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 multivariate approaches to risk classification, Classification and Regression Trees (CART) and multiple logistic regression, and compared them with the best single predictor of outcome, level of consciousness.

Methods: Data 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 groups for unfavorable outcome were devised using CART and a stepwise logistic regression analysis. Admission factors incorporated into both classification schemes were: level of consciousness, age, location of aneurysm (basilar versus other), and the Glasgow Coma Score. The CART prediction tree also branched on a dichotomy of admission glucose level. The two multivariate classifications were then compared with a prediction scheme based on the single best performing prognostic factor, level of consciousness in an independent series, NICSAH II (n = 353), and also in the original training dataset.

Results: A similar discrimination of risk was achieved by the three classification systems in the testing sample (NICSAH II). The 8%, 19%, and 52% rates of unfavorable outcome obtained from low-, medium-, and high-risk groups defined by LOC approximated those obtained using the more complex multivariate systems.

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 after aneurysmal SAH.