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. Methods-All 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. Results-372 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. Conclusions-All 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. (J Neurol Neurosurg Psychiatry 1999;66:20-25)

These findings support the validity of the simple QL measures used in the trial. They are compatible with the simple explanation that patients perceive disease progression before it is clinically evident, but also with a causal relationship between QL and survival duration.

After acute brain injury there may be increased intracranial production of cytokines, with activation of inflammatory cascades. We have sought to determine if a transcranial cytokine gradient was demonstrable in paired sera of 32 patients requiring intensive care after acute brain injury. The difference between concentrations of IL-1 beta, IL-6, IL-8 and TNF alpha in jugular venous and arterial serum was measured on admission, and at 24, 48 and 96 h after the primary injury. There were no differences in IL-1 beta, IL-8 or TNF alpha, but median gradients of 6.7 and 11.5 pg ml-1 for IL-6 were demonstrated in the traumatic brain injury (n = 22) and subarachnoid haemorrhage (n = 10) groups, respectively (normal values in serum < 4.7 pg ml-1; P < 0.001 both groups). This suggests that there is significant production of IL-6 by intracranial cells after acute brain injury. Therapy directed towards combatting the negative effects of IL-6 may potentially benefit patients who have sustained an acute brain injury.

Common methods of treatment allocation for multi-centre and/or stratified randomized clinical trials can result in substantial differences between the number of patients allocated to each treatment arm. This can occur in the overall trial for a permuted block design or within individual institutions/strata when using a minimization scheme. This may lead to a bias in the result. Also, these procedures can be predictable, with the possibility of an investigator-introduced selection bias. An easily implemented method of randomization is proposed which attempts to overcome these problems by balancing treatment allocations both within strata and across the trial as a whole. The method keeps a running tally on total treatment allocation numbers at all stratification levels. When a patient accrues a hierarchical decision rule is applied, and the allocation is deterministic if certain pre-defined limits are exceeded, and random otherwise. The method is an extension of the big stick design of Soares and Wu, and is related to both Zelen's key number randomization methods and the schemes of Nordle and Brantmark. Simulation studies are used to demonstrate that major imbalances possible with other schemes do not occur using this method, and that the potential for selection bias is much reduced.