Literature DB >> 17398241

Comparison of the Traumatic Brain Injury (TBI) Model Systems national dataset to a population-based cohort of TBI hospitalizations.

John D Corrigan1, Anbesaw W Selassie, Lee A Lineberry, Scott R Millis, Kenneth D Wood, E Elisabeth Pickelsimer, Mitchell Rosenthal.   

Abstract

OBJECTIVE: To determine whether severity alone accounts for differences observed between a population-based cohort of acute care hospitalizations for traumatic brain injury (TBI) and the Traumatic Brain Injury Model Systems (TBIMS) national dataset.
DESIGN: Prospective cohort.
SETTING: Acute care hospitals in South Carolina and TBIMS rehabilitation centers. PARTICIPANTS: Subjects enrolled in the TBIMS national dataset and the South Carolina TBI Follow-up Registry (SCTBIFR).
INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Comparable variables in the 2 datasets included demographic characteristics, etiology of injury, initial Glasgow Coma Scale score, Abbreviated Injury Scale score for the head region derived from International Classification of Diseases codes, presence of computed tomography (CT) abnormalities, acute hospital length of stay, and payer source.
RESULTS: As hypothesized, TBIMS participants showed greater initial injury severity, frequency of abnormal CT scans, and longer lengths of acute care hospitalization, explaining over 75% of cohort membership. Counter to a priori hypotheses, when all other factors were held constant, there were also differences in racial and ethnic background and insurance payer source.
CONCLUSIONS: Differences between the TBIMS cohort and patients acutely hospitalized with TBI are primarily due to injury severity; however, an additional difference in payer source may need to be taken into account when generalizing findings. Results showed that TBIMS and SCTBIFR datasets are complementary, each having different strengths for understanding factors that impact long-term recovery after TBI. Recommendations are made for methodologic improvements in both data collection for the TBIMS and future outcome surveillance.

Entities:  

Mesh:

Year:  2007        PMID: 17398241     DOI: 10.1016/j.apmr.2007.01.010

Source DB:  PubMed          Journal:  Arch Phys Med Rehabil        ISSN: 0003-9993            Impact factor:   3.966


  4 in total

1.  Education quality, reading recognition, and racial differences in the neuropsychological outcome from traumatic brain injury.

Authors:  Noah D Silverberg; Robin A Hanks; Season C Tompkins
Journal:  Arch Clin Neuropsychol       Date:  2013-08       Impact factor: 2.813

2.  Representativeness of the Traumatic Brain Injury Model Systems National Database.

Authors:  John D Corrigan; Jeffrey P Cuthbert; Gale G Whiteneck; Marcel P Dijkers; Victor Coronado; Allen W Heinemann; Cynthia Harrison-Felix; James E Graham
Journal:  J Head Trauma Rehabil       Date:  2012 Nov-Dec       Impact factor: 2.710

3.  External Validation and Recalibration of Risk Prediction Models for Acute Traumatic Brain Injury among Critically Ill Adult Patients in the United Kingdom.

Authors:  David A Harrison; Kathryn A Griggs; Gita Prabhu; Manuel Gomes; Fiona E Lecky; Peter J A Hutchinson; David K Menon; Kathryn M Rowan
Journal:  J Neurotrauma       Date:  2015-06-12       Impact factor: 5.269

4.  The Boston Assessment of Traumatic Brain Injury-Lifetime (BAT-L) semistructured interview: evidence of research utility and validity.

Authors:  Catherine Brawn Fortier; Melissa M Amick; Laura Grande; Susan McGlynn; Alexandra Kenna; Lindsay Morra; Alexandra Clark; William P Milberg; Regina E McGlinchey
Journal:  J Head Trauma Rehabil       Date:  2014 Jan-Feb       Impact factor: 2.710

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.