Electrical impedance measurements can identify red blood cell-rich content in acute ischemic stroke clots

acute ischemic stroke clot composition electrical impedance first-pass effect mechanical thrombectomy

Journal

Research and practice in thrombosis and haemostasis
ISSN: 2475-0379
Titre abrégé: Res Pract Thromb Haemost
Pays: United States
ID NLM: 101703775

Informations de publication

Date de publication:
Mar 2024
Historique:
received: 22 02 2024
accepted: 07 03 2024
medline: 15 4 2024
pubmed: 15 4 2024
entrez: 15 4 2024
Statut: epublish

Résumé

Electrochemical impedance spectroscopy can determine characteristics such as cell density, size, and shape. The development of an electrical impedance-based medical device to estimate acute ischemic stroke (AIS) clot characteristics could improve stroke patient outcomes by informing clinical decision making. To assess how well electrical impedance combined with machine learning identified red blood cell (RBC)-rich composition of AIS clots A total of 253 clots from 231 patients who underwent thrombectomy in 5 hospitals in France, Japan, Serbia, and Spain between February 2021 and October 2023 were analyzed in the Clotbase International Registry. Electrical impedance measurements were taken following clot retrieval by thrombectomy, followed by Martius Scarlet Blue staining. The clot components were quantified via Orbit Image Analysis, and RBC percentages were correlated with the RBC estimations made by the electrical impedance machine learning model. Quantification by Martius Scarlet Blue staining identified RBCs as the major component in clots (RBCs, 37.6%; white blood cells, 5.7%; fibrin, 25.5%; platelets/other, 30.3%; and collagen, 1%). The impedance-based RBC estimation correlated well with the RBC content determined by histology, with a slope of 0.9 and Spearman's correlation of r = 0.7. Clots removed in 1 pass were significantly richer in RBCs and clots with successful recanalization in 1 pass (modified first-pass effect) were richer in RBCs as assessed using histology and impedance signature. Electrical impedance estimations of RBC content in AIS clots are consistent with histologic findings and may have potential for clinically relevant parameters.

Sections du résumé

Background UNASSIGNED
Electrochemical impedance spectroscopy can determine characteristics such as cell density, size, and shape. The development of an electrical impedance-based medical device to estimate acute ischemic stroke (AIS) clot characteristics could improve stroke patient outcomes by informing clinical decision making.
Objectives UNASSIGNED
To assess how well electrical impedance combined with machine learning identified red blood cell (RBC)-rich composition of AIS clots
Methods UNASSIGNED
A total of 253 clots from 231 patients who underwent thrombectomy in 5 hospitals in France, Japan, Serbia, and Spain between February 2021 and October 2023 were analyzed in the Clotbase International Registry. Electrical impedance measurements were taken following clot retrieval by thrombectomy, followed by Martius Scarlet Blue staining. The clot components were quantified via Orbit Image Analysis, and RBC percentages were correlated with the RBC estimations made by the electrical impedance machine learning model.
Results UNASSIGNED
Quantification by Martius Scarlet Blue staining identified RBCs as the major component in clots (RBCs, 37.6%; white blood cells, 5.7%; fibrin, 25.5%; platelets/other, 30.3%; and collagen, 1%). The impedance-based RBC estimation correlated well with the RBC content determined by histology, with a slope of 0.9 and Spearman's correlation of r = 0.7. Clots removed in 1 pass were significantly richer in RBCs and clots with successful recanalization in 1 pass (modified first-pass effect) were richer in RBCs as assessed using histology and impedance signature.
Conclusion UNASSIGNED
Electrical impedance estimations of RBC content in AIS clots are consistent with histologic findings and may have potential for clinically relevant parameters.

Identifiants

pubmed: 38617048
doi: 10.1016/j.rpth.2024.102373
pii: S2475-0379(24)00062-1
pmc: PMC11015511
doi:

Types de publication

Journal Article

Langues

eng

Pagination

102373

Informations de copyright

© 2024 The Author(s).

Auteurs

Cansu Sahin (C)

Department of Physiology, University of Galway, Galway, Ireland.
Centre for Research in Medical Devices (CÚRAM)- Science Foundation Ireland (SFI), University of Galway, Galway, Ireland.

Alice Giraud (A)

Sensome, Massy, France.

Duaa Jabrah (D)

Department of Physiology, University of Galway, Galway, Ireland.

Smita Patil (S)

Department of Physiology, University of Galway, Galway, Ireland.
Centre for Research in Medical Devices (CÚRAM)- Science Foundation Ireland (SFI), University of Galway, Galway, Ireland.

Pierluca Messina (P)

Sensome, Massy, France.

Franz Bozsak (F)

Sensome, Massy, France.

Jean Darcourt (J)

Department of Diagnostic and Therapeutic Neuroradiology, Centre Hospitalier Universitaire (CHU) de Toulouse, Toulouse, France.

Federico Sacchetti (F)

Department of Diagnostic and Therapeutic Neuroradiology, Centre Hospitalier Universitaire (CHU) de Toulouse, Toulouse, France.

Anne-Christine Januel (AC)

Department of Diagnostic and Therapeutic Neuroradiology, Centre Hospitalier Universitaire (CHU) de Toulouse, Toulouse, France.

Guillaume Bellanger (G)

Department of Diagnostic and Therapeutic Neuroradiology, Centre Hospitalier Universitaire (CHU) de Toulouse, Toulouse, France.

Jorge Pagola (J)

Department of Neurology, University Hospital Vall d'Hebron, Barcelona, Spain.

Jesus Juega (J)

Department of Neurology, University Hospital Vall d'Hebron, Barcelona, Spain.

Hirotoshi Imamura (H)

Department of Neurosurgery, Kobe City Medical Center General Hospital, Kobe, Japan.

Tsuyoshi Ohta (T)

Department of Neurosurgery, Kobe City Medical Center General Hospital, Kobe, Japan.

Laurent Spelle (L)

Department of Interventional Neuroradiology, Bicêtre Hospital, Le Kremlin-Bicêtre, France.

Vanessa Chalumeau (V)

Department of Interventional Neuroradiology, Bicêtre Hospital, Le Kremlin-Bicêtre, France.

Uros Mircic (U)

Department of Neuroradiology, Centre for Radiology and Magnetic Resonance Imaging (MRI), University Clinical Center of Serbia, Belgrade, Serbia.

Predrag Stanarčević (P)

Neurology Clinic, University Clinical Center of Serbia, Belgrade, Serbia.

Ivan Vukašinović (I)

Department of Neuroradiology, Centre for Radiology and Magnetic Resonance Imaging (MRI), University Clinical Center of Serbia, Belgrade, Serbia.

Marc Ribo (M)

Department of Neurology, University Hospital Vall d'Hebron, Barcelona, Spain.

Nobuyuki Sakai (N)

Department of Neurosurgery, Kobe City Medical Center General Hospital, Kobe, Japan.

Christophe Cognard (C)

Department of Diagnostic and Therapeutic Neuroradiology, Centre Hospitalier Universitaire (CHU) de Toulouse, Toulouse, France.

Karen Doyle (K)

Department of Physiology, University of Galway, Galway, Ireland.
Centre for Research in Medical Devices (CÚRAM)- Science Foundation Ireland (SFI), University of Galway, Galway, Ireland.

Classifications MeSH