Details

Autor(en) / Beteiligte

• Horwitz, Adam
• McCarthy, Kaitlyn
• House, Stacey L.
• Beaudoin, Francesca L.
• An, Xinming
• Neylan, Thomas C.
• Clifford, Gari D.
• Linnstaedt, Sarah D.
• Germine, Laura T.
• Rauch, Scott L.
• Haran, John P.
• Storrow, Alan B.
• Lewandowski, Christopher
• Musey Jr, Paul I.
• Hendry, Phyllis L.
• Sheikh, Sophia
• Jones, Christopher W.
• Punches, Brittany E.
• Swor, Robert A.
• Hudak, Lauren A.
• Pascual, Jose L.
• Seamon, Mark J.
• Harris, Erica
• Pearson, Claire
• Peak, David A.
• Domeier, Robert M.
• Rathlev, Niels K.
• Sergot, Paulina
• Sanchez, Leon D.
• Bruce, Steven E.
• Joormann, Jutta
• Harte, Steven E.
• Koenen, Karestan C.
• McLean, Samuel A.
• Sen, Srijan

Titel

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

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.