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.

Utility of Wrist-Wearable Data for Assessing Pain, Sleep, and Anxiety Outcomes After Traumatic Stress Exposure.

Importance: Adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure are common and have higher incidence among socioeconomically disadvantaged populations. Pain, depression, avoidance of trauma reminders, reexperiencing trauma, anxiety, hyperarousal, sleep disruption, and nightmares have been reported. Wrist-wearable devices with accelerometers capable of assessing 24-hour rest-activity characteristics are prevalent and may have utility in measuring these outcomes. Objective: To evaluate whether wrist-wearable devices can provide useful biomarkers for recovery after traumatic stress exposure. Design, Setting, and Participants: Data were analyzed from a diverse cohort of individuals seen in the emergency department after experiencing a traumatic stress exposure, as part of the Advancing Understanding of Recovery After Trauma (AURORA) study. Participants recruited from 27 emergency departments wore wrist-wearable devices for 8 weeks, beginning in the emergency department, and completed serial assessments of neuropsychiatric symptoms. A total of 19 019 patients were screened. Of these, 3040 patients met study criteria, provided informed consent, and completed baseline assessments. A total of 2021 provided data from wrist-wearable devices, completed the 8-week assessment, and were included in this analysis. The data were randomly divided into 2 equal parts (n = 1010) for biomarker identification and validation. Data were collected from September 2017 to January 2020, and data were analyzed from May 2020 to November 2022. Exposures: Participants were recruited for the study after experiencing a traumatic stress exposure (most commonly motor vehicle collision). Main Outcomes and Measures: Rest-activity characteristics were derived and validated from wrist-wearable devices associated with specific self-reported symptom domains at a point in time and changes in symptom severity over time. Results: Of 2021 included patients, 1257 (62.2%) were female, and the mean (SD) age was 35.8 (13.0) years. Eight wrist-wearable device biomarkers for symptoms of adverse posttraumatic neuropsychiatric sequelae exceeded significance thresholds in the derivation cohort. One of these, reduced 24-hour activity variance, was associated with greater pain severity (r = -0.14; 95% CI, -0.20 to -0.07). Changes in 6 rest-activity measures were associated with changes in pain over time, and changes in the number of transitions between sleep and wake over time were associated with changes in pain, sleep, and anxiety. Simple cutoffs for these biomarkers identified individuals with good recovery for pain (positive predictive value [PPV], 0.85; 95% CI, 0.82-0.88), sleep (PPV, 0.63; 95% CI, 0.59-0.67, and anxiety (PPV, 0.76; 95% CI, 0.72-0.80) with high predictive value. Conclusions and Relevance: These findings suggest that wrist-wearable device biomarkers may have utility as screening tools for pain, sleep, and anxiety symptom outcomes after trauma exposure in high-risk populations.

Validating the HELP Survey to Understand Health Disparity Influence on Chronic Pain.

BACKGROUND: Socioeconomic deprivation is shown to be linked to chronic lower back pain. However, a comprehensive approach to identify key components contributing to pain outcomes using a biopsychosocial model incorporating social determinants of health is needed to develop and tailor successful strategies for mitigating chronic pain development and persistence. We aimed to develop and validate an instrument (the HELP survey) to assess themes characterized by a combination of social determinants of health and the biopsychosocial model in an underserved population. The HELP survey was developed as part of an observational study determining the influence of health disparities on chronic pain interference, The Neighborhood of Pain: Health Disparity Influence on Level of Chronic Pain Interference study (HELP). METHODS: A multidisciplinary group of experts developed the 40-item, 5-domain HELP survey to address Pain Catastrophizing, Pain Motivation, Social Support, Health Care Access, and Physical Environment. After IRB approval, recruitment occurred at an academic pain clinic and emergency department. Survey validation included principal factor analysis, factor loadings of 0.40 or greater, and Cronbach's alpha of 0.70 or greater. RESULTS: Initial pilot testing occurred in 48 participants, followed by question revision and retesting in 57 patients. The average age was 57 years old. The majority were female (61.9%), self-reported black (55.2%), and had a high school health literacy level of 60.9%. Nearly half of participants (52.4%) lived in a neighborhood of extreme socioeconomic disadvantage and for 43.4% highest educational attainment was high school. Pilot testing resulted in 5 domains with Cronbach's alphas: Pain Catastrophizing (0.82), Patient Motivation (0.80), Social Support (0.56), Health Care Access (0.75), and Physical Environment (0.81). CONCLUSION: Pilot study validation of the HELP survey demographic data indicated testing was performed in an underserved chronic pain population, and questions supported measurement of social determinants of health and the biopsychosocial model based on the five themes identified.