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Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning
Ist Teil von
Journal of anxiety disorders, 2024-06, Vol.104, p.102876, Article 102876
Ort / Verlag
Netherlands: Elsevier Ltd
Erscheinungsjahr
2024
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
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.
•ED patients repeatedly assessed for trauma symptoms following trauma exposure.•31% of patients exhibited significant PTSD symptoms 8 weeks following trauma.•Features from 26 trauma symptoms were candidates for pragmatic PTSD algorithm.•CART analysis identified 3-item set of symptoms that accurately predicted PTSD.•Opportunities to vary screening thresholds to balance sensitivity and specificity.