TY - JOUR
T1 - Risk classification after aneurysmal subarachnoid hemorrhage
AU - Germanson, Teresa P.
AU - Lanzino, Giuseppe
AU - Kongable, Gail L.
AU - Torner, James C.
AU - Kassell, Neal F.
N1 - Funding Information:
Supported by grants from the National Institute of Neurological Disorders and Stroke-National tnstitute of Health (NS24806),D uPont-Merck Pharmaceuticals,a nd Syntex Research. The Participants in this study are listed in the appendix. Presented in part at the 19th International Joint Conference on Stroke and Cerebral Circulation. San Diego, California, February 17-19, 1994.
PY - 1998/2
Y1 - 1998/2
N2 - 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.
AB - 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.
KW - Outcome
KW - Prediction
KW - Prognosis
KW - Subarachnoid hemorrhage
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U2 - 10.1016/S0090-3019(97)00337-6
DO - 10.1016/S0090-3019(97)00337-6
M3 - Article
C2 - 9457265
AN - SCOPUS:0032006186
SN - 0090-3019
VL - 49
SP - 155
EP - 161
JO - Surgical Neurology
JF - Surgical Neurology
IS - 2
ER -