Traumatic ,hypoxia and hypotension, brain stem re?exes .The

Traumatic brain injury (TBI) is a global
health problem with an approximate incidence of 0.2– 0.5% each year 1.  It is a leading cause of mortality, morbidity, and
socioeconomic losses in India. Approximately 1.6 million individuals sustain
TBI and seek hospital care annually in India. 2

Clinicians decide therapeutic interventions based on
their assessment of prognosis of TBI patients. Many doctors believe that an
accurate assessment of prognosis is important when they made decisions about
the use of specific methods of treatment which may be hyperventilation,
barbiturates, or hyperosmolar therapy.4 At the same time  the assessment help in deciding whether or
not to withdraw treatment. It plays as important role in counselling the patients
and relatives. 3

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Commonly used clinical predictors of outcome of TBI both
individually or in combination are age, Glasgow coma scale score, pupillary
reactivity, extra cranial injury ,hypoxia and hypotension, brain stem re?exes .The investigational  predictors are based on CT ?ndings, like midline Shift, petechial
haemorrhages and obliteration of Third Ventricle or Basal Cisterns               4


There are many studies predicting the outcome
in severe head injuries taking into

consideration the both clinical and
investigational parameters. However there are not many

studies comparing the predictability of
clinical and investigational parameters based models.


 Clinical parameters are equally
important, particularly at the site of accident or at the place of

disaster, where investigation facilities 
are not available. We have contemplated a study to

evaluate the clinical and imaging parameters by developing logistic
regression models and a

assessing and comparing the models.



Subjects and Methods: After Institutional ethical and research committee
clearance 100 patient who had head injury at our emergency were enrolled in to
our study and followed up to 14 days.


Sample size: As suggested
by Miller and Kunce (1973) subject to predictor ratio is 10 to 1 the sample
size 100 were selected taking into consideration the clinical variable were 10


 The  clinical variables  and  CT
scan findings were recorded and transformed in to binary  data a following manner : age 100 mmHg as ‘1’ ? 100 as ‘0’ and diastolic 
blood pressure (DBP) >60’1′ ? 60as ‘0’ in mm Hg ,Pulse Rate (Beats/Minute) 8 as ‘1’,Pupillary reaction to light  present as ‘1’ absent as’0′,Anisocoria
yes  as ‘1’ no as ‘0’,Extra Cranial
Injury  yes as ‘1’   no
as’0′ and CT findings , Midline Shift 5mm as ‘0’ Petechial
haemorrhages   yes  as ‘1’ 
no as ‘0’  Obliteration of Third Ventricle or Basal
Cisterns   yes as ‘1’  no as’0′, The outcome of head injury was
measured by Glasgow Outcome scale on 14th day as follows 1)
Discharges home without  neurological
Sequelae  2) Discharged home with  neurological sequelae  favorable 
as ‘1’ 3) Severe disability 4) Vegetative state 5)
Death as unfavorable as ,0, 



Initially all the variables were
analyzed and ranking was done by using the Tanagra datamining software 6
using Fisher filtering with P value of 0.05) (Table).



Traumatic brain injuries (TBI) are a
real social problem because of industrialization and motorization 1. Two
million individuals each year sustain traumatic brain injury in the United
States, resulting in 56,000 deaths 2.

It is becoming a major cause of
death and disability. Establishing a reliable prognosis after injury is
difficult. On the other hand, clinicians treating patients often make
therapeutic decisions based on their assessment of prognosis. Many prognostic
models have been reported but none are widely used.7

7. Perel P,
Edwards P, Wentz R, Roberts I. Systematic review of prognostic models in
traumatic brain injury. BMC Med Inform Decis Mak 2006; 6:38.


We have developed two
prognostic models one is dependent on only clinical variable and other investigational
variables of CT imaging for predicting two clinically relevant outcomes in
patients with traumatic brain injury.. The models have excellent discrimination
and good fit with both internal validation.

The clinical model
variables were similar to crash study, however in our study the respiratory
rate, anisocoria, systolic blood pressure found to have significant effect on
TBI outcome, which were not there in many of the outcome studies of TBI. The
systolic blood pressure and respiratory rate were very good predictors in many
studies in trauma patients.8,9,10  The
extra-cranial injury which is one of the clinical factor in crash study, but in
our study it was not significantly effecting the  outcome (p value =0.944).

The clinical Model
sensitivity, accuracy. AUC of ROIC curve in fact are better comparted to

Imaging model. The error
rates by resubstitution, test set, cross validation and bootstrap
methods are almost similar .However the specificity is better with imaging
model. When compared with Z test there is no significant difference between the
two models. Thus the clinical model gives an equally good estimation of
prognosis particularly at site of injury or any natural disaster.


Limitations: Our
study is not a multicentre study. The external validation was not performed in
a different location or a centre.



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