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Using emergency department-based inception cohorts to determine genetic characteristics associated with long term patient outcomes after motor vehicle collision: methodology of the CRASH study.

Publication ,  Journal Article
Platts-Mills, TF; Ballina, L; Bortsov, AV; Soward, A; Swor, RA; Jones, JS; Lee, DC; Peak, DA; Domeier, RM; Rathlev, NK; Hendry, PL; McLean, SA
Published in: BMC Emerg Med
September 26, 2011

BACKGROUND: Persistent musculoskeletal pain and psychological sequelae following minor motor vehicle collision (MVC) are common problems with a large economic cost. Prospective studies of pain following MVC have demonstrated that demographic characteristics, including female gender and low education level, and psychological characteristics, including high pre-collision anxiety, are independent predictors of persistent pain. These results have contributed to the psychological and social components of a biopsychosocial model of post-MVC pain pathogenesis, but the biological contributors to the model remain poorly defined. Recent experimental studies indicate that genetic variations in adrenergic system function influence the vulnerability to post-traumatic pain, but no studies have examined the contribution of genetic factors to existing predictive models of vulnerability to persistent pain. METHODS/DESIGN: The Project CRASH study is a federally supported, multicenter, prospective study designed to determine whether variations in genes affecting synaptic catecholamine levels and alpha and beta adrenergic receptor function augment social and psychological factors in a predictive model of persistent musculoskeletal pain and posttraumatic stress disorder (PTSD) following minor MVC. The Project CRASH study will assess pain, pain interference and PTSD symptoms at 6 weeks, 6 months, and 1 year in approximately 1,000 patients enrolled from 8 Emergency Departments in four states with no-fault accident laws. DISCUSSION: The results from this study will provide insights into the pathophysiology of persistent pain and PTSD following MVC and may serve to improve the ability of clinicians and researchers to identify individuals at high risk for adverse outcomes following minor MVC.

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Published In

BMC Emerg Med

DOI

EISSN

1471-227X

Publication Date

September 26, 2011

Volume

11

Start / End Page

14

Location

England

Related Subject Headings

  • Young Adult
  • Survivors
  • Stress Disorders, Post-Traumatic
  • Prospective Studies
  • Pain
  • Outcome Assessment, Health Care
  • North Carolina
  • Middle Aged
  • Male
  • Logistic Models
 

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Platts-Mills, T. F., Ballina, L., Bortsov, A. V., Soward, A., Swor, R. A., Jones, J. S., … McLean, S. A. (2011). Using emergency department-based inception cohorts to determine genetic characteristics associated with long term patient outcomes after motor vehicle collision: methodology of the CRASH study. BMC Emerg Med, 11, 14. https://doi.org/10.1186/1471-227X-11-14
Platts-Mills, Timothy F., Lauren Ballina, Andrey V. Bortsov, April Soward, Robert A. Swor, Jeffrey S. Jones, David C. Lee, et al. “Using emergency department-based inception cohorts to determine genetic characteristics associated with long term patient outcomes after motor vehicle collision: methodology of the CRASH study.BMC Emerg Med 11 (September 26, 2011): 14. https://doi.org/10.1186/1471-227X-11-14.
Platts-Mills TF, Ballina L, Bortsov AV, Soward A, Swor RA, Jones JS, Lee DC, Peak DA, Domeier RM, Rathlev NK, Hendry PL, McLean SA. Using emergency department-based inception cohorts to determine genetic characteristics associated with long term patient outcomes after motor vehicle collision: methodology of the CRASH study. BMC Emerg Med. 2011 Sep 26;11:14.
Journal cover image

Published In

BMC Emerg Med

DOI

EISSN

1471-227X

Publication Date

September 26, 2011

Volume

11

Start / End Page

14

Location

England

Related Subject Headings

  • Young Adult
  • Survivors
  • Stress Disorders, Post-Traumatic
  • Prospective Studies
  • Pain
  • Outcome Assessment, Health Care
  • North Carolina
  • Middle Aged
  • Male
  • Logistic Models