Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy

Jean Darcourt; Waleed Brinjikji; Olivier François; Alice Giraud; Collin R. Johnson; Smita Patil; Senna Staessens; Ramanathan Kadirvel; Mahmoud H. Mohammaden; Leonardo Pisani; Gabriel Martins Rodrigues; Nicole M. Cancelliere; Vitor Mendes Pereira; Franz Bozsak; Karen Doyle; Simon F. De Meyer; Pierluca Messina; David Kallmes; Christophe Cognard; Raul G. Nogueira

doi:10.1177/15910199231175377

Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy

Jean Darcourt, Waleed Brinjikji, Olivier François, Alice Giraud, Collin R. Johnson, Smita Patil, Senna Staessens, Ramanathan Kadirvel, Mahmoud H. Mohammaden, Leonardo Pisani, Gabriel Martins Rodrigues, Nicole M. Cancelliere, Vitor Mendes Pereira, Franz Bozsak, Karen Doyle, Simon F. De Meyer, Pierluca Messina, David Kallmes, Christophe Cognard, Raul G. Nogueira

Radiology

Research output: Contribution to journal › Article › peer-review

Abstract

Background: Intra-procedural characterization of stroke thromboemboli might guide mechanical thrombectomy (MT) device choice to improve recanalization rates. Electrochemical impedance spectroscopy (EIS) has been used to characterize various biological tissues in real time but has not been used in thrombus. Objective: To perform a feasibility study of EIS analysis of thrombi retrieved by MT to evaluate: (1) the ability of EIS and machine learning to predict red blood cell (RBC) percentage content of thrombi and (2) to classify the thrombi as “RBC-rich” or “RBC-poor” based on a range of cutoff values of RBC. Methods: ClotbasePilot was a multicentric, international, prospective feasibility study. Retrieved thrombi underwent histological analysis to identify proportions of RBC and other components. EIS results were analyzed with machine learning. Linear regression was used to evaluate the correlation between the histology and EIS. Sensitivity and specificity of the model to classify the thrombus as RBC-rich or RBC-poor were also evaluated. Results: Among 514 MT,179 thrombi were included for EIS and histological analysis. The mean composition in RBC of the thrombi was 36% ± 24. Good correlation between the impedance-based prediction and histology was achieved (slope of 0.9, R² = 0.53, Pearson coefficient = 0.72). Depending on the chosen cutoff, ranging from 20 to 60% of RBC, the calculated sensitivity for classification of thrombi ranged from 77 to 85% and the specificity from 72 to 88%. Conclusion: Combination of EIS and machine learning can reliably predict the RBC composition of retrieved ex vivo AIS thrombi and then classify them into groups according to their RBC composition with good sensitivity and specificity.

Original language	English (US)
Journal	Interventional Neuroradiology
DOIs	https://doi.org/10.1177/15910199231175377
State	Accepted/In press - 2023

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Clinical Neurology
Cardiology and Cardiovascular Medicine

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10.1177/15910199231175377

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Darcourt, J., Brinjikji, W., François, O., Giraud, A., Johnson, C. R., Patil, S., Staessens, S., Kadirvel, R., Mohammaden, M. H., Pisani, L., Rodrigues, G. M., Cancelliere, N. M., Pereira, V. M., Bozsak, F., Doyle, K., De Meyer, S. F., Messina, P., Kallmes, D., Cognard, C., & Nogueira, R. G. (Accepted/In press). Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy. Interventional Neuroradiology. https://doi.org/10.1177/15910199231175377

Darcourt, J, Brinjikji, W, François, O, Giraud, A, Johnson, CR, Patil, S, Staessens, S, Kadirvel, R, Mohammaden, MH, Pisani, L, Rodrigues, GM, Cancelliere, NM, Pereira, VM, Bozsak, F, Doyle, K, De Meyer, SF, Messina, P, Kallmes, D, Cognard, C & Nogueira, RG 2023, 'Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy', Interventional Neuroradiology. https://doi.org/10.1177/15910199231175377

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title = "Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy",

abstract = "Background: Intra-procedural characterization of stroke thromboemboli might guide mechanical thrombectomy (MT) device choice to improve recanalization rates. Electrochemical impedance spectroscopy (EIS) has been used to characterize various biological tissues in real time but has not been used in thrombus. Objective: To perform a feasibility study of EIS analysis of thrombi retrieved by MT to evaluate: (1) the ability of EIS and machine learning to predict red blood cell (RBC) percentage content of thrombi and (2) to classify the thrombi as “RBC-rich” or “RBC-poor” based on a range of cutoff values of RBC. Methods: ClotbasePilot was a multicentric, international, prospective feasibility study. Retrieved thrombi underwent histological analysis to identify proportions of RBC and other components. EIS results were analyzed with machine learning. Linear regression was used to evaluate the correlation between the histology and EIS. Sensitivity and specificity of the model to classify the thrombus as RBC-rich or RBC-poor were also evaluated. Results: Among 514 MT,179 thrombi were included for EIS and histological analysis. The mean composition in RBC of the thrombi was 36% ± 24. Good correlation between the impedance-based prediction and histology was achieved (slope of 0.9, R2 = 0.53, Pearson coefficient = 0.72). Depending on the chosen cutoff, ranging from 20 to 60% of RBC, the calculated sensitivity for classification of thrombi ranged from 77 to 85% and the specificity from 72 to 88%. Conclusion: Combination of EIS and machine learning can reliably predict the RBC composition of retrieved ex vivo AIS thrombi and then classify them into groups according to their RBC composition with good sensitivity and specificity.",

author = "Jean Darcourt and Waleed Brinjikji and Olivier Fran{\c c}ois and Alice Giraud and Johnson, {Collin R.} and Smita Patil and Senna Staessens and Ramanathan Kadirvel and Mohammaden, {Mahmoud H.} and Leonardo Pisani and Rodrigues, {Gabriel Martins} and Cancelliere, {Nicole M.} and Pereira, {Vitor Mendes} and Franz Bozsak and Karen Doyle and {De Meyer}, {Simon F.} and Pierluca Messina and David Kallmes and Christophe Cognard and Nogueira, {Raul G.}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2023.",

year = "2023",

doi = "10.1177/15910199231175377",

language = "English (US)",

journal = "Interventional Neuroradiology",

issn = "1591-0199",

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TY - JOUR

T1 - Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy

AU - Darcourt, Jean

AU - Brinjikji, Waleed

AU - François, Olivier

AU - Giraud, Alice

AU - Johnson, Collin R.

AU - Patil, Smita

AU - Staessens, Senna

AU - Kadirvel, Ramanathan

AU - Mohammaden, Mahmoud H.

AU - Pisani, Leonardo

AU - Rodrigues, Gabriel Martins

AU - Cancelliere, Nicole M.

AU - Pereira, Vitor Mendes

AU - Bozsak, Franz

AU - Doyle, Karen

AU - De Meyer, Simon F.

AU - Messina, Pierluca

AU - Kallmes, David

AU - Cognard, Christophe

AU - Nogueira, Raul G.

PY - 2023

Y1 - 2023

N2 - Background: Intra-procedural characterization of stroke thromboemboli might guide mechanical thrombectomy (MT) device choice to improve recanalization rates. Electrochemical impedance spectroscopy (EIS) has been used to characterize various biological tissues in real time but has not been used in thrombus. Objective: To perform a feasibility study of EIS analysis of thrombi retrieved by MT to evaluate: (1) the ability of EIS and machine learning to predict red blood cell (RBC) percentage content of thrombi and (2) to classify the thrombi as “RBC-rich” or “RBC-poor” based on a range of cutoff values of RBC. Methods: ClotbasePilot was a multicentric, international, prospective feasibility study. Retrieved thrombi underwent histological analysis to identify proportions of RBC and other components. EIS results were analyzed with machine learning. Linear regression was used to evaluate the correlation between the histology and EIS. Sensitivity and specificity of the model to classify the thrombus as RBC-rich or RBC-poor were also evaluated. Results: Among 514 MT,179 thrombi were included for EIS and histological analysis. The mean composition in RBC of the thrombi was 36% ± 24. Good correlation between the impedance-based prediction and histology was achieved (slope of 0.9, R2 = 0.53, Pearson coefficient = 0.72). Depending on the chosen cutoff, ranging from 20 to 60% of RBC, the calculated sensitivity for classification of thrombi ranged from 77 to 85% and the specificity from 72 to 88%. Conclusion: Combination of EIS and machine learning can reliably predict the RBC composition of retrieved ex vivo AIS thrombi and then classify them into groups according to their RBC composition with good sensitivity and specificity.

AB - Background: Intra-procedural characterization of stroke thromboemboli might guide mechanical thrombectomy (MT) device choice to improve recanalization rates. Electrochemical impedance spectroscopy (EIS) has been used to characterize various biological tissues in real time but has not been used in thrombus. Objective: To perform a feasibility study of EIS analysis of thrombi retrieved by MT to evaluate: (1) the ability of EIS and machine learning to predict red blood cell (RBC) percentage content of thrombi and (2) to classify the thrombi as “RBC-rich” or “RBC-poor” based on a range of cutoff values of RBC. Methods: ClotbasePilot was a multicentric, international, prospective feasibility study. Retrieved thrombi underwent histological analysis to identify proportions of RBC and other components. EIS results were analyzed with machine learning. Linear regression was used to evaluate the correlation between the histology and EIS. Sensitivity and specificity of the model to classify the thrombus as RBC-rich or RBC-poor were also evaluated. Results: Among 514 MT,179 thrombi were included for EIS and histological analysis. The mean composition in RBC of the thrombi was 36% ± 24. Good correlation between the impedance-based prediction and histology was achieved (slope of 0.9, R2 = 0.53, Pearson coefficient = 0.72). Depending on the chosen cutoff, ranging from 20 to 60% of RBC, the calculated sensitivity for classification of thrombi ranged from 77 to 85% and the specificity from 72 to 88%. Conclusion: Combination of EIS and machine learning can reliably predict the RBC composition of retrieved ex vivo AIS thrombi and then classify them into groups according to their RBC composition with good sensitivity and specificity.

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DO - 10.1177/15910199231175377

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SN - 1591-0199

JO - Interventional Neuroradiology

JF - Interventional Neuroradiology

ER -

Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy

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