Pisani, Leonardo

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, R 2 = 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.

Background Several animal studies have demonstrated that mechanical thrombectomy (MT) for acute ischemic stroke (AIS) may cause vessel wall injury (VWI). However, the histological changes in human cerebral arteries following MT are difficult to determine. Objective To investigate the occurrence of VWI during MT by histological and immunohistochemical evaluation of AIS clots. Methods As part of the multicenter STRIP registry, 277 clots from 237 patients were analyzed using Martius Scarlett Blue stain and immunohistochemistry for CD34 (endothelial cells) and smooth muscle actin (smooth muscle cells). Results MT devices used were aspiration catheters (100 cases), stentriever (101 cases), and both (36 cases). VWI was found in 33/277 clots (12%). There was no significant correlation between VWI and MT device. The degree of damage varied from grade I (mild intimal damage, 24 clots), to grade II (relevant intimal and subintimal damage, 3 clots), and III (severe injury, 6 clots). VWI clots contained significantly more erythrocytes (p=0.006*) and less platelets/other (p=0.005*) than non-VWI clots suggesting soft thrombus material. Thrombolysis correlated with a lower rate of VWI (p=0.04*). VWI cases showed a significantly higher number of passes (2 [1–4] vs 1 [1–3], p=0.028*) and poorer recanalization outcome (p=0.01*) than cases without VWI. Conclusions Histological markers of VWI were present in 12% of AIS thrombi, suggesting that VWI might be related to MT. VWI was associated with soft thrombus consistency, higher number of passes and poorer revascularization outcome. There was no significant correlation between VWI and MT device.