Outcome prediction model for severe diffuse brain injuries: development and evaluation.
BACKGROUND
Intensive care resources for the management of severe diffuse brain injury patients (SDBI) are limited. Their optimal use is possible only if we can predict at admission which patients are unlikely to improve.


OBJECTIVE
To develop a simple and effective model to predict poor outcome in patients with SDBI in order to help guide initial therapy.


METHODS
The prognostic factors and outcomes of 289 patients with severe diffuse brain injury (GCS 3-8) were analyzed retrospectively. The prognostic factors analyzed were age, mode of injury, GCS at admission, pupillary reaction, horizontal oculocephalic reflex, and CT scan findings. Outcome at 1 month was classified as unfavorable--death or persistent vegetative state, or favorable--improvement with or without some disability. A stepwise linear logistic regression analysis was used to identify the most important predictors of poor outcome. A prediction model (NIMHANS model-NM) was developed using these factors. NM and several currently available outcome prediction models were prospectively applied in a separate group of 26 patients with severe diffuse brain injury managed with a different protocol.


RESULTS
The most important predictors of poor outcome were found to be the horizontal oculocephalic reflex, motor score of GCS, and midline shift on CT scan. NM was found to be more sensitive (75%) and specific (67%) than most other models in predicting unfavorable outcome. NM had high false pessimistic results (33%).


CONCLUSIONS
Prediction models cannot be used to guide initial therapy.
Pillai
S V
SV
Department of Neurosurgery, NIMHANS, Bangalore - 560029, India. drshibupillai@hotmail.com
Kolluri
V R
VR
Praharaj
S S
SS
D000328:Adult; D001930:Brain Injuries / Q000000981:diagnostic imaging* / Q000401:mortality*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D012189:Retrospective Studies; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed