Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: A machine learning multicenter cohort study

Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method

Background: Approximately 70% of individuals globally experience at least one traumatic event in their lifetimes, potentially leading to posttraumatic stress disorder (PTSD). Understanding the development of PTSD and devising effective prevention and treatment strategies are crucial. This proof-of-concept study aimed to design a concise tree-based adaptive test using the Classification and Regression Trees (CART) framework to predict PTSD development.Methods: Utilizing data from a longitudinal neuroimaging study, adult trauma survivors were enrolled from local hospital emergency departments within 48 h of experiencing trauma. Participants who completed psychological evaluations within 2 weeks post-trauma and a PTSD diagnosis assessment at 3 months were included in the analytic sample (n = 143). A total of 131 features including demographic, trauma-related, and behavioural and clinical symptoms were collected during this initial two-week post-trauma period. The performance of the CART model was benchmarked against two of the most powerful and widely used machine learning algorithms in the field, Random Forest (RF) and Gradient Boosting (GB) models.Results: The CART model, which incorporates just three critical questions from established assessments, predicted PTSD development with performance closely matched to that of the RF and GB models. The CART model achieved an accuracy of 0.641 and an AUC of 0.663, which showed only slightly worse performance compared to the RF and GB models. Its efficiency in utilizing a minimal set of questions for prediction highlights its potential for practical application in early PTSD detection and intervention strategies.Conclusion: The CART framework demonstrates a streamlined and efficient method for predicting PTSD onset in trauma survivors. While showing promise for practical application, further validation and refinement are necessary to enhance its predictive performance and establish its broader utility in early intervention strategies.

Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis

This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We conducted a systematic review and random-effect meta-analysis summarizing predictive model development and validation studies using machine learning in diverse samples to predict PTSD. Model performances were pooled using the area under the curve (AUC) with a 95% confidence interval (CI). Heterogeneity in each meta-analysis was measured using I2. The risk of bias in each study was appraised using the PROBAST tool. 48% of the 23 included studies had a high ROB, and the remaining had unclear. Tree-based models were the primarily used algorithms and showed promising results in predicting PTSD outcomes for various groups, as indicated by their pooled AUCs: military incidents (0.745), sexual or physical trauma (0.861), natural disasters (0.771), medical trauma (0.808), firefighters (0.96), and alcohol-related stress (0.935). However, the applicability of these findings is limited due to several factors, such as significant variability among the studies, high and unclear risks of bias, and a shortage of models that maintain accuracy when tested in new settings. Researchers should follow the reporting standards for AI/ML and adhere to the PROBAST guidelines. It is also essential to conduct external validations of these models to ensure they are practical and relevant in real-world settings.

A systematic review of machine learning findings in PTSD and their relationships with theoretical models

In recent years, the application of machine learning (ML) techniques in research on the prediction of post-traumatic stress disorder (PTSD) has increased. However, concerns regarding the clinical relevance and generalizability of ML findings hamper their implementation by clinicians and researchers. Here in this systematic review we examined (1) the extent to which pre-, peri- and post-traumatic risk factors identified using ML approaches coincide with the theoretical understanding of the disorder; (2) whether new insights were gained through ML techniques; and (3) whether ML findings, combined with previous research, enable an integrative model of PTSD risk encompassing both predictor categories and their theoretical relevance. We reviewed ML studies on PTSD risk factors in PubMed, Web of Science and Scopus. Studies were included if they specified when predictors and PTSD symptoms were collected in temporal relation to the traumatic event. A total of 30 studies with 12,908 participants (mean age 36.5 years) were included. After extracting the 15 most important predictors from all studies, we categorized them into pre-, peri- and post-trauma exposure predictors and examined their associations with established theoretical models of PTSD. Many studies exhibited a risk of bias, assessed using the prediction model risk of bias assessment tool (PROBAST). However, we found overlaps in identified predictors across studies, a concordance between data-driven results and theory-driven research, and underexplored predictors identified through ML. We propose an integrative model of PTSD risk that incorporates both data-driven and theory-driven findings and discuss future directions. We emphasize the importance of standards on how to apply and report ML approaches for mental health.

Towards accurate screening and prevention for PTSD (2-ASAP): protocol of a longitudinal prospective cohort study

BACKGROUND

Effective preventive interventions for PTSD rely on early identification of individuals at risk for developing PTSD. To establish early post-trauma who are at risk, there is a need for accurate prognostic risk screening instruments for PTSD that can be widely implemented in recently trauma-exposed adults. Achieving such accuracy and generalizability requires external validation of machine learning classification models. The current 2-ASAP cohort study will perform external validation on both full and minimal feature sets of supervised machine learning classification models assessing individual risk to follow an adverse PTSD symptom trajectory over the course of 1 year. We will derive these models from the TraumaTIPS cohort, separately for men and women.

METHOD

The 2-ASAP longitudinal cohort will include N = 863 adults (N = 436 females, N = 427 males) who were recently exposed to acute civilian trauma. We will include civilian victims of accidents, crime and calamities at Victim Support Netherlands; and who were presented for medical evaluation of (suspected) traumatic injuries by emergency transportation to the emergency department. The baseline assessment within 2 months post-trauma will include self-report questionnaires on demographic, medical and traumatic event characteristics; potential risk and protective factors for PTSD; PTSD symptom severity and other adverse outcomes; and current best-practice PTSD screening instruments. Participants will be followed at 3, 6, 9, and 12 months post-trauma, assessing PTSD symptom severity and other adverse outcomes via self-report questionnaires.

DISCUSSION

The ultimate goal of our study is to improve accurate screening and prevention for PTSD in recently trauma-exposed civilians. To enable future large-scale implementation, we will use self-report data to inform the prognostic models; and we will derive a minimal feature set of the classification models. This can be transformed into a short online screening instrument that is user-friendly for recently trauma-exposed adults to fill in. The eventual short online screening instrument will classify early post-trauma which adults are at risk for developing PTSD. Those at risk can be targeted and may subsequently benefit from preventive interventions, aiming to reduce PTSD and relatedly improve psychological, functional and economic outcomes.

Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review

BACKGROUND

Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.

OBJECTIVE

This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs.

METHODS

Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs.

RESULTS

A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms.

CONCLUSIONS

The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.

Racial discrimination increases the risk for nonremitting posttraumatic stress disorder symptoms in traumatically injured Black individuals living in the United States

Traumatic, life-threatening events are experienced commonly among the general U.S. population, yet Black individuals in the United States (i.e., Black Americans) exhibit higher prevalence rates of posttraumatic stress disorder (PTSD) and more severe symptoms than other populations. Although empirical research has noted a range of symptom patterns that follow traumatic injury, minimal work has examined the role of racial discrimination in relation to PTSD symptom trajectories. The current study assessed racial discrimination and PTSD symptom trajectories at 6 months postinjury across two separate samples of traumatically injured Black Americans (i.e. emergency department (ED)-discharged and hospitalized). Identified PTSD symptom trajectories largely reflect those previously reported (i.e., ED: nonremitting, moderate, remitting, and resilient; hospitalized: nonremitting, delayed, and resilient), although the resilient trajectory was less represented than expected given past research (ED: 55.8%, n = 62; hospitalized: 46.9%, n = 38). Finally, higher racial discrimination was associated with nonremitting, ED: relative risk ratio (RR) = 1.32, hospitalized: RR = 1.23; moderate, ED: RR = 1.18; and delayed, hospitalized: RR = 1.26, PTSD symptom trajectories. Overall, the current findings not only emphasize the inimical effects of racial discrimination but also demonstrate the unique ways in which race-related negative events can impact PTSD symptom levels and recovery across time.

Applications of artificial intelligence-machine learning for detection of stress: a critical overview

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.

Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning

There are significant challenges to identifying which individuals require intervention following exposure to trauma, and a need for strategies to identify and provide individuals at risk for developing PTSD with timely interventions. The present study seeks to identify a minimal set of trauma-related symptoms, assessed during the weeks following traumatic exposure, that can accurately predict PTSD. Participants were 2185 adults (Mean age=36.4 years; 64% women; 50% Black) presenting for emergency care following traumatic exposure. Participants received a 'flash survey' with 6-8 varying symptoms (from a pool of 26 trauma symptoms) several times per week for eight weeks following the trauma exposure (each symptom assessed ∼6 times). Features (mean, sd, last, worst, peak-end scores) from the repeatedly assessed symptoms were included as candidate variables in a CART machine learning analysis to develop a pragmatic predictive algorithm. PTSD (PCL-5 ≥38) was present for 669 (31%) participants at the 8-week follow-up. A classification tree with three splits, based on mean scores of nervousness, rehashing, and fatigue, predicted PTSD with an Area Under the Curve of 0.836. Findings suggest feasibility for a 3-item assessment protocol, delivered once per week, following traumatic exposure to assess and potentially facilitate follow-up care for those at risk.

The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis

Introduction

Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively applied in the realm of mental health. Therefore, our present undertaking seeks to provide a comprehensive assessment of studies employing ML methods that use diverse data modalities on the classification of people with PTSD.

Methods and analysis

In pursuit of pertinent studies, we will search both English and Chinese databases from January 2000 to May 2022. Two researchers will independently conduct screening, extract data and assess study quality. We intend to employ the assessment framework introduced by Luis Francisco Ramos-Lima in 2020 for quality evaluation. Rate, standard error and 95% CIs will be utilized for effect size measurement. A Cochran's Q test will be applied to assess heterogeneity. Subgroup and sensitivity analysis will further elucidate the source of heterogeneity and funnel plots and Egger's test will detect publication bias.

Ethics and dissemination

This systematic review and meta-analysis does not encompass patient interactions or engagements with healthcare providers. The outcomes of this research will be disseminated through scholarly channels, including presentations at scientific conferences and publications in peer-reviewed journals.PROSPERO registration number CRD42023342042.

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.

Advances in the molecular neurobiology of posttraumatic stress disorder from global contexts: A systematic review of longitudinal studies

Trauma exposure is prevalent globally and is a defining event for the development of posttraumatic stress disorder (PTSD), characterised by intrusive thoughts, avoidance behaviours, hypervigilance and negative alterations in cognition and mood. Exposure to trauma elicits a range of physiological responses which can interact with environmental factors to confer relative risk or resilience for PTSD. This systematic review summarises the findings of longitudinal studies examining biological correlates predictive of PTSD symptomology. Databases (Pubmed, Scopus and Web of Science) were systematically searched using relevant keywords for studies published between 1 January 2021 and 31 December 2022. English language studies were included if they were original research manuscripts or meta-analyses of cohort investigations that assessed longitudinal relationships between one or more molecular-level measures and either PTSD status or symptoms. Eighteen of the 1,042 records identified were included. Studies primarily included military veterans/personnel, individuals admitted to hospitals after acute traumatic injury, and women exposed to interpersonal violence or rape. Genomic, inflammation and endocrine measures were the most commonly assessed molecular markers and highlighted processes related to inflammation, stress responding, and learning and memory. Quality assessments were done using the Systematic Appraisal of Quality in Observational Research, and the majority of studies were rated as being of high quality, with the remainder of moderate quality. Studies were predominantly conducted in upper-income countries. Those performed in low- and middle-income countries were not broadly representative in terms of demographic, trauma type and geographic profiles, with three out of the four studies conducted assessing only female participants, rape exposure and South Africa, respectively. They also did not generate multimodal data or use machine learning or multilevel modelling, potentially reflecting greater resource limitations in LMICs. Research examining molecular contributions to PTSD does not adequately reflect the global burden of the disorder.

Machine Learning and the Digital Measurement of Psychological Health

Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.

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.

Derivation and Validation of a Brief Emergency Department-Based Prediction Tool for Posttraumatic Stress After Motor Vehicle Collision

STUDY OBJECTIVE

To derive and initially validate a brief bedside clinical decision support tool that identifies emergency department (ED) patients at high risk of substantial, persistent posttraumatic stress symptoms after a motor vehicle collision.

METHODS

Derivation (n=1,282, 19 ED sites) and validation (n=282, 11 separate ED sites) data were obtained from adults prospectively enrolled in the Advancing Understanding of RecOvery afteR traumA study who were discharged from the ED after motor vehicle collision-related trauma. The primary outcome was substantial posttraumatic stress symptoms at 3 months (Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders-5 ≥38). Logistic regression derivation models were evaluated for discriminative ability using the area under the curve and the accuracy of predicted risk probabilities (Brier score). Candidate posttraumatic stress predictors assessed in these models (n=265) spanned a range of sociodemographic, baseline health, peritraumatic, and mechanistic domains. The final model selection was based on performance and ease of administration.

RESULTS

Significant 3-month posttraumatic stress symptoms were common in the derivation (27%) and validation (26%) cohort. The area under the curve and Brier score of the final 8-question tool were 0.82 and 0.14 in the derivation cohort and 0.76 and 0.17 in the validation cohort.

CONCLUSION

This simple 8-question tool demonstrates promise to risk-stratify individuals with substantial posttraumatic stress symptoms who are discharged to home after a motor vehicle collision. Both external validation of this instrument, and work to further develop more accurate tools, are needed. Such tools might benefit public health by enabling the conduct of preventive intervention trials and assisting the growing number of EDs that provide services to trauma survivors aimed at promoting psychological recovery.

Oxytocin vs. placebo effects on intrusive memory consolidation using a trauma film paradigm: a randomized, controlled experimental study in healthy women

Oxytocin administration during a trauma analogue has been shown to increase intrusive memories, which are a core symptom of post-traumatic stress disorder (PTSD). However, it is unknown whether oxytocin influences the acquisition or the consolidation of the trauma. The current study investigates the effect of the activation of the oxytocin system during the consolidation of an analogue trauma on the formation of intrusive memories over four consecutive days and whether this effect is influenced by individual neurobiological, genetic, or psychological factors. We conducted a randomized double-blind placebo-controlled study in 217 healthy women. They received either a single dose of intranasal oxytocin (24 IU) or placebo after exposure to a trauma film paradigm, which reliably induces intrusive memories. We used a general random forest to examine a potential heterogeneous treatment effect of oxytocin on the consolidation of intrusive memories. Furthermore, we used a poisson regression to examine whether salivary alpha amylase activity (sAA) as a marker of noradrenergic activity and cortisol response to the film, polygenic risk score (PRS) for psychiatric disorders, and psychological factors influence the number of intrusive memories. We found no significant effect of oxytocin on the formation of intrusive memories (F(2, 543.16) = 0.75, p = 0.51, ηp2 = 0.00) and identified no heterogeneous treatment effect. We replicated previous associations of the PRS for PTSD, sAA and the cortisol response on intrusive memories. We further found a positive association between high trait anxiety and intrusive memories, and a negative association between the emotion regulation strategy reappraisal and intrusive memories. Data of the present study suggest that the consolidation of intrusive memories in women is modulated by genetic, neurobiological and psychological factors, but is not influenced by oxytocin. Trial registration: NCT03875391.

Potential peripheral biomarkers associated with the emergence and presence of posttraumatic stress disorder symptomatology: A systematic review

BACKGROUND

Evidence suggests posttraumatic stress disorder (PTSD) involves an interplay between psychological manifestations and biological systems. Biological markers of PTSD could assist in identifying individuals with underlying dysregulation and increased risk; however, accurate and reliable biomarkers are yet to be identified.

METHODS

A systematic review following the PRISMA guidelines was conducted. Databases included EMBASE, MEDLINE, and Cochrane Central. Studies from a comprehensive 2015 review (Schmidt et al., 2015) and English language papers published subsequently (between 2014 and May 2022) were included. Forty-eight studies were eligible.

RESULTS

Alterations in neuroendocrine and immune markers were most commonly associated with PTSD symptoms. Evidence indicates PTSD symptoms are associated with hypothalamic-pituitary-adrenal axis dysfunction as represented by low basal cortisol, a dysregulated immune system, characterized by an elevated pro-inflammatory state, and metabolic dysfunction. However, a considerable number of studies neglected to measure sex or prior trauma, which have the potential to affect the biological outcomes of posttraumatic stress symptoms. Mixed findings are indicative of the complexity and heterogeneity of PTSD and suggest the relationship between allostatic load, biological markers, and PTSD remain largely undefined.

CONCLUSIONS

In addition to prospective research design and long-term follow up, it is imperative future research includes covariates sex, prior trauma, and adverse childhood experiences. Future research should include exploration of biological correlates specific to PTSD symptom domains to determine whether underlying processes differ with symptom expression, in addition to subclinical presentation of posttraumatic stress symptoms, which would allow for greater understanding of biomarkers associated with disorder risk and assist in untangling directionality.

Machine learning prediction of posttraumatic stress disorder trajectories following traumatic injury: Identification and validation in two independent samples

Due to its heterogeneity, the prediction of posttraumatic stress disorder (PTSD) development after traumtic injury is difficult. Recent machine learning approaches have yielded insight into predicting PTSD symptom trajectories. Using data collected within 1 month of traumatic injury, we applied eXtreme Gradient Boosting (XGB) to classify admitted and discharged patients (hospitalized, n = 192; nonhospitalized, n = 214), recruited from a Level 1 trauma center, according to PTSD symptom trajectories. Trajectories were identified using latent class mixed models on PCL-5 scores collected at baseline, 1-3 months posttrauma, and 6 months posttrauma. In both samples, nonremitting, remitting, and resilient PTSD symptom trajectories were identified. In the admitted patient sample, a unique delayed trajectory emerged. Machine learning classifiers (i.e., XGB) were developed and tested on the admitted patient sample and externally validated on the discharged sample with biological and clinical self-report baseline variables as predictors. For external validation sets, prediction was fair for nonremitting versus other trajectories, areas under the curve (AUC = .70); good for nonremitting versus resilient trajectories, AUCs = .73-.76; and prediction failed for nonremitting versus remitting trajectories, AUCs = .46-.48. However, poor precision (< .57) across all models suggests limited generalizability of nonremitting symptom trajectory prediction from admitted to discharged patient samples. Consistency in symptom trajectory identification across samples supports prior studies on the stability of PTSD symptom trajectories following trauma exposure; however, continued work and replication with larger samples are warranted to understand overlapping and unique predictive features of PTSD in different traumatic injury populations.

Traumatic stress symptoms in family caregivers of patients with acute leukaemia: protocol for a multisite mixed methods, longitudinal, observational study

INTRODUCTION

The diagnosis, progression or recurrence of cancer is often highly traumatic for family caregivers (FCs), but systematic assessments of distress and approaches for its prevention and treatment are lacking. Acute leukaemia (AL) is a life-threatening cancer of the blood, which most often presents acutely, requires intensive treatment and is associated with severe physical symptoms. Consequently, traumatic stress may be common in the FCs of patients with AL. We aim to determine the prevalence, severity, longitudinal course and predictors of traumatic stress symptoms in FCs of patients with AL in the first year after diagnosis, and to understand their lived experience of traumatic stress and perceived support needs.

METHODS AND ANALYSIS

This two-site longitudinal, observational, mixed methods study will recruit 223 adult FCs of paediatric or adult patients newly diagnosed with AL from two tertiary care centres. Quantitative data will be collected from self-report questionnaires at enrolment, and 1, 3, 6, 9 and 12 months after admission to hospital for initial treatment. Quantitative data will be analysed using descriptive and machine learning approaches and a multilevel modelling (MLM) approach will be used to confirm machine learning findings. Semi-structured qualitative interviews will be conducted at 3, 6 and 12 months and analysed using a grounded theory approach.

ETHICS AND DISSEMINATION

This study is funded by the Canadian Institutes of Health Research (CIHR number PJT 173255) and has received ethical approval from the Ontario Cancer Research Ethics Board (CTO Project ID: 2104). The data generated have the potential to inform the development of targeted psychosocial interventions for traumatic stress, which is a public health priority for high-risk populations such as FCs of patients with haematological malignancies. An integrated and end-of-study knowledge translation strategy that involves FCs and other stakeholders will be used to interpret and disseminate study results.

Acute stress reactivity and intrusive memory development: a randomized trial using an adjusted trauma film paradigm

Understanding the neurobiological and cognitive processes underlying the development of posttraumatic stress disorder (PTSD) and its specific symptoms may facilitate preventive intervention development. Severe traumatic stress and resulting biological stress system activations can alter contextual memory processes. This may provide a neurobiological explanation for the occurrence of intrusive memories following trauma. Investigating the associations between temporal aspects and individual variation in peri- and post-traumatic hypothalamic pituitary adrenal (HPA) axis and sympathetic nervous system (SNS) stress reactivity and memory processing may increase our understanding of intrusive symptom development. The experimental trauma film paradigm is commonly used for this purpose but lacks robust SNS and HPA axis activation. Here, we performed an RCT to investigate the effect of an adjusted trauma film paradigm containing an added brief psychosocial stressor on HPA and SNS stress reactivity throughout the experiment and intrusive memory frequency in the following week in healthy males (N = 63, mean age = 22.3). Secondary, we investigated effects on film-related declarative memory accuracy and intrusion-related characteristics, and associations between acute HPA and SNS stress reactivity, film-related memory, glucocorticoid receptor functioning and intrusion frequency and characteristics. Participants were randomized to the socially-evaluated cold pressor test (seCPT n = 29) or control condition (warm water n = 34) immediately prior to a trauma film. Linear Mixed Models revealed increased acute SNS and cortisol reactivity, lower recognition memory accuracy and more intrusions that were more vivid and distressing during the following week in the seCPT compared to control condition. Linear regression models revealed initial associations between cortisol and alpha amylase reactivity during the experimental assessment and subsequent intrusions, but these effects did not survive multiple comparison corrections. Thus, with this adjustment, we increased the translational value of the trauma film paradigm as it appears to elicit a stronger stress response that is likely more comparable to real-life trauma. The adapted paradigm may be useful to investigate individual variation in biological and cognitive processes underlying early post-trauma PTSD symptoms and could advance potential preventive interventions.

Intranasal oxytocin administration impacts the acquisition and consolidation of trauma-associated memories: a double-blind randomized placebo-controlled experimental study in healthy women

Intrusive memories are a hallmark symptom of post-traumatic stress disorder (PTSD) and oxytocin has been implicated in the formation of intrusive memories. This study investigates how oxytocin influences the acquisition and consolidation of trauma-associated memories and whether these effects are influenced by individual neurobiological and genetic differences. In this randomized, double-blind, placebo-controlled study, 220 healthy women received either a single dose of intranasal 24IU oxytocin or a placebo before exposure to a trauma film paradigm that solicits intrusive memories. We used a "general random forest" machine learning approach to examine whether differences in the noradrenergic and hypothalamic-pituitary-adrenal axis activity, polygenic risk for psychiatric disorders, and genetic polymorphism of the oxytocin receptor influence the effect of oxytocin on the acquisition and consolidation of intrusive memories. Oxytocin induced significantly more intrusive memories than placebo did (t(188.33) = 2.12, p = 0.035, Cohen's d = 0.30, 95% CI 0.16-0.44). As hypothesized, we found that the effect of oxytocin on intrusive memories was influenced by biological covariates, such as salivary cortisol, heart rate variability, and PTSD polygenic risk scores. The five factors that were most relevant to the oxytocin effect on intrusive memories were included in a Poisson regression, which showed that, besides oxytocin administration, higher polygenic loadings for PTSD and major depressive disorder were directly associated with a higher number of reported intrusions after exposure to the trauma film stressor. These results suggest that intranasal oxytocin amplifies the acquisition and consolidation of intrusive memories and that this effect is modulated by neurobiological and genetic factors. Trial registration: NCT03031405.

Sex-differential PTSD symptom trajectories across one year following suspected serious injury

BACKGROUND

Recent years have shown an increased application of prospective trajectory-oriented approaches to posttraumatic stress disorder (PTSD). Although women are generally considered at increased PTSD risk, sex and gender differences in PTSD symptom trajectories have not yet been extensively studied.

OBJECTIVE

To perform an in-depth investigation of differences in PTSD symptom trajectories across one-year post-trauma between men and women, by interpreting the general trends of trajectories observed in sex-disaggregated samples, and comparing within-trajectory symptom course and prevalence rates.

METHOD

We included N = 554 participants (62.5% men, 37.5% women) from a multi-centre prospective cohort of emergency department patients with suspected severe injury. PTSD symptom severity was assessed at 1, 3, 6, and 12 months post-trauma, using the Clinician-Administered PTSD Scale for DSM-IV. Latent growth mixture modelling on longitudinal PTSD symptoms was performed within the sex-disaggregated whole samples. Bayesian modelling with informative priors was applied for reliable model estimation, considering the imbalanced prevalence of the expected latent trajectories.

RESULTS

In terms of general trends, the same trajectories were observed for men and women, i.e. resilient, recovery, chronic symptoms and delayed onset. Within-trajectory symptom courses were largely comparable, but resilient women had higher symptoms than resilient men. Sex differences in prevalence rates were observed for the recovery (higher in women) and delayed onset (higher in men) trajectories. Model fit for the sex-disaggregated samples was better than for the whole sample, indicating preferred application of sex-disaggregation. Analyses within the whole sample led to biased estimates of overall and sex-specific trajectory prevalence rates.

CONCLUSIONS

Sex-disaggregated trajectory analyses revealed limited sex differences in PTSD symptom trajectories within one-year post-trauma in terms of general trends, courses and prevalence rates. The observed biased trajectory prevalence rates in the whole sample emphasize the necessity to apply appropriate statistical techniques when conducting sex-sensitive research.

Development and Validation of a Model to Predict Posttraumatic Stress Disorder and Major Depression After a Motor Vehicle Collision

IMPORTANCE

A substantial proportion of the 40 million people in the US who present to emergency departments (EDs) each year after traumatic events develop posttraumatic stress disorder (PTSD) or major depressive episode (MDE). Accurately identifying patients at high risk in the ED would facilitate the targeting of preventive interventions.

OBJECTIVES

To develop and validate a prediction tool based on ED reports after a motor vehicle collision to predict PTSD or MDE 3 months later.

DESIGN, SETTING, AND PARTICIPANTS

The Advancing Understanding of Recovery After Trauma (AURORA) study is a longitudinal study that examined adverse posttraumatic neuropsychiatric sequalae among patients who presented to 28 US urban EDs in the immediate aftermath of a traumatic experience. Enrollment began on September 25, 2017. The 1003 patients considered in this diagnostic/prognostic report completed 3-month assessments by January 31, 2020. Each patient received a baseline ED assessment along with follow-up self-report surveys 2 weeks, 8 weeks, and 3 months later. An ensemble machine learning method was used to predict 3-month PTSD or MDE from baseline information. Data analysis was performed from November 1, 2020, to May 31, 2021.

MAIN OUTCOMES AND MEASURES

The PTSD Checklist for DSM-5 was used to assess PTSD and the Patient Reported Outcomes Measurement Information System Depression Short-Form 8b to assess MDE.

RESULTS

A total of 1003 patients (median [interquartile range] age, 34.5 [24-43] years; 715 [weighted 67.9%] female; 100 [weighted 10.7%] Hispanic, 537 [weighted 52.7%] non-Hispanic Black, 324 [weighted 32.2%] non-Hispanic White, and 42 [weighted 4.4%] of non-Hispanic other race or ethnicity were included in this study. A total of 274 patients (weighted 26.6%) met criteria for 3-month PTSD or MDE. An ensemble machine learning model restricted to 30 predictors estimated in a training sample (patients from the Northeast or Midwest) had good prediction accuracy (mean [SE] area under the curve [AUC], 0.815 [0.031]) and calibration (mean [SE] integrated calibration index, 0.040 [0.002]; mean [SE] expected calibration error, 0.039 [0.002]) in an independent test sample (patients from the South). Patients in the top 30% of predicted risk accounted for 65% of all 3-month PTSD or MDE, with a mean (SE) positive predictive value of 58.2% (6.4%) among these patients at high risk. The model had good consistency across regions of the country in terms of both AUC (mean [SE], 0.789 [0.025] using the Northeast as the test sample and 0.809 [0.023] using the Midwest as the test sample) and calibration (mean [SE] integrated calibration index, 0.048 [0.003] using the Northeast as the test sample and 0.024 [0.001] using the Midwest as the test sample; mean [SE] expected calibration error, 0.034 [0.003] using the Northeast as the test sample and 0.025 [0.001] using the Midwest as the test sample). The most important predictors in terms of Shapley Additive Explanations values were symptoms of anxiety sensitivity and depressive disposition, psychological distress in the 30 days before motor vehicle collision, and peritraumatic psychosomatic symptoms.

CONCLUSIONS AND RELEVANCE

The results of this study suggest that a short set of questions feasible to administer in an ED can predict 3-month PTSD or MDE with good AUC, calibration, and geographic consistency. Patients at high risk can be identified in the ED for targeting if cost-effective preventive interventions are developed.

Psychiatry in the Digital Age: A Blessing or a Curse?

Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor-patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.