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(Stroke. 2009;40:181.)
© 2009 American Heart Association, Inc.

Original Contributions

Clinical- and Imaging-Based Prediction of Stroke Risk After Transient Ischemic Attack

The CIP Model

Hakan Ay, MD; E. Murat Arsava, MD; S. Claiborne Johnston, MD, PhD; Mark Vangel, PhD; Lee H. Schwamm, MD; Karen L. Furie, MD; Walter J. Koroshetz, MD A. Gregory Sorensen, MD

From Stroke Service, Department of Neurology (H.A., L.H.S., K.L.F.) and AA Martinos Center for Biomedical Imaging, Department of Radiology (H.A., E.M.A., M.V., A.G.S.), Massachusetts General Hospital, Harvard Medical School, Boston, Mass; the Departments of Neurology and Epidemiology and Biostatistics (S.C.J.), University of California–San Francisco, San Francisco, Calif; and the National Institute of Neurological Disorders and Stroke (W.J.K.), National Institutes of Health, Bethesda, Md.

Correspondence to Hakan Ay, MD, AA Martinos Center for Biomedical Imaging and Stroke Service, Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Room 2301, Charlestown MA 02129. E-mail hay{at}partners.org

Background and Purpose— Predictive instruments based on clinical features for early stroke risk after transient ischemic attack suffer from limited specificity. We sought to combine imaging and clinical features to improve predictions for 7-day stroke risk after transient ischemic attack.

Methods— We studied 601 consecutive patients with transient ischemic attack who had MRI within 24 hours of symptom onset. A logistic regression model was developed using stroke within 7 days as the response criterion and diffusion-weighted imaging findings and dichotomized ABCD² score (ABCD² 4) as covariates.

Results— Subsequent stroke occurred in 25 patients (5.2%). Dichotomized ABCD² score and acute infarct on diffusion-weighted imaging were each independent predictors of stroke risk. The 7-day risk was 0.0% with no predictor, 2.0% with ABCD² score 4 alone, 4.9% with acute infarct on diffusion-weighted imaging alone, and 14.9% with both predictors (an automated calculator is available at http://cip.martinos.org). Adding imaging increased the area under the receiver operating characteristic curve from 0.66 (95% CI, 0.57 to 0.76) using the ABCD² score to 0.81 (95% CI, 0.74 to 0.88; P=0.003). The sensitivity of 80% on the receiver operating characteristic curve corresponded to a specificity of 73% for the CIP model and 47% for the ABCD² score.

Conclusions— Combining acute imaging findings with clinical transient ischemic attack features causes a dramatic boost in the accuracy of predictions with clinical features alone for early risk of stroke after transient ischemic attack. If validated in relevant clinical settings, risk stratification by the CIP model may assist in early implementation of therapeutic measures and effective use of hospital resources.

Key Words: diffusion-weighted imaging • stroke risk • transient ischemic attack

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