Citations

Background Clot perviousness in large vessel occlusion has been shown to be associated with improved recanalization outcomes with mechanical thrombectomy and intravenous thrombolysis. Objective To evaluate the association between clot perviousness based on thrombus attenuation increase (TAI) on CT, and histologic composition of clots in acute ischemic stroke (AIS). Methods A retrospective review was completed of patients with AIS secondary to large vessel occlusion, non-contrast CT (NCCT) and CT angiography (CTA) images, and histologic analysis of the retrieved clot. TAI was measured by subtracting clot attenuation on NCCT from the attenuation on CTA. Up to 3 regions of interest (ROIs) were evaluated on each clot; the average attenuation was used for analysis if multiple ROIs were assessed. Pervious clots were defined as TAI ≥10 Hounsfield units (HUs); impervious clots had TAI <10 HU. Histopathologic analyses of clots were assessed for relative compositions of red blood cells (RBCs), white blood cells (WBCs), fibrin, and platelets/other. Results 57 patients were included. Pervious clots were more likely to be RBC rich (p=0.04); impervious clots were more likely to be fibrin and WBC rich (p=0.01 for both). Pervious clots also had greater RBC density than impervious clots (49.8% and 33.0%, respectively; p=0.006); fibrin density of pervious clots was lower than that of impervious clots (17.8% and 23.2%, respectively; p=0.02). Conclusion Clot perviousness, assessed on NCCT and CTA imaging, is associated with higher RBC density and lower fibrin density, offering a possible explanation for the higher rates of successful thrombectomy and favorable clinical outcome seen in such patients.

Background and Purpose— Nearly 30% of large vessel occlusion acute ischemic stroke clots are from an unknown source. We assessed histological clot composition in a series of patients with large vessel occlusion and investigated correlations between clot composition and stroke pathogenesis. Methods— As part of the multi-institutional STRIP registry (Stroke Thromboembolism Registry of Imaging and Pathology), consecutive emboli retrieved during mechanical thrombectomy were stained using Martius Scarlett Blue and analyzed using machine learning software. We assessed proportions of red blood cells, fibrin, platelets, and white blood cells. Correlations between clot components and stroke pathogenesis (large artery atherosclerosis, cardioembolism, and stroke of undetermined pathogenesis) were assessed using SPSS22. Results— One hundred five patients were included. The proportion of platelet-rich clots (55.0% versus 21.2%; P =0.005) and percentage of platelet content (22.1±4.2% versus 13.9±14.2%; P =0.03) was significantly higher in the large artery atherosclerosis group compared with the cardioembolic group. The proportion of platelet-rich clots (50.0% versus 21.2%; P =0.024) was also significantly higher in the cryptogenic group compared with cardioembolic cases. Large artery atherosclerosis and cryptogenic cases had a similar proportion of platelet-rich clots (55.0% versus 50.0%; P =0.636). There was no significant difference between stroke pathogenesis and the other major clot components. Conclusions— High platelet content of emboli is associated with a large artery atherosclerosis etiology of large vessel occlusion.

Background Bacteria and bacterial components have been associated with the activation of coagulation factors and initiating the blood clot formation. The aim of this study was to investigate whether bacterial populations are present in clots retrieved from patients that have suffered a large vessel occlusion acute ischemic stroke (AIS). Materials and methods Clot samples were collected from 20 AIS patients who underwent clot retrieval with mechanical thrombectomy. Patient clinical demographic details were noted. Expression of bacterial 16S rDNA was analyzed by standard and real-time polymerase chain reaction (PCR). Gram staining was performed to identify Gram-positive and Gram-negative bacteria. Results Both the real-time and standard PCR demonstrated no expression of 16S rDNA in any of the 20 clots samples from AIS patients. Gram staining results showed no expression of Gram-positive or Gram-negative bacteria present in the clot samples. Conclusion Our current study found no bacteria populations in the clots of AIS patients.

Summary Cardiac surgery has been shown to be associated with increased risk of acute ischemic stroke. This report presents a case of a successful mechanical embolectomy procedure to treat a patient for an acute ischemic stroke, which was caused by the cardiac embolization of a ‘foreign body’ containing debris following a redo sternotomy procedure for mitral valve replacement and tricuspid valve annuloplasty.

Introduction: The composition of acute ischemic stroke (AIS) blood clots can impact their successful removal via endovascular therapy. Identification of the clot composition prior to endovascular treatment may influence the optimal treatment strategy. In this study, we aimed to investigate the relationship between the Hounsfield Unit intensity on CT and machine-learned characterization of clot composition. Methods: Prior to endovascular treatment, each patient had a non-contrast CT performed. Histopathological analysis of the clots was performed and clot composition was quantified via standard thresholding techniques using Adobe Photoshop. In addition, following representative cell labeling by an expert pathologist, image segmentation algorithms (Orbital Image Analysis, Actelion Ltd.) were trained on each case to quantify composition by cell type. The clots were categorized into 3 groups: red blood cell (RBC) dominant (>50%), RBC proportion equal to fibrin and fibrin dominant (>50%). Correlations between clot composition and Hounsfield Units (HU) density on CT were assessed. A positive Hyperdense Artery Sign was defined as ≥50 HU. Results: Twenty patients were included in the study. There was a significant correlation between clots quantified using Adobe Photoshop Image-Pro and Orbit image analysis ( ρ = 0.837, p < 0.001). A significant relationship was found between clot composition and the presence of a HAS sign, ( X 2 (2) = 6.496, p = 0.039). There is a significant positive correlation between percentage of RBC in the clot and the presence of a HAS sign ( ρ = 0.632, p = 0.002**). There is also a significant positive correlation between the presence of fibrin-rich and the absence of a HAS sign ( ρ = 0.600, p = 0.004**). Conclusion: Machine learning software can accurately quantify AIS clot composition. The Hounsfield unit density of an AIS clot on a non-contrast CT correlates with the composition of the clot. Measuring the HU of the clot on CT prior to endovascular intervention may inform the clinician of the clot type and help to determine the most appropriate treatment strategy for that clot.