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Updated: Mar 8, 2026
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
Published on: September 16, 2017
Validating a Predictive Model of Acute Advanced Imaging Biomarkers in Ischemic Stroke
Andrew Bivard1, Christopher Levi2, Longting Lin2
1From the Departments of Neurology, John Hunter Hospital, University of Newcastle, Australia (A.B., C.L., L.L., N.J.S., M.P.); Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China (X.C., M.L., Q.D.); Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, ON, Canada (R.A.); Department of Neurology, Royal Adelaide Hospital, Australia (T.K., J.J.); Department of Neurology, Gosford Hospital, Australia (B.O.); Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada (K.B.); and Department of Neurology, Baotou Central Hospital, China (J.Z.). Andrew.bivard@hotmail.com.
Background And Purpose:
Advanced imaging to identify tissue pathophysiology may provide more accurate prognostication than the clinical measures used currently in stroke. This study aimed to derive and validate a predictive model for functional outcome based on acute clinical and advanced imaging measures.
Methods:
A database of prospectively collected sub-4.5 hour patients with ischemic stroke being assessed for thrombolysis from 5 centers who had computed tomographic perfusion and computed tomographic angiography before a treatment decision was assessed. Individual variable cut points were derived from a classification and regression tree analysis. The optimal cut points for each assessment variable were then used in a backward logic regression to predict modified Rankin scale (mRS) score of 0 to 1 and 5 to 6. The variables remaining in the models were then assessed using a receiver operating characteristic curve analysis.
Results:
Overall, 1519 patients were included in the study, 635 in the derivation cohort and 884 in the validation cohort. The model was highly accurate at predicting mRS score of 0 to 1 in all patients considered for thrombolysis therapy (area under the curve [AUC] 0.91), those who were treated (AUC 0.88) and those with recanalization (AUC 0.89). Next, the model was highly accurate at predicting mRS score of 5 to 6 in all patients considered for thrombolysis therapy (AUC 0.91), those who were treated (0.89) and those with recanalization (AUC 0.91). The odds ratio of thrombolysed patients who met the model criteria achieving mRS score of 0 to 1 was 17.89 (4.59-36.35, P<0.001) and for mRS score of 5 to 6 was 8.23 (2.57-26.97, P<0.001).
Conclusions:
This study has derived and validated a highly accurate model at predicting patient outcome after ischemic stroke.
