Statistical methods for determining prognosis in severe head injury.
Determining the prognostic significance of clinical factors for patients with severe head injury can lead to an improved understanding of the pathophysiology of head injury and to improvement in therapy. A technique known as the sequential Bayes method has been used previously for the purpose of prognosis. The application of this method assumes that prognostic factors are statistically independent. It is now known that they are not. Violation of the assumption of independence may produce errors in determining prognosis. As an alternative technique for predicting the outcome of patients with severe head injury, a logistic regression model is proposed. A preliminary evaluation of the method using data from 115 patients with head injury shows the feasibility of using early data to predict outcome accurately and of being able to rank input variables in order of their prognostc significance.
D001794:Blood Pressure; D002245:Carbon Dioxide / Q000097:blood; D006259:Craniocerebral Trauma / Q000097:blood / Q000175:diagnosis*; D005133:Eye Movements; D006400:Hematocrit; D006801:Humans; D008954:Models, Biological; D009068:Movement; D010100:Oxygen / Q000097:blood; D011336:Probability; D011379:Prognosis; D011680:Pupil; D012025:Reflex, Pupillary; D013060:Speech
D002245:Carbon Dioxide; D010100:Oxygen