Replication and Extension of a Prognostic Model in Severe Traumatic Brain Injury

J Int Neuropsychol Soc. 2011 Mar;

17(s1):332.

Watson, W., S. C. Heaton, H. J. Hannay, J. Sirinek, A. Schmalfuss, A. Gabrielli and S. Robicsek.

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Abstract:

Objective: Outcomes for patients who survive a severe TBI can range from permanently disabled to remarkably favorable. Researchers continue to strive to predict not only mortality but long term outcome. A recent model has proven to be useful in crude predictions of outcome after severe TBI (Steyerberg et al., 2008). The present study sought to replicate the main findings of this model and extend these findings by using a measure that provides more detailed characterization of outcome at 6 months post-injury. Participants and Methods: Participants consisted of 546 adults ages 18-89 with severe TBI. Acute physiological data was gathered for use in the extended ImPACT model and included: patient’s age, pupil response, GCS motor score, CT characteristics, as well as events of hypoxia and hypotension. Sum scores derived from the ImPACT model were calculated from each participant’s acute data. Outcome measures obtained at 6-month post injury included mortality, favorable/unfavorable outcome on the Glasgow Outcome Scale (GOS), and the Disability Rating Scale (DRS). Results: The ImPACT model predicted mortality at 6-months post-injury with area under curve (AUC) of .792. When predicting favorable outcome, the AUC improved to .800. Logistic regression showed the ImPACT model significantly predicted both mortality (odds ratio = 1.30; B = -.26, S.E. = .06, p < .001) and favorable outcome (odds ratio = 1.32; B = -.28, S.E. = .06, p < .001). Linear regression analyses revealed that the IMPACT model moderately predicted outcome on the DRS 6-months post-injury (F(1,544) = 256.02, p < .001, R2 = .32). Conclusions: This study replicated the ImPACT study findings and showed that the model is sensitive and specific for predicting mortality and favorable outcome in this sample. The model shows promise for creating a more precise way of classifying severity to help explain the heterogeneity among severe TBI patient outcomes.

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