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.

Challenge of intravascular volume assessment in acute ischemic stroke.

INTRODUCTION: Retrospective data indicates that dehydration in acute ischemic stroke patients may be common, even though these patients frequently have elevated blood pressure. We sought to evaluate clinical and laboratory measures of intravascular volume status compared to more objective measures using ultrasound measurements of the inferior vena cava (IVC). METHODS: This was a prospective observation study of acute ischemic stroke patients in the emergency department. Patients with NIH stroke scale ≥4 within 12 h of symptom onset were included. A trained ultrasonographer performed bi-dimensional imaging of the IVC with passive respiration to determine the percent inspiratory collapse and maximum diameter. We defined low intravascular volume as >50% IVC collapse and a maximal diameter < 2.1 cm. Analysis was limited to patients with confirmed ischemic stroke. RESULTS: There were 42 patients, of whom 31 had confirmed acute ischemic stroke. The mean age was 65 ±â€¯15 years, 52% were female, and 71% were hypertensive. The median NIH stroke scale score was 7 (IQR 5-15). Based on IVC ultrasound, low intravascular volume was present in 63% (95% CI 44-80%) of patients. A higher proportion of hypertensive patients had low intrasvascular volume (72% vs. 33%). There was poor correlation between IVC assessment of intrasvascular volume and blinded clinician assessment or laboratory markers of dehydration. CONCLUSION: The majority of ED acute ischemic stroke patients in this sample were hypertensive and demonstrated low intravascular volume based on IVC ultrasound.