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.

Do Gender and Race Make a Difference in Acute Coronary Syndrome Pretest Probabilities in the Emergency Department?

OBJECTIVES: The objective was to test for significant differences in subjective and objective pretest probabilities for acute coronary syndrome (ACS) in a large cohort of chest pain patients stratified by race or gender. Secondarily we wanted to test for any differences in rates of ACS, rates of 90-day returns, cost, and chest radiation exposure after these stratifications. METHODS: This is a secondary analysis of a prospective outcomes study of ED patients with chest pain and shortness of breath. We performed two separate analyses. The data set was divided by gender for analysis 1 while the analysis 2 stratification was made by race (nonwhite vs. white). For each analysis, groups were compared on several variables: provider visual analog scales (VAS) for likelihood of ACS, PREtest Consult ACS probabilities, rates of ACS, total radiation exposure to the chest, total costs at 30 days, and 90-day recidivism (ED, overnight observations, and inpatient admissions). RESULTS: A total of 844 patients were studied. Gender information was present on all 844 subjects, while complete race/ethnicity information was available on 783 (93%) subjects. For the first analysis, female patients made up 57% (478/844) of the population and their mean provider VAS scores for ACS were significantly lower (p = 0.000) at 14% (95% confidence interval [CI] = 13% to 16%) than that of males at 22% (95% CI = 19% to 24%). This was consistent with the objective pretest ACS probabilities subsequently calculated via the validated online tool, PREtest Consult, which were also significantly lower (p = 0.000) at 2.7% (95% CI = 2.4% to 3.1%) for females versus 6.6% (95% CI = 5.9% to 7.3%) for males. However, comparing females to males, there was no significant difference in diagnosis of ACS (3.6% vs. 1.6%), mean chest radiation doses (5.0 mSv vs. 4.9 mSv), total costs at 30 days ($3,451.24 vs. $3,847.68), or return to the ED within 90 days (26% each). For analysis 2 by race, nonwhite patients also comprised 57% (444/783) of individuals. Similar to the gender analysis, mean provider VAS scores for ACS were found to be significantly lower (p = 0.000) at 15% (95% CI = 13% to 16%) for nonwhite versus 20% (95% CI = 18% to 23%) for white subjects. Concordantly, objective pretest ACS probabilities were also significantly lower (p = 0.000) at 3.4% (95% CI = 2.9% to 3.9%) for nonwhite versus 5.3% (95% CI = 4.7% to 5.9%) for white subjects. There were no significant differences in outcomes in nonwhite versus white subjects when compared on diagnosis of ACS (3.2% vs 2.4%), mean chest radiation dose (4.6 mSv vs. 5.0 mSv), cost ($3,156.02 vs. $2,885.18), or 90-day ED returns (28% vs. 23%). CONCLUSIONS: Despite consistently estimating the risk for ACS to be lower for both females and minorities concordantly with calculated objective pretest assessments, there does not appear to have been any significant decrease in subsequent evaluation of these perceived lower-risk groups when radiation exposure and costs are taken into account. Further studies on the impact of pretest assessments on gender and racial disparities in ED chest pain evaluation are needed.