The Los Angeles motor scale (LAMS) and ASPECTS score are independently associated with DSA ASITN collateral score.

Los Angeles motor scale Stroke collateral status large vessel occlusion mechanical thrombectomy

Journal

Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences
ISSN: 2385-2011
Titre abrégé: Interv Neuroradiol
Pays: United States
ID NLM: 9602695

Informations de publication

Date de publication:
01 Oct 2024
Historique:
medline: 1 10 2024
pubmed: 1 10 2024
entrez: 1 10 2024
Statut: aheadofprint

Résumé

Mechanical thrombectomy (MT) is the treatment standard in eligible patients with acute ischemic stroke (AIS) secondary to large vessel occlusions (LVO). Studies have shown that good collateral status is a strong predictor of MT efficacy, thus making collateral status important to quickly assess. The Los Angeles Motor Scale is a clinically validated tool for identifying LVO in the field. The aim of this study is to investigate whether admission LAMS score is also associated with the American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score on digital subtraction angiography (DSA). We conducted a retrospective multicenter cohort study of consecutive patients presenting with AIS caused by LVO from 9/1/2017 to 10/1/2023 with diagnostically adequate DSA imaging. Demographic, clinical, and imaging data was collected through manual chart review. Both univariate and multivariate analysis were applied to assess associations. A A total of 308 patients (median age: 68, IQR: 57.5-77) were included in the study. On multivariate logistic regression analysis, we found that lower admission LAMS score (adjusted OR: 0.82, 95% CI: 0.68-0.98, Admission LAMS and ASPECTS score are both independently associated with DSA ASITN collateral score. This demonstrates the capability of LAMS to act as a surrogate marker of CS in the field.

Sections du résumé

BACKGROUND BACKGROUND
Mechanical thrombectomy (MT) is the treatment standard in eligible patients with acute ischemic stroke (AIS) secondary to large vessel occlusions (LVO). Studies have shown that good collateral status is a strong predictor of MT efficacy, thus making collateral status important to quickly assess. The Los Angeles Motor Scale is a clinically validated tool for identifying LVO in the field. The aim of this study is to investigate whether admission LAMS score is also associated with the American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score on digital subtraction angiography (DSA).
METHODS METHODS
We conducted a retrospective multicenter cohort study of consecutive patients presenting with AIS caused by LVO from 9/1/2017 to 10/1/2023 with diagnostically adequate DSA imaging. Demographic, clinical, and imaging data was collected through manual chart review. Both univariate and multivariate analysis were applied to assess associations. A
RESULTS RESULTS
A total of 308 patients (median age: 68, IQR: 57.5-77) were included in the study. On multivariate logistic regression analysis, we found that lower admission LAMS score (adjusted OR: 0.82, 95% CI: 0.68-0.98,
CONCLUSIONS CONCLUSIONS
Admission LAMS and ASPECTS score are both independently associated with DSA ASITN collateral score. This demonstrates the capability of LAMS to act as a surrogate marker of CS in the field.

Identifiants

pubmed: 39350749
doi: 10.1177/15910199241282434
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

15910199241282434

Auteurs

Richard Wang (R)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Dhairya A Lakhani (DA)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Aneri B Balar (AB)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Sadra Sepehri (S)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Nathan Hyson (N)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Licia P Luna (LP)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Andrew Cho (A)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Argye E Hillis (AE)

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

Manisha Koneru (M)

Cooper Medical School, Rowan University, Camden, NJ, USA.

Meisam Hoseinyazdi (M)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Hanzhang Lu (H)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Janet Mei (J)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Risheng Xu (R)

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

Mehreen Nabi (M)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Ishan Mazumdar (I)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Victor C Urrutia (VC)

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

Kevin Chen (K)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Judy Huang (J)

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

Kambiz Nael (K)

Department of Radiology, University of California San Francisco, San Francisco, CA, USA.

Vivek S Yedavalli (VS)

Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Classifications MeSH