RISK CLASSIFICATION AFTER ANEURYSMAL SUBARACHNOID HEMORRHAGE

BACKGROUND Prediction of patient outcome is an important aspect of the management and study of aneurysmal subarachnoid hemorrhage (SAH). In the present study, we evaluated the prognostic value of two multivaria te approaches to risk classification, Classification and Regression Tr ees (CART) and multiple logistic regression, and compared them with th e best single predictor of outcome, level of consciousness. METHODS Da ta prospectively collected in the first Cooperative Aneurysm Study of intravenous nicardipine after aneurysmal SAH (NICSAH I, n = 885) were used to develop the prediction models. Low-, medium-, and high-risk gr oups for unfavorable outcome were devised using CART and a stepwise lo gistic regression analysis. Admission factors incorporated into both c lassification schemes were: level of consciousness, age, location of a neurysm (basilar versus other), and the Glasgow Coma Score. The CART p rediction tree also branched on a dichotomy of admission glucose level . The two multivariate classifications were then compared with a predi ction scheme based on the single best performing prognostic factor, le vel of consciousness in an independent series, NICSAH II (n = 353), an d also in the original training dataset. RESULTS A similar discriminat ion of risk was achieved by the three classification systems in the te sting sample (NICSAH II). The 8%, 19%, and 52% rates of unfavorable ou tcome obtained from low-, medium-, and high-risk groups defined by LOC approximated those obtained using the more complex multivariate syste ms.CONCLUSION Although multivariate classification systems are useful to characterize the relationship of multiple risk factors to outcome, the simple clinical measure LOC is favored as a concise and practical classification for predicting the probability of unfavorable outcome a fter aneurysmal SAH. (C) 1998 by Elsevier Science Inc.