Predicting Language Improvement in Acute Stroke Patients Presenting with Aphasia: A Multivariate Logistic Model Using Location-Weighted Atlas-Based Analysis of Admission CT Perfusion Scans
Payabvash S, Kamalian S, Fung S, Wang Y, Passanese J, Kamalian S, Souza LC, Kemmling A, Harris GJ, Halpern EF, González RG, Furie KL, Lev MH. Predicting Language Improvement in Acute Stroke Patients Presenting with Aphasia: A Multivariate Logistic Model Using Location-Weighted Atlas-Based Analysis of Admission CT Perfusion Scans. American Journal Of Neuroradiology 2010, 31: 1661-1668. PMID: 20488905, PMCID: PMC3640318, DOI: 10.3174/ajnr.a2125.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAphasiaBrainComputer SimulationFemaleHumansLogistic ModelsMaleModels, NeurologicalMultivariate AnalysisPattern Recognition, AutomatedPerfusion ImagingPrognosisRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSensitivity and SpecificityStrokeSubtraction TechniqueTomography, X-Ray ComputedConceptsBrain CTPNIHSS scoreStroke onsetFunctional outcomeFirst-time ischemic strokeProximal cerebral artery occlusionMultiple logistic regression analysisMultivariate logistic regression modelMultivariate modelDischarge NIHSS scoreTotal NIHSS scoreAcute stroke patientsCerebral artery occlusionTime of dischargeCT perfusion imagingLogistic regression analysisMultivariate logistic modelCT perfusion scansLogistic regression modelsAdmission CTAArtery occlusionInfarct volumeIschemic strokeClinical predictorsConsecutive patients