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Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure

Published online by Cambridge University Press:  01 July 2022

Raphael Kim ,
Tina Lin ,
Gehao Pang ,
Yufeng Liu ,
Andrew S. Tungate ,
Phyllis L. Hendry ,
Michael C. Kurz ,
David A. Peak ,
Jeffrey Jones  and
Niels K. Rathlev
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Raphael Kim
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
Tina Lin*
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
Gehao Pang
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
Yufeng Liu
Affiliation:
Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA Department of Genetics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
Andrew S. Tungate
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
Phyllis L. Hendry
Affiliation:
Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, FL, USA
Michael C. Kurz
Affiliation:
Department of Emergency Medicine, University of Alabama, Birmingham, AL, USA
David A. Peak
Affiliation:
Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
Jeffrey Jones
Affiliation:
Department of Emergency Medicine, Spectrum Health Butterworth Campus, Grand Rapids, MI, USA
Niels K. Rathlev
Affiliation:
Department of Emergency Medicine, Baystate State Health System, Springfield, MA, USA
Robert A. Swor
Affiliation:
Department of Emergency Medicine, Beaumont Hospital, Royal Oak, MI, USA
Robert Domeier
Affiliation:
Department of Emergency Medicine, St Joseph Mercy Health System, Ann Arbor, MI, USA
Marc-Anthony Velilla
Affiliation:
Department of Emergency Medicine, Sinai Grace, Detroit, MI, USA
Christopher Lewandowski
Affiliation:
Department of Emergency Medicine, Henry Ford Hospital, Detroit, MI, USA
Elizabeth Datner
Affiliation:
Department of Emergency Medicine, Albert Einstein Medical Center, Philadelphia, PA, USA
Claire Pearson
Affiliation:
Department of Emergency Medicine, Detroit Receiving, Detroit, MI, USA
David Lee
Affiliation:
Department of Emergency Medicine, North Shore University Hospital, Manhasset, NY, USA
Patricia M. Mitchell
Affiliation:
Department of Emergency Medicine, Boston University School of Medicine, Boston, MA, USA
Samuel A. McLean
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA Department of Emergency Medicine, University of North Carolina, Chapel Hill, NC, USA
Sarah D. Linnstaedt*
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
*
Author for correspondence: Sarah D. Linnstaedt, E-mail: sarah_linnstaedt@med.unc.edu
Author for correspondence: Sarah D. Linnstaedt, E-mail: sarah_linnstaedt@med.unc.edu
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Abstract

Background

Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS.

Methods

Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale – Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample).

Results

Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms.

Conclusions

These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.

Keywords

Machine learningpredictionPTSDtraumarisk factors
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 11 , August 2023 , pp. 4952 - 4961
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

These authors contributed equally to this work.

†

Co-senior.

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