Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning
Author Department
Emergency Medicine
Document Type
Article, Peer-reviewed
Publication Date
5-2024
Abstract
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.
Recommended Citation
Horwitz A, McCarthy K, House SL, Beaudoin FL, An X, Neylan TC, Clifford GD, Linnstaedt SD, Germine LT, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI Jr, Hendry PL, Sheikh S, Jones CW, Punches BE, Swor RA, Hudak LA, Pascual JL, Seamon MJ, Harris E, Pearson C, Peak DA, Domeier RM, Rathlev NK, Sergot P, Sanchez LD, Bruce SE, Joormann J, Harte SE, Koenen KC, McLean SA, Sen S. Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning. J Anxiety Disord. 2024 May 5;104:102876. doi: 10.1016/j.janxdis.2024.102876. Epub ahead of print.
PMID
38723405