Outcome prediction model for severe traumatic brain injury.

Aim: Treatment of severe traumatic brain injury is aided by better prediction of outcomes. The purpose of the present study was to develop and validate a prediction model using retrospective analysis of prospectively collected clinical data from two tertiary critical care medical centers in Japan.

Methods: Data were collected from 253 patients with a Glasgow Coma Scale score of <9. Within 24 h of their admission, 15 factors possibly related to outcome were evaluated. The dataset was randomly split into training and validation datasets using the repeated random subsampling method. A logistic regression model was fitted to the training dataset and predictive accuracy was assessed using the validation data.

Results: The best model included the variables age, pupillary light reflex, extensive subarachnoid hemorrhage, intracranial pressure, and midline shift. The estimated area under the curve for the model development data was 0.957, with a 95% confidence interval of 0.926-0.987, and that for validation data was 0.947, with a 95% confidence interval of 0.909-0.980.

Conclusion: Our predictive model was shown to have high predictive value. It will be useful for review of treatment, family counseling, and efficient allocation of resources for patients with severe traumatic brain injury.