Citation

Alsulaim HA, Smart BJ, Asemota AO, Haring RS, Canner JK, Efron DT, Haut ER, Schneider EB. Am. J. Surg. 2016; 214(2): 207-210.

Affiliation

Johns Hopkins Surgery Center for Outcomes Research, Johns Hopkins School of Medicine, Baltimore, MD, USA; Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: eschneider3@partners.org.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.amjsurg.2016.07.012

PMID

27663651

Abstract

BACKGROUND: Outcome studies in trauma using administrative data traditionally employ anatomy-based definitions of injury severity; however, physiologic factors, including consciousness, may correlate with outcomes. We examined whether accounting for conscious status in administrative data improved mortality prediction among patients with moderate to severe TBI.

METHODS: Patients meeting Centers for Disease Control and Prevention (CDC) guidelines for TBI in the 2006 to 2011 Nationwide Emergency Department Sample were identified. Patients were dichotomized as having no/brief loss of consciousness (LOC) vs extended LOC greater than 1 hour using International Classification of Diseases, Ninth Revision (ICD-9) fifth digit modifiers. Receiver operating curves compared the ability of logistic regression to predict mortality in models that included LOC vs models that did not.

RESULTS: Overall, 98,397 individuals met criteria, of whom 25.8% had extended LOC. In univariate analysis, AIS alone predicted mortality in 69.6% of patients (area under receiver operating characteristic curve.696, 95% CI.689 to.702), extended LOC alone predicted mortality in 76.8% (AUROC.768, 95% CI.764 to.773), and a combination of AIS and extended LOC predicted mortality in 82.6% of cases (AUROC.826, 95% CI.821 to.830). Similar differences were observed in best-fit models.

CONCLUSIONS: Accounting for LOC along with anatomical measures of injury severity improves mortality prediction among patients with moderate/severe TBI in administrative datasets. Further work is warranted to determine whether other physiological measures may also improve prediction across a variety of injury types.

Copyright © 2016 Elsevier Inc. All rights reserved.

Language: en