Clinical elements that predict outcome after traumatic brain injury: a prospective multicenter recursive partitioning (decision-tree) analysis.

Short Title:
Model System:
TBI
Reference Type:
Journal Article
Accession No.:
Journal:
Journal of Neurotrauma
Year, Volume, Issue, Page(s):
2005, vol. 22, issue 10, pp 1040-1051
Publication Website:
Abstract:
Traumatic brain injury (TBI) often presents clinicians with a complex combination of clinical elements that can confound treatment and make outcome prediction challenging. Predictive models have commonly used acute physiological variables and gross clinical measures to predict mortality and basic outcome endpoints. The primary goal of this study was to consider all clinical elements available concerning a survivor of TBI admitted for inpatient rehabilitation, and identify those factors that predict disability, need for supervision, and productive activity one year after injury. The Traumatic Brain Injury Model Systems (TBIMS) database was used for decision tree analysis using recursive partitioning (n = 3463). Outcome measures included the Functional Independence Measure(), the Disability Rating Scale, the Supervision Rating Scale, and a measure of productive activity. Predictor variables included all physical examination elements, measures of injury severity (initial Glasgow Coma Scale score, duration of post-traumatic amnesia [PTA], length of coma, CT scan pathology), gender, age, and years of education. The duration of PTA, age, and most elements of the physical examination were predictive of early disability. The duration of PTA alone was selected to predict late disability and independent living. The duration of PTA, age, sitting balance, and limb strength were selected to predict productive activity at 1 year. The duration of PTA was the best predictor of outcome selected in this model for all endpoints and elements of the physical examination provided additional predictive value. Valid and reliable measures of PTA and physical impairment after TBI are important for accurate outcome prediction.
Author(s):
Brown AW; Malec JF; McClelland, R. L.; Diehl, N. N.; Englander, J.; Cifu, D. X.
Author Address(es):

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