Sahin Cansu, Giraud Alice, Jabrah Duaa, Patil Smita, Messina Pierluca, Bozsak Franz, Darcourt Jean, Sacchetti Federico, Januel Anne-Christine, Bellanger Guillaume, Pagola Jorge, Juega Jesus, Imamura Hirotoshi, Ohta Tsuyoshi, Spelle Laurent, Chalumeau Vanessa, Mircic Uros, Stanarčević Predrag, Vukašinović Ivan, Ribo Marc, Sakai Nobuyuki, Cognard Christophe, Doyle Karen
Department of Physiology, University of Galway, Galway, Ireland.
Centre for Research in Medical Devices (CÚRAM)- Science Foundation Ireland (SFI), University of Galway, Galway, Ireland.
Res Pract Thromb Haemost. 2024 Mar 15;8(3):102373. doi: 10.1016/j.rpth.2024.102373. eCollection 2024 Mar.
Electrochemical impedance spectroscopy can determine characteristics such as cell density, size, and shape. The development of an electrical impedance-based medical device to estimate acute ischemic stroke (AIS) clot characteristics could improve stroke patient outcomes by informing clinical decision making.
To assess how well electrical impedance combined with machine learning identified red blood cell (RBC)-rich composition of AIS clots , which is associated with a successfully modified first-pass effect.
A total of 253 clots from 231 patients who underwent thrombectomy in 5 hospitals in France, Japan, Serbia, and Spain between February 2021 and October 2023 were analyzed in the Clotbase International Registry. Electrical impedance measurements were taken following clot retrieval by thrombectomy, followed by Martius Scarlet Blue staining. The clot components were quantified via Orbit Image Analysis, and RBC percentages were correlated with the RBC estimations made by the electrical impedance machine learning model.
Quantification by Martius Scarlet Blue staining identified RBCs as the major component in clots (RBCs, 37.6%; white blood cells, 5.7%; fibrin, 25.5%; platelets/other, 30.3%; and collagen, 1%). The impedance-based RBC estimation correlated well with the RBC content determined by histology, with a slope of 0.9 and Spearman's correlation of r = 0.7. Clots removed in 1 pass were significantly richer in RBCs and clots with successful recanalization in 1 pass (modified first-pass effect) were richer in RBCs as assessed using histology and impedance signature.
Electrical impedance estimations of RBC content in AIS clots are consistent with histologic findings and may have potential for clinically relevant parameters.
电化学阻抗谱可确定细胞密度、大小和形状等特征。开发一种基于电阻抗的医疗设备来估计急性缺血性中风（AIS）血凝块特征，可通过为临床决策提供信息来改善中风患者的预后。
评估电阻抗与机器学习相结合对识别富含红细胞（RBC）的AIS血凝块成分的效果，这与成功改变的首过效应相关。
在Clotbase国际登记处分析了2021年2月至2023年10月期间在法国、日本、塞尔维亚和西班牙的5家医院接受血栓切除术的231例患者的253个血凝块。在通过血栓切除术取出血凝块后进行电阻抗测量，然后进行马休黄猩红蓝染色。通过轨道图像分析对血凝块成分进行定量，红细胞百分比与电阻抗机器学习模型做出的红细胞估计值相关。
马休黄猩红蓝染色定量显示红细胞是血凝块中的主要成分（红细胞，37.6%；白细胞，5.7%；纤维蛋白，25.5%；血小板/其他，30.3%；胶原蛋白，1%）。基于阻抗的红细胞估计值与组织学确定的红细胞含量相关性良好，斜率为0.9，Spearman相关性r = 0.7。一次取出的血凝块红细胞含量显著更高，并且使用组织学和阻抗特征评估，一次成功再通（改变的首过效应）的血凝块红细胞含量更高。
AIS血凝块中红细胞含量的电阻抗估计与组织学结果一致，可能具有用于临床相关参数的潜力。
