Pretreatment CTP Collateral Parameters Predict Good Outcomes in Successfully Recanalized Middle Cerebral Artery Distal Medium Vessel Occlusions.

Acute ischemic stroke CT perfusion Collateral imaging Medium vessel occlusion Stroke imaging instead

Journal

Clinical neuroradiology
ISSN: 1869-1447
Titre abrégé: Clin Neuroradiol
Pays: Germany
ID NLM: 101526693

Informations de publication

Date de publication:
28 Dec 2023
Historique:
received: 05 09 2023
accepted: 23 11 2023
medline: 29 12 2023
pubmed: 29 12 2023
entrez: 28 12 2023
Statut: aheadofprint

Résumé

Distal medium vessel occlusions (DMVOs) account for a large percentage of vessel occlusions resulting in acute ischemic stroke (AIS) with disabling symptoms. We aim to assess whether pretreatment quantitative CTP collateral status (CS) parameters can serve as imaging biomarkers for good clinical outcomes prediction in successfully recanalized middle cerebral artery (MCA) DMVOs. We performed a retrospective analysis of consecutive patients with AIS secondary to primary MCA-DMVOs who were successfully recanalized by mechanical thrombectomy (MT) defined as modified thrombolysis in cerebral infarction (mTICI) 2b, 2c, or 3. We evaluated the association between the CBV index and HIR independently with good clinical outcomes (modified Rankin score 0-2) using Spearman rank correlation, logistic regression, and ROC analyses. From 22 August 2018 to 18 October 2022 8/22/2018 to 10/18/2022, 60 consecutive patients met our inclusion criteria (mean age 71.2 ± 13.9 years old [mean ± SD], 35 female). The CBV index (r = -0.693, p < 0.001) and HIR (0.687, p < 0.001) strongly correlated with 90-day mRS. A CBV index ≥ 0.7 (odds ratio, OR, 2.27, range 6.94-21.23 [OR] 2.27 [6.94-21.23], p = 0.001)) and lower likelihood of prior stroke (0.13 [0.33-0.86]), p = 0.024)) were independently associated with good outcomes. The ROC analysis demonstrated good performance of the CBV index in predicting good 90-day mRS (AUC 0.73, p = 0.003) with a threshold of 0.7 for optimal sensitivity (71% [52.0-85.8%]) and specificity (76% [54.9-90.6%]). The HIR also demonstrated adequate performance in predicting good 90-day mRS (AUC 0.77, p = 0.001) with a threshold of 0.3 for optimal sensitivity (64.5% [45.4-80.8%]) and specificity (76.0% [54.9-90.6%]). A CBV index ≥ 0.7 may be independently associated with good clinical outcomes in our cohort of AIS caused by MCA-DMVOs that were successfully treated with MT. Furthermore, a HIR < 0.3 is also associated with good clinical outcomes. This is the first study of which we are aware to identify a CBV index threshold for MCA-DMVOs.

Sections du résumé

BACKGROUND/PURPOSE OBJECTIVE
Distal medium vessel occlusions (DMVOs) account for a large percentage of vessel occlusions resulting in acute ischemic stroke (AIS) with disabling symptoms. We aim to assess whether pretreatment quantitative CTP collateral status (CS) parameters can serve as imaging biomarkers for good clinical outcomes prediction in successfully recanalized middle cerebral artery (MCA) DMVOs.
METHODS METHODS
We performed a retrospective analysis of consecutive patients with AIS secondary to primary MCA-DMVOs who were successfully recanalized by mechanical thrombectomy (MT) defined as modified thrombolysis in cerebral infarction (mTICI) 2b, 2c, or 3. We evaluated the association between the CBV index and HIR independently with good clinical outcomes (modified Rankin score 0-2) using Spearman rank correlation, logistic regression, and ROC analyses.
RESULTS RESULTS
From 22 August 2018 to 18 October 2022 8/22/2018 to 10/18/2022, 60 consecutive patients met our inclusion criteria (mean age 71.2 ± 13.9 years old [mean ± SD], 35 female). The CBV index (r = -0.693, p < 0.001) and HIR (0.687, p < 0.001) strongly correlated with 90-day mRS. A CBV index ≥ 0.7 (odds ratio, OR, 2.27, range 6.94-21.23 [OR] 2.27 [6.94-21.23], p = 0.001)) and lower likelihood of prior stroke (0.13 [0.33-0.86]), p = 0.024)) were independently associated with good outcomes. The ROC analysis demonstrated good performance of the CBV index in predicting good 90-day mRS (AUC 0.73, p = 0.003) with a threshold of 0.7 for optimal sensitivity (71% [52.0-85.8%]) and specificity (76% [54.9-90.6%]). The HIR also demonstrated adequate performance in predicting good 90-day mRS (AUC 0.77, p = 0.001) with a threshold of 0.3 for optimal sensitivity (64.5% [45.4-80.8%]) and specificity (76.0% [54.9-90.6%]).
CONCLUSION CONCLUSIONS
A CBV index ≥ 0.7 may be independently associated with good clinical outcomes in our cohort of AIS caused by MCA-DMVOs that were successfully treated with MT. Furthermore, a HIR < 0.3 is also associated with good clinical outcomes. This is the first study of which we are aware to identify a CBV index threshold for MCA-DMVOs.

Identifiants

pubmed: 38155255
doi: 10.1007/s00062-023-01371-2
pii: 10.1007/s00062-023-01371-2
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.

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Auteurs

Vivek Yedavalli (V)

Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Phipps B112-D, 21287, Baltimore, MD, USA. Vyedava1@jhmi.edu.
Department of Neurology, Stanford University School of Medicine, Stanford, Ca, USA. Vyedava1@jhmi.edu.

Manisha Koneru (M)

Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Phipps B112-D, 21287, Baltimore, MD, USA.

Omar Hamam (O)

Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Phipps B112-D, 21287, Baltimore, MD, USA.

Meisam Hoseinyazdi (M)

Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Phipps B112-D, 21287, Baltimore, MD, USA.

Elisabeth Breese Marsh (EB)

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Raf Llinas (R)

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Victor Urrutia (V)

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Richard Leigh (R)

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Fernando Gonzalez (F)

Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Risheng Xu (R)

Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Justin Caplan (J)

Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Judy Huang (J)

Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Hanzhang Lu (H)

Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Phipps B112-D, 21287, Baltimore, MD, USA.

Max Wintermark (M)

Department of Radiology, University of Texas, MD Anderson, TX, USA.

Jeremy Heit (J)

Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

Adrien Guenego (A)

Department of Radiology, Université libre de Bruxelles, Bruxelles, Belgium.

Greg Albers (G)

Department of Radiology, Université libre de Bruxelles, Bruxelles, Belgium.

Kambiz Nael (K)

Department of Radiology, David Geffen UCLA School of Medicine, Los Angeles, Ca, USA.

Argye Hillis (A)

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Classifications MeSH