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a Department of
Clinical Neurosciences, University of Edinburgh, UK, b Department of Anaesthetics, University of
Edinburgh, UK
Correspondence to: Dr D F Signorini, University Department of Clinical Neurosciences, Bramwell-Dott Building, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK.
Received 23 December 1997 and in revised form 23 June 1998;
Accepted 9 July 1998
OBJECTIVES
Prediction
of patient outcome can be useful as an aid to clinical decision making,
to explore possible biological mechanisms, and as part of the clinical
audit process. Many studies have constructed predictive models for
survival after traumatic brain injury, but these have often used
expensive, time consuming, or highly specialised measurements. The aim
of this study was to develop a simple easy to use model involving only
variables which are rapidly and easily clinically achievable in routine practice.
METHODSAll
consecutive patients admitted to a regional trauma centre with moderate
or severe head injury were enrolled in the study. Basic demographic,
injury, and CT characteristics were recorded. Patient survival at 1 year was used to construct a simple predictive model which was then
validated on a very similar patient group.
RESULTS372 patients
were included in the study, of whom 365 (98%) were followed up for
survival at 1 year. Multiple logistic regression resulted in a model
containing age (p<0.001), Glasgow coma scale score (p<0.001), injury
severity score (p<0.001), pupil reactivity (p=0.004), and presence of
haematoma on CT (p=0.004) as independently significant predictors of
survival. The model was validated on an independent set of 520 patients, showing good discrimination and adequate calibration, but
with a tendency to be pessimistic about very severely injured patients.
It is presented as an easy to use nomogram.
CONCLUSIONSAll five
variables have previously been shown to be related to survival. All
variables in the model are clinically simple and easy to measure
rapidly in a centre with access to 24 hour CT, resulting in a model
that is both well validated and clinically useful.
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