Andrew Bivard1, Christopher Levi2, Longting Lin2, Xin Cheng2, Richard Aviv2, Neil J Spratt2, Min Lou2, Tim Kleinig2, Billy O'Brien2, Kenneth Butcher2, Jingfen Zhang2, Jim Jannes2, Qiang Dong2, Mark Parsons2. 1. From 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. 2. From 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.).
Abstract
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.
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.
Authors: Thoralf Thamm; Jia Guo; Jarrett Rosenberg; Tie Liang; Michael P Marks; Soren Christensen; Huy M Do; Stephanie M Kemp; Emma Adair; Irina Eyngorn; Michael Mlynash; Tudor G Jovin; Bart P Keogh; Hui J Chen; Maarten G Lansberg; Gregory W Albers; Greg Zaharchuk Journal: Stroke Date: 2019-10-17 Impact factor: 7.914
Authors: Jason Siegel; Michael A Pizzi; J Brent Peel; David Alejos; Nnenne Mbabuike; Benjamin L Brown; David Hodge; W David Freeman Journal: Curr Cardiol Rep Date: 2017-08 Impact factor: 2.931
Authors: Ana Lima Silva; Ana Sofia Pessoa; Renato Nogueira; José Manuel Araújo; José Nuno Alves; João Pinho; Carla Ferreira Journal: Neurol Sci Date: 2019-11-11 Impact factor: 3.307
Authors: Huiqiao Tian; Mark W Parsons; Christopher R Levi; Xin Cheng; Richard I Aviv; Neil J Spratt; Timothy J Kleinig; Billy O'Brien; Kenneth S Butcher; Longting Lin; Jingfen Zhang; Qiang Dong; Chushuang Chen; Andrew Bivard Journal: Front Neurol Date: 2018-06-06 Impact factor: 4.003
Authors: Elizabeth Holliday; Thomas Lillicrap; Timothy Kleinig; Philip M C Choi; Jane Maguire; Andrew Bivard; Lisa F Lincz; Monica Anne Hamilton-Bruce; Sushma R Rao; Marten F Snel; Paul J Trim; Longting Lin; Mark W Parsons; Bradford B Worrall; Simon Koblar; John Attia; Chris Levi Journal: BMJ Open Date: 2020-04-06 Impact factor: 2.692
Authors: Penny Reeves; Kim Edmunds; Christopher Levi; Longting Lin; Xin Cheng; Richard Aviv; Tim Kleinig; Kenneth Butcher; Jingfen Zhang; Mark Parsons; Andrew Bivard Journal: PLoS One Date: 2018-10-23 Impact factor: 3.240