# Longitudinal Assessment of Post-traumatic Syndromes

> **NIH NIH U01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $3,915,397

## Abstract

Each year, more than 40 million Americans present to US emergency departments (EDs) for evaluation after
trauma exposure (TE). While the majority of these individuals recover, an important subset develops adverse
posttraumatic neuropsychiatric sequelae (APNS). These APNS include traditionally categorized outcomes
such as posttraumatic stress disorder (PTSD), depression, minor traumatic brain injury (MTBI), and regional or
widespread pain. However, these previous definitions of outcome have limited progress, and we now
appreciate that the actual trajectories of APNS are multidimensional, incorporating a range of specific
outcomes that may be best understood, and optimally targeted for intervention, by dividing across specific
domains of functioning. This application, submitted in response to RFA-MH-16-500, proposes to identify and
characterize the trajectories of the most common trauma-induced APNS within these domains of functioning
using the RDoC classification system. 5,000 patients presenting to the ED after trauma will be screened,
recruited, and will receive initial baseline evaluation in the ED, including blood collection and psychophysical,
survey, and neurocognitive evaluation. They will be closely monitored over the next 8 weeks using innovative
technologies (a wrist wearable for continuous-time monitoring of daytime physiology and sleep; a smart phone
app for continuous-time monitoring of GPS and daily “flash” surveys; weekly web-based neurocognitive tests;
periodic mixed-mode surveys; serial saliva collection; deep phenotyping [blood collection, fMRI,
psychophysical evaluation]) and then followed less intensively using similar procedures (including deep
phenotyping) over the remainder of a 52-week follow-up period. Adaptive sampling and state-of-the-art
statistical methods will be used to (1) optimize precision in characterizing RDoC construct trajectories and (2)
test theoretically-guided, “high yield” hypotheses evaluating the effects of pre-trauma, peritraumatic, and
recovery-related factors on these trajectories and on multivariate RDoC construct trajectory profiles. The
longitudinal schedule of rich, granular, multidimensional data collection in the study has been specifically
designed to evaluate those constructs most important to post-TE outcomes and to test the proposed
hypotheses. Ensemble machine learning methods will be used to develop tiered-targeted clinical decision
support models to identify individuals at high risk of specific, common APNS outcomes. The close-knit ED
research network that will undertake the study has a strong track record of prospective research on APNS and
is ideally suited to carry out this exceedingly complex study. The study has been designed to be a resource for
the entire field (for example, it has been designed and budgeted to collect and store a great many more
biological samples at the NIMH Biorespository than we can analyze, for use by other investigators).

## Key facts

- **NIH application ID:** 10019595
- **Project number:** 5U01MH110925-05
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** RONALD C KESSLER
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $3,915,397
- **Award type:** 5
- **Project period:** 2016-09-23 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10019595

## Citation

> US National Institutes of Health, RePORTER application 10019595, Longitudinal Assessment of Post-traumatic Syndromes (5U01MH110925-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10019595. Licensed CC0.

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