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Cortical atrophy on baseline computed tomography imaging predicts clinical outcome in patients undergoing endovascular treatment for acute ischemic stroke

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Title: Cortical atrophy on baseline computed tomography imaging predicts clinical outcome in patients undergoing endovascular treatment for acute ischemic stroke
Authors: Gianluca Brugnara, Adrian Engel, Jessica Jesser, Peter Arthur Ringleb, Jan Purrucker, Markus A. Möhlenbruch, Martin Bendszus, Ulf Neuberger
Source: Eur Radiol
Publisher Information: Springer Science and Business Media LLC, 2023.
Publication Year: 2023
Subject Terms: Stroke, Treatment Outcome, Endovascular Procedures, Medizin, Humans, Neuro, Atrophy, Tomography, X-Ray Computed, Brain Ischemia/surgery [MeSH], Atrophy [MeSH], Endovascular Procedures/methods [MeSH], Thrombectomy/methods [MeSH], Humans [MeSH], Treatment Outcome [MeSH], Retrospective Studies [MeSH], Tomography, X-Ray Computed/methods [MeSH], Prognosis, Stroke/surgery [MeSH], Ischemic Stroke [MeSH], Thrombectomy, Stroke/diagnostic imaging [MeSH], Brain Ischemia/diagnostic imaging [MeSH], Ischemic stroke, Ischemic Stroke, Brain Ischemia, Retrospective Studies
Description: Objective Multiple variables beyond the extent of recanalization can impact the clinical outcome after acute ischemic stroke due to large vessel occlusions. Here, we assessed the influence of small vessel disease and cortical atrophy on clinical outcome using native cranial computed tomography (NCCT) in a large single-center cohort. Methods A total of 1103 consecutive patients who underwent endovascular treatment (EVT) due to occlusion of the middle cerebral artery territory were included. NCCT data were visually assessed for established markers of age-related white matter changes (ARWMC) and brain atrophy. All images were evaluated separately by two readers to assess the inter-observer variability. Regression and machine learning models were built to determine the predictive relevance of ARWMC and atrophy in the presence of important baseline clinical and imaging metrics. Results Patients with favorable outcome presented lower values for all measured metrics of pre-existing brain deterioration (p p p p Conclusion NCCT-based cortical atrophy and ARWMC scores on NCCT were strong and independent predictors of clinical outcome after EVT. Clinical relevance statement Visual assessment of cortical atrophy and age-related white matter changes on CT could improve the prediction of clinical outcome after thrombectomy in machine learning models which may be integrated into existing clinical routines and facilitate patient selection. Key Points • Cortical atrophy and age-related white matter changes were quantified using CT-based visual scores. • Atrophy and age-related white matter change scores independently predicted clinical outcome after mechanical thrombectomy and improved machine learning–based prediction models. • Both scores could easily be integrated into existing clinical routines and prediction models.
Document Type: Article
Other literature type
Language: English
ISSN: 1432-1084
DOI: 10.1007/s00330-023-10107-2
Access URL: https://pubmed.ncbi.nlm.nih.gov/37581657
https://repository.publisso.de/resource/frl:6518579
https://www.ncbi.nlm.nih.gov/pubmed/37581657
https://doi.org/10.1007/s00330-023-10107-2
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85167895882
Rights: CC BY
Accession Number: edsair.doi.dedup.....c727475a4854a653d5e016db1cdc303e
Database: OpenAIRE
Description
Abstract:Objective Multiple variables beyond the extent of recanalization can impact the clinical outcome after acute ischemic stroke due to large vessel occlusions. Here, we assessed the influence of small vessel disease and cortical atrophy on clinical outcome using native cranial computed tomography (NCCT) in a large single-center cohort. Methods A total of 1103 consecutive patients who underwent endovascular treatment (EVT) due to occlusion of the middle cerebral artery territory were included. NCCT data were visually assessed for established markers of age-related white matter changes (ARWMC) and brain atrophy. All images were evaluated separately by two readers to assess the inter-observer variability. Regression and machine learning models were built to determine the predictive relevance of ARWMC and atrophy in the presence of important baseline clinical and imaging metrics. Results Patients with favorable outcome presented lower values for all measured metrics of pre-existing brain deterioration (p p p p Conclusion NCCT-based cortical atrophy and ARWMC scores on NCCT were strong and independent predictors of clinical outcome after EVT. Clinical relevance statement Visual assessment of cortical atrophy and age-related white matter changes on CT could improve the prediction of clinical outcome after thrombectomy in machine learning models which may be integrated into existing clinical routines and facilitate patient selection. Key Points • Cortical atrophy and age-related white matter changes were quantified using CT-based visual scores. • Atrophy and age-related white matter change scores independently predicted clinical outcome after mechanical thrombectomy and improved machine learning–based prediction models. • Both scores could easily be integrated into existing clinical routines and prediction models.
ISSN:14321084
DOI:10.1007/s00330-023-10107-2