Samuel A. McLean

Chronic pain after traumatic injury likely reflects interacting processes rather than isolated recovery pathways, yet longitudinal evidence examining the joint development of pain, sleep disturbance, and hyperarousal from the acute postinjury stage is limited. We analysed data from the AURORA cohort of emergency department trauma survivors recruited within 72 hours of injury (n = 2834) to characterise trajectories of pain, sleep discontinuity, and hyperarousal over 12 months, identify shared multitrajectory profiles, and examine factors associated with adverse outcomes. Smartphone-based surveys assessed symptoms repeatedly from 1 to 5 days postinjury to one year. Group-based trajectory modelling identified 5 pain (n = 1635), 6 sleep discontinuity (n = 1635), and 5 hyperarousal (n = 1820) trajectories, ranging from rapid recovery to persistent high symptoms. Persistent high pain occurred in 35.1% of participants, while 23.5% followed persistently high or worsening sleep trajectories and 27.2% followed high hyperarousal trajectories. Multitrajectory modelling (n = 1800) identified 6 joint profiles, with 10.6% experiencing persistently high symptoms across all domains and 25.7% showing medium symptom levels. Older age and higher pretrauma pain were associated with adverse trajectories, whereas resilience and better prior mental health were protective. Baseline sleep discontinuity showed the strongest association with persistent high pain (adjusted odds ratio 3.31; 95% confidence interval 2.61-4.18), while physical activity was associated with a lower likelihood of medium-to-high pain trajectories. Female sex and lower income were associated with the most adverse multidomain profile. These findings highlight early postinjury sleep disruption, hyperarousal, and pain as interconnected processes. Targeting multiple domains may offer a promising strategy to prevent chronic pain after injury.

Background: Outcomes after mild traumatic brain injury (mTBI) without neuroimaging or blood biomarker abnormalities are poorly characterized. We evaluated 6-month outcomes in this population. Methods: We analyzed prospective TRACK-TBI data from 18 US Level I trauma centers between 2014 and 2018. Participants aged 17 years or older presented within 24 hours of injury with a Glasgow Coma Scale score of 15 and clinical signs of mTBI. Patients with negative head CT and MRI and non-elevated glial fibrillary acidic protein and ubiquitin C-terminal hydrolase-L1 concentrations (CT−/MRI−/BNE) were compared with patients who had positive imaging or elevated biomarkers and with orthopedic trauma controls. The primary outcome was incomplete functional recovery at 6 months, defined as a Glasgow Outcome Scale–Extended score below 8. Secondary outcomes were persistent post-concussive symptoms, psychological distress, and impaired quality of life. Findings: We included 578 patients with mTBI (98 CT−/MRI−/BNE and 480 imaging-positive or biomarker-positive) and 168 orthopedic controls. In the CT−/MRI−/BNE group, median age was 27.5 years and 54% were male. At 6 months, 65% had incomplete all-cause functional recovery, 43% had persistent post-concussive symptoms, 30% had psychological distress, and 37% had impaired quality of life. Outcomes were similar to or worse than those in imaging-positive or biomarker-positive patients and worse than those in orthopedic controls. Pre-injury psychiatric history was associated with incomplete TBI-attributable recovery (adjusted odds ratio [aOR] 3.2, 95% CI 1.2–9.1), persistent symptoms (3.1, 1.1–8.4), psychological distress (7.2, 2.1–24.0), and impaired quality of life (9.3, 2.9–30.0). Interpretation: Negative CT, MRI, and acute blood biomarker findings do not identify a uniformly low-risk mTBI population. Pre-injury psychiatric history could inform emergency-department risk stratification and targeted post-discharge follow-up.

The Momentary Posttraumatic Stress Disorder Checklist for DSM‐5 (PTSD; mPCL‐5) is a 10‐item ecological momentary assessment (EMA) version of the PTSD Checklist for DSM ‐5 designed to capture momentary PTSD symptoms. Prior work supports its reliability and validity but is limited by small samples, short EMA periods, and untested stability across time and participants. The present study examined within‐ and between‐person reliability, convergent and discriminant validity, factor structure, and measurement invariance across time and participants. The sample comprised 64 women who presented to emergency care after sexual assault ( M _age = 25.80 years, 54.7% White) who completed EMA surveys over 6 weeks. Total mPCL‐5 scores demonstrated good within‐person, ω = .88, and excellent between‐person, ω = .96, reliability. Moderate‐to‐strong correlations between mPCL‐5 total and subscale scores and negative affect, r s = .41–.88, p s < .001, evidenced convergent validity. Mostly nonsignificant positive affect associations suggested discriminant validity. Confirmatory factor analysis results supported a four‐factor structure as the best‐fitting model across levels, CFI = .94, TLI = .91, RMSEA = .072, although a one‐factor model was also appropriate for between‐person analyses. Measurement invariance analyses indicated stable loadings, B s = 0.41–0.55, and intercepts, B s = 1.51–2.18, across time and participants, with minimal between‐time variance (< .002). Findings support mPCL‐5 use in EMA research for monitoring short‐term PTSD symptom change, identifying high‐risk periods, and evaluating intervention responsiveness.

Objective: Polygenic risk scores (PRS) for posttraumatic stress disorder (PTSD) often account for a low amount of variance. Ancestry-related differences in PRS scale and variance limit cross-group comparisons. This methodological challenge further complicates gene-by-environment (GxE) analyses, given that socioenvironmental exposures are inequitably distributed across ethnoracial groups. We constructed an ancestry-calibrated polygenic risk score (AC-PRS) for PTSD in the largest longitudinal study of trauma survivors to date and investigated GxE interactions. Method: Recent trauma survivors (N=1,801) provided a blood specimen for genotyping. Six PTSD trajectories were previously identified from PTSD Checklist for DSM-5 (PCL-5) scores at 2-weeks, 8-weeks, 3-months, and 6-months post-trauma. Greenspace (normalized difference vegetation index [NDVI) and socioeconomic disadvantage (area deprivation index [ADI]) were derived from residential addresses. Logistic regressions examined interactions between newly developed AC-PRS and neighborhood factors on trajectories after adjusting for sociodemographic and trauma-related covariates. Secondary linear models considered GxE interactions on 6-month PCL-5 scores. Results: AC-PRS performed well across ethnoracial groups, explaining significant variability in PTSD trajectories (R ² =.053). ADI moderated the association between AC-PRS and the likelihood of assignment in a high nonremitting trajectory of PTSD symptoms and severity of symptoms at 6-months ( ps < .05). There were no NDVI x AC-PRS interactions in any models. Conclusions: AC-PRS captures genetic risk for PTSD in admixed trauma survivors, demonstrating good discrimination between nonremitting and resilient courses of PTSD. However, neighborhood disadvantage may modify utility of PRS for PTSD, warranting careful consideration when applying these scores across contexts.

Background: Adverse posttraumatic neuropsychiatric sequelae are common after trauma. Early identification of individuals at risk for these outcomes could enable the deployment of preventive interventions to survivors at greatest risk. Smartphone keystroke biomarkers show promise in identifying individuals with neuropsychiatric symptoms; however, to our knowledge, no research has examined whether they can be used to identify symptoms in the aftermath of trauma. Objective: This study evaluates whether passively collected keystroke data from smartphone use in daily life could identify individuals with high symptom levels, as well as worsening or recovery of symptoms, after trauma exposure. Methods: Data from a diverse cohort of individuals presenting to 27 emergency departments after trauma were analyzed. Inclusion criteria were presenting to the emergency department within 72 hours of trauma, age 18-75, and the ability to speak and read English. Exclusion criteria were solid organ injury, significant hemorrhage, operative intervention, or likely admission for over 72 hours. Participants installed an app that passively collected keystroke data during use of any app on their smartphone, beginning in the emergency department. Participants also completed serial symptom assessments over 8 weeks after trauma exposure. Results: A total of 3445 patients met study criteria, provided informed consent, and completed assessments in the emergency department. Of these, 1072 (mean age 40, SD 13; 616/1072, 57.46%, women; 565/1072, 52.71% non–Hispanic Black) installed the app on their Android smartphone and completed the 8-week assessment and were therefore included in analyses. Keystroke biomarkers related to typing speed, identified using bivariate linear mixed models controlling for false discovery rates, were associated with elevated pain, reexperiencing, and mental fatigue (absolute values of rs=0.22-0.25, Ps=.02). Separate change-of-operation and scrolling keystroke biomarkers were associated with increased reexperiencing symptoms (r=0.18, P=.047) and mental fatigue (rs=0.18-0.19, Ps=.031-.047). Further, changes in specific keystroke biomarkers were associated with worsening or recovery of pain (rs=0.07-0.10, Ps=.02), somatic symptoms (rs=0.02, Ps=.02), mental fatigue (rs=0.02-0.04, Ps=.02), sleep disturbance (absolute rs=0.07-0.09, Ps=.02), reexperiencing (rs=0.02-0.04, Ps=.02), and hyperarousal (rs=0.02-0.04, Ps=.02). Conclusions: In general, slower typing and scrolling speeds were associated with higher symptom levels, with small to medium effect sizes. Keystroke data passively collected via smartphone use may help identify individuals with significant or changing posttraumatic symptoms. Future research should continue to explore these keystroke biomarkers and whether they can be leveraged to connect vulnerable trauma survivors to appropriate services. Overall, these results add to the literature, indicating that passively collected keystroke data may help identify individuals with neuropsychiatric symptoms or changes and are, to our knowledge, the first to test whether keystroke biomarkers are useful in the aftermath of trauma. This represents a critical period during which preventive interventions could be deployed to reduce the long-term burden of trauma-related sequelae.

Background: Anxiety sensitivity (AS), defined as the fear of anxious arousal, is a promising therapeutic target for reducing posttraumatic stress disorder (PTSD) symptom development after trauma exposure. Initial research suggests that smartphone-based AS interventions may be acceptable to sexual assault survivors at risk for PTSD symptoms and effective for symptom reduction, but only small one-arm proof-of-concept studies have been conducted. Objective: The goal of this study was to extend prior proof-of-concept work by conducting a pilot randomized controlled trial. The aims were to evaluate intervention efficacy, AS and PTSD symptom change, the acceptability and credibility of a control intervention, and the feasibility of a larger randomized controlled trial. Methods: A total of 60 women with high AS presenting for emergency care after sexual assault were recruited and randomized to either the AS intervention or a control condition, and they were followed up with for over 6 months via remote self-report questionnaires. Results: The findings indicated that the study population is at risk and in need of intervention: 88.8% (40/45) and 80.6% (29/36) of women sexual assault survivors in the sample met the criteria for probable PTSD at 7 weeks and 6 months post assault, respectively. Most (16/27, 59.3%) individuals receiving the AS intervention who completed it rated it as acceptable (eg, 18/21, 85.7% reported that the treatment was helpful). Early within-group reductions were not statistically significant, but by month 6, statistically significant reductions in AS and PTSD symptoms were observed in both conditions. Recruitment and retention data supported the feasibility of the study design, although some suggestions were noted for future research (eg, improving intervention and ecological momentary assessment compliance). Conclusions: This pilot study replicated the proof of concept and acceptability of a novel smartphone-based intervention targeting AS delivered to women sexual assault survivors presenting to emergency care. Intervention completion was in line with or better than traditional therapy but remained a challenge. Our selected control condition demonstrated a larger effect than expected, and we were unable to track its initiation or completion, causing difficulty in drawing conclusions. Overall, the results highlight the need for additional research.

Residential segregation is associated with differential exposure to air pollution. Hippocampus structure and function are highly susceptible to pollutants and associated with posttraumatic stress disorder (PTSD) development. Therefore, we investigated associations between residential segregation, air pollutants, hippocampal neurobiology, and PTSD in recent trauma survivors. Participants (N = 278; 34% non-Hispanic white, 46% Non-Hispanic Black, 16% Hispanic) completed multimodal neuroimaging two weeks after trauma. Yearly averages of air pollutants (PM_2.5 and NO₂) and racial/economic segregation (Index of Concentration at the Extremes) were derived from each participant's address. Linear models assessed if air pollutants mediated associations between segregation and hippocampal volume, threat reactivity, or parahippocampal cingulum fractional anisotropy (FA) after covarying for age, sex, income, and 2-week PTSD symptoms. Further models evaluated if pollutants or segregation prospectively predicted PTSD symptoms six months post-trauma. We found that non-Hispanic Black participants lived in neighborhoods with significantly greater segregation and air pollution compared to Hispanic and non-Hispanic white participants (ps < 0.001). PM_2.5 concentration was positively correlated with threat reactivity (r(276) = 0.16, p < 0.006), while NO₂ concentration was positively correlated with hippocampus volume (r(276) = 0.17, p < 0.005) and negatively correlated with white matter tract FA (r(276) = -0.18, p < 0.003). There was a significant indirect effect of NO₂ between segregation and FA values (β = 0.08, 95% CI[0.01, 0.15]), and an indirect effect of PM_2.5 between segregation and threat reactivity (β = -0.08, 95% CI[-0.14, -0.01]). There was no direct effect of segregation on hippocampal features. Pollutants and segregation were not associated with PTSD symptoms. In conclusion, residential segregation is associated with greater air pollution exposure, which is in turn associated with variability in hippocampal features among recent trauma survivors. Further research is needed to assess relationships between other environmental factors and trauma and stress-related disorders.

Background: Systemic inflammation is hypothesized to contribute to post-traumatic stress disorder (PTSD) vulnerability. Few studies have examined inflammation shortly after trauma as a predictor of later PTSD symptoms. We examined whether inflammation from the emergency department (ED) post-trauma is associated with PTSD symptom severity over the following 6 months. Methods: Our sample included 742 AURORA participants, a longitudinal cohort of patients in 29 EDs across the United States after a traumatic stressor, followed up to 6 months. Plasma cytokines were assessed from a study blood draw in the ED: an inflammatory index (standardized sum of generally pro-inflammatory markers interleukin [IL]-6, IL-8, tumor necrosis factor alpha [TNF-α], interferon gamma [IFN-γ]), and generally anti-inflammatory IL-10. PTSD symptoms were self-reported at 2 weeks, 8 weeks, 3 months, and 6 months post-ED. Covariate-adjusted repeated-measures regressions estimated associations between inflammation and PTSD symptoms, overall and sex-stratified. Results: Among 742 participants (age m = 40.0 [13.7]; 479 [64.6%] female), PTSD symptoms were elevated then modestly decreased over follow-up. Higher ED inflammation was associated with higher PTSD symptoms across follow-up (standardized symptoms β = 0.05, 95% CI: 0.01–0.09), adjusted for potential confounders. Higher pro-inflammatory index levels and IL-6, IL-8, and TNF-α were associated with higher PTSD symptoms in males only, while higher IL-10 was associated with higher PTSD symptoms in females only. Conclusions: Pro-inflammatory levels shortly after traumatic stress are associated with heightened PTSD symptoms, particularly among males. Inflammatory markers may prove useful additions to prediction models for PTSD following trauma, with attention to sex differences.

Background: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classification is essential for effective treatment and improved patient outcomes. Sleep/wake behavior has emerged as a potential biomarker for TBI classification, yet the optimal time window in which to identify sleep/wake changes after TBI remains unclear. Methods: We evaluated daily longitudinal sleep/wake data from a prospective cohort of more than 2,000 emergency department patients with and without blood biomarker-documented TBI (Glial Fibrillary Acidic Protein - GFAP $ > 268 \frac{pg}{ml}$). We utilized a deep learning model to identify the impact of time from trauma and duration of data collection on the model's ability to distinguish between TBI-positive (TBI+) and TBI-negative (TBI-) cases. Results: Our analysis showed that sleep/wake data from the first 7 days after TBI most accurately identified TBI. Sleep-wake data from the first 7, 14, and 21 days after trauma achieved sensitivity/specificity of 81%/25%, 40%/66%, and 45%/58%, respectively. F1 scores of deep learning models developed from the first 7, 14, and 21 days were 22%, 21%, and 20%, respectively. Conclusions: The results suggest that early sleep/wake data has promise for assisting with TBI identification. Significance: In the future, the incorporation of sleep/wake derived biomarkers into TBI identification tools could assist in the identification of individuals with potential TBI for further screening and intervention.