Anterior ischemic stroke: Analysis of the multivariable CT-based models for prediction of clinical outcome |
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| Affiliation: | 1. Department of Radiology, George Washington University Hospital, USA;2. Non-Communicable Disease Research Center, Endocrinology and Metabolism Research Center, Tehran University of Medical Sciences, Tehran, Iran;3. Department of Neurology, Atrium Health, USA;1. Department of Radiology, George Washington University Hospital, USA;2. Non-Communicable Disease Research Center, Endocrinology and Metabolism Research Center, Tehran University of Medical Sciences, Tehran, Iran;3. Department of Neurology, Atrium Health, USA;1. Department of Neurology, Mayo Clinic, 4500 San Pablo Rd., Jacksonville, FL 32224, United States;2. Department of Ophthalmology, Mayo Clinic, Jacksonville, FL, United States;1. Department of Physical Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil;2. Department of Occupational Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil;3. Department of Neurology, Hospital Risoleta Tolentino Neves, Belo Horizonte, MG, Brazil;1. Department of Epidemiology, School of Public Health, Chongqing Medical University, Chongqing 400016, China;2. Department of Chronic Diseases, Yubei District Center for Disease Control and Prevention, Chongqing 401120, China;1. College of Pharmacy, Gulf Medical University, Ajman, United Arab Emirates;2. School of Pharmacy, Lebanese International University, Beirut, Lebanon;3. School of Medicine and Medical Sciences, Holy Spirit University of Kaslik (USEK), P.O Box: 446, Jounieh lebanon;4. Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon;6. INSPECT-LB (Institut National de Sant e Publique, d’Épid emiologie Clinique et de Toxicologie-Liban);7. UMR U955 INSERM, Institut Mondor de Recherche Biomédicale, Université Paris-Est Créteil, 94010 Créteil, France;8. École Doctorale Sciences de la Vie et de la Santé, Université Paris-Est Créteil, 94010 Créteil, France;9. Faculty of Pharmacy, Lebanese University, Beirut, Lebanon;10. Faculty of Public health, Lebanese University, Fanar, Lebanon;11. Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, Cyprus;12. School of Medicine, Lebanese American University, Byblos, Lebanon;13. INSERM U955-E01, IMRB, Henri Mondor Hospital, Créteil, France;14. Department of Neurology, Henri Mondor Hospital, AP-HP, Créteil, France;1. State University of New York, Downstate College of Medicine, Brooklyn, NY;2. University of Rochester Medical Center, Department of Neurology, Rochester, NY |
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| Abstract: | ObjectiveTo determine the predictive value of multiple CT-based measurements, individually and collectively, including arterial collateral filling (AC), tissue perfusion parameters, as well as cortical venous (CV) and medullary venous (MV) outflow, in patients with acute ischemic stroke (AIS).MethodsWe retrospectively reviewed a database of patients with AIS in the middle cerebral artery distribution, who underwent evaluation by multiphase CT-angiography and perfusion. AC pial filling was evaluated using a multiphase CTA imaging. The CV status was scored using the adopted PRECISE system based on contrast opacification of the main cortical veins. The MV status was defined by the degree of contrast opacification of medullary veins in one cerebral hemisphere as compared to the contralateral hemisphere. The perfusion parameters were calculated using FDA-approved automated software. A good clinical outcome was defined as a Modified Rankin Scale of 0-2 at 90 days.ResultsA total of 64 patients were included. Each of the CT-based measurements could predict clinical outcomes independently (P<0.05). AC pial filling and perfusion core based models did slightly better compared to each of the other models (AUC = 0.66). Among models with two variables, the perfusion core combined with MV status had the highest AUC=0.73 followed by a combination of MV status and AC (AUC=0.72). Multivariable modeling with all four variables resulted in the highest predictive value (AUC=0.77).ConclusionThe combination of arterial collateral flow, tissue perfusion, and venous outflow provides a more accurate prediction of clinical outcome in AIS than each variable alone. This additive effect of these techniques suggests that the information collected by each of these methods only partially overlaps. |
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