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

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

doi:10.1016/j.janxdis.2024.102876

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.

Authors

Adam Horwitz¹, Kaitlyn McCarthy², Stacey L House³, Francesca L Beaudoin⁴, Xinming An⁵, Thomas C Neylan⁶, Gari D Clifford⁷, Sarah D Linnstaedt⁵, Laura T Germine⁸, Scott L Rauch⁹, John P Haran¹⁰, Alan B Storrow¹¹, Christopher Lewandowski¹², Paul I Musey Jr¹³, Phyllis L Hendry¹⁴, Sophia Sheikh¹⁴, Christopher W Jones¹⁵, Brittany E Punches¹⁶, Robert A Swor¹⁷, Lauren A Hudak¹⁸, Jose L Pascual¹⁹, Mark J Seamon²⁰, Erica Harris²¹, Claire Pearson²², David A Peak²³, Robert M Domeier²⁴, Niels K Rathlev²⁵, Paulina Sergot²⁶, Leon D Sanchez²⁷, Steven E Bruce²⁸, Jutta Joormann²⁹, Steven E Harte³⁰, Karestan C Koenen³¹, Samuel A McLean³², Srijan Sen²

Affiliations

¹ Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: ahor@umich.edu.
² Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.
³ Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA.
⁴ Department of Epidemiology, Brown University, Providence, RI 02930, USA; Department of Emergency Medicine, Brown University, Providence, RI 02930, USA.
⁵ Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27559, USA.
⁶ Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA 94143, USA.
⁷ Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30332, USA; Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
⁸ Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA 02478, USA; The Many Brains Project, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA.
⁹ Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA; Department of Psychiatry, McLean Hospital, Belmont, MA 02478, USA.
¹⁰ Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.
¹¹ Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
¹² Department of Emergency Medicine, Henry Ford Health System, Detroit, MI 48202, USA.
¹³ Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
¹⁴ Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL 32209, USA.
¹⁵ Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ 08103, USA.
¹⁶ Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH 43210, USA; Ohio State University College of Nursing, Columbus, OH 43210, USA.
¹⁷ Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI 48309, USA.
¹⁸ Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA 30329, USA.
¹⁹ Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
²⁰ Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Surgery, Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA 19104, USA.
²¹ Department of Emergency Medicine, Einstein Medical Center, Philadelphia, PA 19107, USA.
²² Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI 48202, USA.
²³ Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
²⁴ Department of Emergency Medicine, Trinity Health-Ann Arbor, Ypsilanti, MI 48197, USA.
²⁵ Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA 01107, USA.
²⁶ Department of Emergency Medicine, McGovern Medical School at UTHealth, Houston, TX 77030, USA.
²⁷ Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Emergency Medicine, Harvard Medical School, Boston, MA 02115, USA.
²⁸ Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO 63121, USA.
²⁹ Department of Psychology, Yale University, New Haven, CT 06510, USA.
³⁰ Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
³¹ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA.
³² Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27559, USA; Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27559, USA.

PMID: 38723405
PMCID: PMC11215748 (available on 2025-06-01)
DOI: 10.1016/j.janxdis.2024.102876

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.

MeSH terms

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

Abstract

MeSH terms

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