ArticleInterventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences2025
Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy.
Article in Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 7 citations in OpenAlex.
- Electrical impedance spectroscopy signatures of red blood cells and platelets in acute ischemic stroke clots in the Clotbase International Registry: Significance for etiology and first pass effect.Journal of neurointerventional surgery · 2026Article
- Potential of Guidewire-Clot Interaction Forces for Clot Detection in Robotic Mechanical Thrombectomy.The international journal of medical robotics + computer assisted surgery : MRCAS · 2025Article
- The use of the NeVa stent-retriever for bail-out mechanical thrombectomy.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Can micro-guidewire advancement forces predict clot consistency and location to assist the first-line technique for mechanical thrombectomy?Journal of neurointerventional surgery · 2025Article
- Electrical impedance measurements can identify red blood cell-rich content in acute ischemic stroke clotsResearch and practice in thrombosis and haemostasis · 2024Article
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Authors and funding
20 authors at 9 institutions in 5 countries.
Funding
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Abstract
BackgroundIntra-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.ObjectiveTo 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.MethodsClotbasePilot 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.ResultsAmong 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,
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