes.

RESULTS

We were able to identify a reduced subset of 5 highly predictive features of the ISRC in the model development sample (AUC = 0.80), and we were able to validate those findings in the external validation sample (AUC = 0.84) to discriminate non-remitting vs. resilient trajectories.

CONCLUSION

This study developed and validated a brief 5-item screener in the ED setting, which may help to improve the diagnostic process of PTSD in the acute care setting and help ED clinicians plan follow-up care when patients are still in contact with the healthcare system. This could reduce the burden on patients and decrease the risk of chronic PTSD.

Altered brain activity and functional connectivity after MDMA-assisted therapy for post-traumatic stress disorder

Introduction

3,4-methylenedioxymethamphetamine-assisted therapy (MDMA-AT) for post-traumatic stress disorder (PTSD) has demonstrated promise in multiple clinical trials. MDMA is hypothesized to facilitate the therapeutic process, in part, by decreasing fear response during fear memory processing while increasing extinction learning. The acute administration of MDMA in healthy controls modifies recruitment of brain regions involved in the hyperactive fear response in PTSD such as the amygdala, hippocampus, and insula. However, to date there have been no neuroimaging studies aimed at directly elucidating the neural impact of MDMA-AT in PTSD patients.

Methods

We analyzed brain activity and connectivity via functional MRI during both rest and autobiographical memory (trauma and neutral) response before and two-months after MDMA-AT in nine veterans and first-responders with chronic PTSD of 6 months or more.

Results

We hypothesized that MDMA-AT would increase amygdala-hippocampus resting-state functional connectivity, however we only found evidence of a trend in the left amygdala-left hippocampus (t = -2.91, uncorrected p = 0.0225, corrected p = 0.0901). We also found reduced activation contrast (trauma > neutral) after MDMA-AT in the cuneus. Finally, the amount of recovery from PTSD after MDMA-AT correlated with changes in four functional connections during autobiographical memory recall: the left amygdala-left posterior cingulate cortex (PCC), left amygdala-right PCC, left amygdala-left insula, and left isthmus cingulate-left posterior hippocampus.

Discussion

Amygdala-insular functional connectivity is reliably implicated in PTSD and anxiety, and both regions are impacted by MDMA administration. These findings compliment previous research indicating that amygdala, hippocampus, and insula functional connectivity is a potential target of MDMA-AT, and highlights other regions of interest related to memory processes. More research is necessary to determine if these findings are specific to MDMA-AT compared to other types of treatment for PTSD.

Clinical trial registration

https://clinicaltrials.gov/ct2/show/NCT02102802, identifier NCT02102802.

Beyond Recidivism: Hospital-Based Violence Intervention and Early Health and Social Outcomes

BACKGROUND

Hospital-based violence intervention programs (HVIPs) are aimed at decreasing recurrent injury and improving the social determinants of health. We hypothesized that the HVIP intervention should be evaluated by how well it can address the immediate health and social needs of patients after injury. Our study sought to describe the experience of our nascent HVIP.

STUDY DESIGN

Case management records of patients treated by the HVIP of a level 1 trauma center from July 1, 2017 to October 1, 2020 were reviewed. Inclusion criteria were as follows: age between 18 and 60 with injury mechanisms that resulted from intentional violence. Patient-stated goals and social worker designation of patient services provided were analyzed. A subset of HVIP patients who completed the three planned study visit surveys at discharge and 1 and 3 months were compared with a cohort of violently injured patients to whom HVIP services were not available. Participants in both groups were asked to complete a battery of validated surveys to assess social outcomes and post-traumatic stress disorder (PTSD). Repeated-measures ANOVA was used to compare the two groups.

RESULTS

Two hundred and ninety-five patients met the inclusion criteria. One hundred and forty-six patients (49%) achieved their stated goals within 6 months of hospital discharge. Sixteen patients who achieved their stated goals disengaged from the program. Engagement in the HVIP resulted in significantly less PTSD at the time of hospital discharge. HVIP patients also experienced higher positive affect at hospital discharge, as described in the Positive and Negative Affect Schedule. HVIP participants were significantly more likely to achieve early positive health outcomes, such as completion of victim of crime compensation and return to school.

CONCLUSIONS

Our HVIP successfully achieved patient-stated short-term health and social goals in nearly half of all enrollees, indicating that HVIP patients are more likely to improve their social determinants of health than non-HVIP patients. Short-term health and social outcomes were improved in HVIP patients compared with non-HVIP patients, indicating increased engagement with the healthcare system. We suggest that these outcomes should replace recidivism as a metric for the efficacy of HVIP programs.

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae

Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and management at the individual level. Biomarker-enriched clinical trials have shown increased efficiency and informativeness in cancer research due to the selective exclusion of patients unlikely to benefit. In acute stress situations, clinically significant decisions are often made in time-sensitive manners and providers may be pressed to make decisions based on abbreviated clinical assessments. Up to 30% of trauma survivors admitted to the Emergency Department (ED) will develop long-lasting posttraumatic stress psychopathologies. The long-term impact of those survivors with posttraumatic stress sequelae are significant, impacting both long-term psychological and physiological recovery. An accurate prognostic model of who will develop posttraumatic stress symptoms does not exist yet. Additionally, no scalable and cost-effective method that can be easily integrated into routine care exists, even though especially the acute care setting provides a critical window of opportunity for prevention in the so-called golden hours when preventive measures are most effective. In this review, we aim to discuss emerging machine learning (ML) applications that are promising for precisely risk stratification and targeted treatments in the acute care setting. The aim of this narrative review is to present examples of digital health innovations and to discuss the potential of these new approaches for treatment selection and prevention of posttraumatic sequelae in the acute care setting. The application of artificial intelligence-based solutions have already had great success in other areas and are rapidly approaching the field of psychological care as well. New ways of algorithm-based risk predicting, and the use of digital phenotypes provide a high potential for predicting future risk of PTSD in acute care settings and to go new steps in precision psychiatry.

Symptoms of Anxiety, Depression, and Peritraumatic Dissociation in Critical Care Clinicians Managing Patients with COVID-19. A Cross-Sectional Study

Rationale: Frontline healthcare providers (HCPs) during the coronavirus disease (COVID-19) pandemic are at high risk of mental morbidity.

Objectives: To assess the prevalence of symptoms of anxiety, depression, and peritraumatic dissociation in HCPs.

Methods: This was a cross-sectional study in 21 ICUs in France between April 20, 2020, and May 21, 2020. The Hospital Anxiety and Depression Scale and the Peritraumatic Dissociative Experience Questionnaire were used. Factors independently associated with reported symptoms of mental health disorders were identified.

Measurements and Main Results: The response rate was 67%, with 1,058 respondents (median age 33 yr; 71% women; 68% nursing staff). The prevalence of symptoms of anxiety, depression, and peritraumatic dissociation was 50.4%, 30.4%, and 32%, respectively, with the highest rates in nurses. By multivariable analysis, male sex was independently associated with lower prevalence of symptoms of anxiety, depression, and peritraumatic dissociation (odds ratio of 0.58 [95% confidence interval, 0.42–0.79], 0.57 [95% confidence interval, 0.39–0.82], and 0.49 [95% confidence interval, 0.34–0.72], respectively). HCPs working in non–university-affiliated hospitals and nursing assistants were at high risk of symptoms of anxiety and peritraumatic dissociation. Importantly, we identified the following six modifiable determinants of symptoms of mental health disorders: fear of being infected, inability to rest, inability to care for family, struggling with difficult emotions, regret about the restrictions in visitation policies, and witnessing hasty end-of-life decisions.

Conclusions: HCPs experience high levels of psychological burden during the COVID-19 pandemic. Hospitals, ICU directors, and ICU staff must devise strategies to overcome the modifiable determinants of adverse mental illness symptoms.

Correction to: Evaluating a screener to quantify PTSD risk using emergency care information: a proof of concept study

An amendment to this paper has been published and can be accessed via the original article.