Impedance-based sensors discriminate among different types of blood thrombi with very high specificity and sensitivity

Abstract

BackgroundIntracranial occlusion recanalization fails in 20% of endovascular thrombectomy procedures, and thrombus composition is likely to be an important factor. In this study, we demonstrate that the combination of electrical impedance spectroscopy (EIS) and machine learning constitutes a novel and highly accurate method for the identification of different human thrombus types.Methods134 samples, subdivided into four categories, were analyzed by EIS: 29 ‘White’, 26 ‘Mixed’, 12 ‘Red’ thrombi, and 67 liquid ‘Blood’ samples. Thrombi were generated in vitro using citrated human blood from five healthy volunteers. Histological analysis was performed to validate the thrombus categorization based on red blood cell content. A machine learning prediction model was trained on impedance data to differentiate blood samples from any type of thrombus and in between the four sample categories.ResultsHistological analysis confirmed the similarity between the composition of in vitro generated thrombi and retrieved human thrombi. The prediction model yielded a sensitivity/specificity of 90%/99% for distinguishing blood samples from thrombi and a global accuracy of 88% for differentiating among the four sample categories.ConclusionsCombining EIS measurements with machine learning provides a highly effective approach for discriminating among different thrombus types and liquid blood. These findings raise the possibility of developing a probe-like device (eg, a neurovascular guidewire) integrating an impedance-based sensor. This sensor, placed in the distal part of the smart device, would allow the characterization of the probed thrombus on contact. The information could help physicians identify optimal thrombectomy strategies to improve outcomes for stroke patients.

First Experience with the Nimbus Stentretriever

“…Nimbus may be a suitable first-line device for fibrin-rich clots, however, it is not designed to retrieve red blood cell rich clots. Although progress has been made in the prediction of clot composition, either by imaging with the use of machine learning algorithms [ 10 ] or endovascularly by the use of electrical impedance spectroscopy [ 11 ], no reliable method to predict thrombus composition has been introduced into clinical practice. Another aspect is that Nimbus is more expensive when compared to most conventional stentretrievers.…”

Can micro-guidewire advancement forces predict clot consistency and location to assist the first-line technique for mechanical thrombectomy?

“…9 In an experimental animal study, Skyrman et al 10 evaluated the use of diffuse reflectance spectroscopy applied to a custom guidewire to differentiate clot composition, which allowed the identification of red cell-rich soft clots, fibrin-rich stiff clots, and mixed clots. Messina et al 11 conducted an experimental study conceived to evaluate a guidewire device that used an impedance-based microsensor placed at its tip, coupled with a machine learning algorithm, to identify different types of clot composition by contact. However, contrary to our approach, none of these technologies provide information on the mechanical properties of the clot or have been currently implemented in clinical practice.…”

Granulocytes-Rich Thrombi in Cerebral Large Vessel Occlusion Are Associated with Increased Stiffness and Poorer Revascularization Outcomes

“…There are new sensors designed to discriminate different types of thrombus during EVT, but no device has been demonstrated yet to improve the decision-making process during EVT of acute stroke treatment [ 33 ]. Future works might focus on the development of a simplified FC analysis that might analyze part of the intracranial clot during MT by a point-of-care test might be worthy as a guide for patient-specific treatment strategy.…”