The coronal plane maximum diameter of deep intracerebral hemorrhage predicts functional outcome more accurately than hematoma volume.

CT Intracerebral hemorrhage computed tomography hematoma outcome shape feature volume

Journal

International journal of stroke : official journal of the International Stroke Society
ISSN: 1747-4949
Titre abrégé: Int J Stroke
Pays: United States
ID NLM: 101274068

Informations de publication

Date de publication:
Aug 2022
Historique:
pubmed: 28 9 2021
medline: 9 8 2022
entrez: 27 9 2021
Statut: ppublish

Résumé

Among prognostic imaging variables, the hematoma volume on admission computed tomography (CT) has long been considered the strongest predictor of outcome and mortality in intracerebral hemorrhage. To examine whether different features of hematoma shape are associated with functional outcome in deep intracerebral hemorrhage. We analyzed 790 patients from the ATACH-2 trial, and 14 shape features were quantified. We calculated Spearman's Rho to assess the correlation between shape features and three-month modified Rankin scale (mRS) score, and the area under the receiver operating characteristic curve (AUC) to quantify the association between shape features and poor outcome defined as mRS>2 as well as mRS > 3. Among 14 shape features, the maximum intracerebral hemorrhage diameter in the coronal plane was the strongest predictor of functional outcome, with a maximum coronal diameter >∼3.5 cm indicating higher three-month mRS scores. The maximum coronal diameter versus hematoma volume yielded a Rho of 0.40 versus 0.35 ( A coronal-plane maximum diameter measurement offers greater prognostic value in deep intracerebral hemorrhage than hematoma volume. This simple shape metric may expedite assessment of admission head CTs, offer a potential biomarker for hematoma size eligibility criteria in clinical trials, and may substitute volume in prognostic intracerebral hemorrhage scoring systems.

Sections du résumé

BACKGROUND
Among prognostic imaging variables, the hematoma volume on admission computed tomography (CT) has long been considered the strongest predictor of outcome and mortality in intracerebral hemorrhage.
AIMS
To examine whether different features of hematoma shape are associated with functional outcome in deep intracerebral hemorrhage.
METHODS
We analyzed 790 patients from the ATACH-2 trial, and 14 shape features were quantified. We calculated Spearman's Rho to assess the correlation between shape features and three-month modified Rankin scale (mRS) score, and the area under the receiver operating characteristic curve (AUC) to quantify the association between shape features and poor outcome defined as mRS>2 as well as mRS > 3.
RESULTS
Among 14 shape features, the maximum intracerebral hemorrhage diameter in the coronal plane was the strongest predictor of functional outcome, with a maximum coronal diameter >∼3.5 cm indicating higher three-month mRS scores. The maximum coronal diameter versus hematoma volume yielded a Rho of 0.40 versus 0.35 (
CONCLUSIONS
A coronal-plane maximum diameter measurement offers greater prognostic value in deep intracerebral hemorrhage than hematoma volume. This simple shape metric may expedite assessment of admission head CTs, offer a potential biomarker for hematoma size eligibility criteria in clinical trials, and may substitute volume in prognostic intracerebral hemorrhage scoring systems.

Identifiants

pubmed: 34569877
doi: 10.1177/17474930211050749
pmc: PMC9005571
mid: NIHMS1774032
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

777-784

Subventions

Organisme : NINDS NIH HHS
ID : U24 NS107136
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS097728
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : NICHD NIH HHS
ID : K01 HD091283
Pays : United States
Organisme : NIA NIH HHS
ID : K76 AG059992
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG021342
Pays : United States
Organisme : NINDS NIH HHS
ID : U24 NS107215
Pays : United States
Organisme : NINDS NIH HHS
ID : U01 NS106513
Pays : United States
Organisme : Doris Duke Charitable Foundation
ID : 2020097
Pays : United States
Organisme : NHLBI NIH HHS
ID : T35 HL007649
Pays : United States
Organisme : NINDS NIH HHS
ID : K23 NS110980
Pays : United States
Organisme : NINDS NIH HHS
ID : K23 NS118056
Pays : United States
Organisme : NINR NIH HHS
ID : R01 NR018335
Pays : United States
Organisme : NINDS NIH HHS
ID : K08 NS078110
Pays : United States
Organisme : NINDS NIH HHS
ID : R03 NS112859
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS095993
Pays : United States

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Auteurs

Stefan P Haider (SP)

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany.

Adnan I Qureshi (AI)

Zeenat Qureshi Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, USA.

Abhi Jain (A)

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.

Hishan Tharmaseelan (H)

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.

Elisa R Berson (ER)

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.

Shahram Majidi (S)

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Christopher G Filippi (CG)

Department of Radiology, Tufts University School of Medicine, Boston, MA, USA.

Adrian Mak (A)

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité - Universitätsmedizin Berlin, Berlin, Germany.

David J Werring (DJ)

Stroke Research Centre, University College London, Queen Square Institute of Neurology, London, UK.

Julian N Acosta (JN)

Department of Neurology, Yale School of Medicine, New Haven, CT, USA.

Ajay Malhotra (A)

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.

Jennifer A Kim (JA)

Department of Neurology, Yale School of Medicine, New Haven, CT, USA.

Lauren H Sansing (LH)

Department of Neurology, Yale School of Medicine, New Haven, CT, USA.

Guido J Falcone (GJ)

Department of Neurology, Yale School of Medicine, New Haven, CT, USA.

Kevin N Sheth (KN)

Department of Neurology, Yale School of Medicine, New Haven, CT, USA.

Seyedmehdi Payabvash (S)

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.

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Classifications MeSH