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(Stroke. 2002;33:466.)
© 2002 American Heart Association, Inc.

Original Contributions

Combined Clinical and Imaging Information as an Early Stroke Outcome Measure

Karen C. Johnston, MD; Douglas P. Wagner, PhD;; E. Clarke Haley, Jr, MD Alfred F. Connors, Jr, MD for the RANTTAS Investigators

From the Departments of Neurology (K.C.J., E.C.H.) and Health Evaluation Sciences (K.C.J., D.P.W., A.F.C.), University of Virginia Health System, Charlottesville, Va.

Correspondence to Karen C. Johnston, MD, University of Virginia Health System, Department of Neurology, #800394, Charlottesville, VA 22908. E-mail kj4v{at}virginia.edu

Background and Purpose— Imaging information has been proposed as a potential surrogate outcome in stroke clinical trials. The purpose of this study was to determine whether an early outcome measure combining clinical and imaging information is better than either alone in predicting 3-month outcome in acute ischemic stroke patients.

Methods— Clinical information (National Institutes of Health Stroke Scale) and imaging information (CT infarct volume), measured at 1 week from 201 patients from the Randomized Trial of Tirilazad Mesylate in Acute Stroke (RANTTAS) study, were used in a multivariable logistic regression analysis to predict excellent and devastating 3-month outcome. The combined models were compared with the infarct volume models and the clinical models. Discrimination, calibration, and change in global model chi-square were assessed.

Results— The combined models and models using clinical information alone had areas under the receiver operating characteristic curves that did not differ significantly (probability value = 0.092 to 0.4), ranging from 0.83 to 0.95. The imaging alone models performed less well (P<0.005) and had areas under the receiver operating characteristic curves that ranged from 0.70 to 0.80.

Conclusions— The National Institutes of Health Stroke Scale at 1 week is highly predictive of 3-month outcome in ischemic stroke patients. The addition of 1-week infarct volume does not improve the accuracy of the predictive model.

Key Words: cerebral ischemia • models, statistical • prognosis • stroke outcome

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