- Title
- Assessing the Relative Value of CT Perfusion Compared to Non-contrast CT and CT Angiography in Prognosticating Reperfusion-Eligible Acute Ischemic Stroke Patients
- Creator
- Bivard, Andrew; Levi, Christopher; Parsons, Mark; Lin, Longting; Cheng, Xin; Aviv, Richard; Spratt, Neil J.; Kleinig, Tim; Butcher, Kenneth; Chen, Chushuang; Dong, Qiang
- Relation
- NHMRC.APP1013719 http://purl.org/au-research/grants/nhmrc/1013719
- Relation
- Frontiers in Neurology Vol. 12, Issue Septembeer 2021, no. 736768
- Publisher Link
- http://dx.doi.org/10.3389/fneur.2021.736768
- Publisher
- Frontiers Research Foundation
- Resource Type
- journal article
- Date
- 2021
- Description
- In the present study we sought to measure the relative statistical value of various multimodal CT protocols at identifying treatment responsiveness in patients being considered for thrombolysis. We used a prospectively collected cohort of acute ischemic stroke patients being assessed for IV-alteplase, who had CT-perfusion (CTP) and CT-angiography (CTA) before a treatment decision. Linear regression and receiver operator characteristic curve analysis were performed to measure the prognostic value of models incorporating each imaging modality. One thousand five hundred and sixty-two sub-4.5 h ischemic stroke patients were included in this study. A model including clinical variables, alteplase treatment, and NCCT ASPECTS was weak (R2 0.067, P < 0.001, AUC 0.605) at predicting 90 day mRS. A second model, including dynamic CTA variables (collateral grade, occlusion severity) showed better predictive accuracy for patient outcome (R2 0.381, P < 0.001, AUC 0.781). A third model incorporating CTP variables showed very high predictive accuracy (R2 0.488, P < 0.001, AUC 0.899). Combining all three imaging modalities variables also showed good predictive accuracy for outcome but did not improve on the CTP model (R2 0.439, P < 0.001, AUC 0.825). CT perfusion predicts patient outcomes from alteplase therapy more accurately than models incorporating NCCT and/or CT angiography. This data has implications for artificial intelligence or machine learning models.
- Subject
- reperfusion; brain imaging; computed tomography angiography; CT perfusion; ischemic stroke; SDG 3; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1448928
- Identifier
- uon:43520
- Identifier
- ISSN:1664-2295
- Language
- eng
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