Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning

J Anxiety Disord. 2024 Jun:104:102876. doi: 10.1016/j.janxdis.2024.102876. Epub 2024 May 5.


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.

Keywords: Emergency services; Machine learning; Mobile assessment; Posttraumatic stress disorder; Trauma.

MeSH terms

  • Adult
  • Female
  • Humans
  • Longitudinal Studies
  • Machine Learning*
  • Male
  • Middle Aged
  • Stress Disorders, Post-Traumatic* / diagnosis
  • Stress Disorders, Post-Traumatic* / psychology