CT perfusion based rCBF <38% volume is independently and negatively associated with digital subtraction angiography collateral score in anterior circulation large vessel occlusions.

Acute ischemic stroke CT perfusion collateral status rCBF <38%

Journal

The neuroradiology journal
ISSN: 2385-1996
Titre abrégé: Neuroradiol J
Pays: United States
ID NLM: 101295103

Informations de publication

Date de publication:
25 Mar 2024
Historique:
medline: 26 3 2024
pubmed: 26 3 2024
entrez: 26 3 2024
Statut: aheadofprint

Résumé

Collateral status (CS) is an important biomarker of functional outcomes in patients with acute ischemic stroke secondary to large vessel occlusion (AIS-LVO). Pretreatment CT perfusion (CTP) parameters serve as reliable surrogates of collateral status (CS). In this study, we aim to assess the relationship between the relative cerebral blood flow less than 38% (rCBF <38%), with the reference standard American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score (CS) on DSA. In this prospectively collected, retrospectively reviewed analysis, inclusion criteria were as follows: (a) CT angiography (CTA) confirmed anterior circulation large vessel occlusion from 9/1/2017 to 10/01/2023; (b) diagnostic CT perfusion; and (c) underwent mechanical thrombectomy with documented ASITN CS. The ratios of the CTP-derived CBF values were calculated by dividing the values of the ischemic lesion by the corresponding values of the contralateral normal region (which were defined as rCBF). Spearman's rank correlation and logistic regression analysis were performed to determine the relationship of rCBF <38% lesion volume with DSA ASITN CS. In total, 223 patients [mean age: 67.77 ± 15.76 years, 56.1% ( Greater volume of tissue with rCBF <38% is independently associated with better DSA CS. rCBF <38% is a useful adjunct tool in collateralization-based prognostication. Future studies are needed to expand our understanding of the role of rCBF <38% within the decision-making in patients with AIS-LVO.

Sections du résumé

BACKGROUND BACKGROUND
Collateral status (CS) is an important biomarker of functional outcomes in patients with acute ischemic stroke secondary to large vessel occlusion (AIS-LVO). Pretreatment CT perfusion (CTP) parameters serve as reliable surrogates of collateral status (CS). In this study, we aim to assess the relationship between the relative cerebral blood flow less than 38% (rCBF <38%), with the reference standard American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score (CS) on DSA.
METHODS METHODS
In this prospectively collected, retrospectively reviewed analysis, inclusion criteria were as follows: (a) CT angiography (CTA) confirmed anterior circulation large vessel occlusion from 9/1/2017 to 10/01/2023; (b) diagnostic CT perfusion; and (c) underwent mechanical thrombectomy with documented ASITN CS. The ratios of the CTP-derived CBF values were calculated by dividing the values of the ischemic lesion by the corresponding values of the contralateral normal region (which were defined as rCBF). Spearman's rank correlation and logistic regression analysis were performed to determine the relationship of rCBF <38% lesion volume with DSA ASITN CS.
RESULTS RESULTS
In total, 223 patients [mean age: 67.77 ± 15.76 years, 56.1% (
CONCLUSION CONCLUSIONS
Greater volume of tissue with rCBF <38% is independently associated with better DSA CS. rCBF <38% is a useful adjunct tool in collateralization-based prognostication. Future studies are needed to expand our understanding of the role of rCBF <38% within the decision-making in patients with AIS-LVO.

Identifiants

pubmed: 38528780
doi: 10.1177/19714009241242639
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

19714009241242639

Déclaration de conflit d'intérêts

Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: VSY, JJH, and GWA are consultants for RAPID.AI, and GWA holds RAPID.AI equity.

Auteurs

Dhairya A Lakhani (DA)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Aneri B Balar (AB)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Manisha Koneru (M)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Sijin Wen (S)

Department of Biostatistics, West Virginia University, USA.

Burak Berksu Ozkara (BB)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Richard Wang (R)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Meisam Hoseinyazdi (M)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Mehreen Nabi (M)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Ishan Mazumdar (I)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Andrew Cho (A)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Kevin Chen (K)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Sadra Sepehri (S)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Nathan Hyson (N)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Risheng Xu (R)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Victor Urrutia (V)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Licia Luna (L)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Argye E Hillis (AE)

Department of Neurology, Johns Hopkins University, USA.

Jeremy J Heit (JJ)

Department of Neurology, Stanford University, USA.

Greg W Albers (GW)

Department of Neurology, Stanford University, USA.

Ansaar T Rai (AT)

Department of Neuroradiology, West Virginia University, USA.

Vivek S Yedavalli (VS)

Department of Radiology and Radiological Sciences, Johns Hopkins University, USA.

Classifications MeSH