Abstract：
Extracorporeal membrane oxygenation (ECMO) is a life-support technique used to treat cardiac and pulmonary failure, including severe cases of COVID-19 (coronavirus disease 2019) involving acute respiratory distress syndrome. Blood clot formation in the circuit is one of the most common complications in ECMO, having potentially harmful and even fatal consequences. It is therefore essential to regularly monitor for clots within the circuit and take appropriate measures to prevent or treat them. A review of the various methods used by hospital units for detecting blood clots is presented. The benefits and limitations of each method are discussed, specifically concerning detecting blood clots in the oxygenator, as it is concluded that this is the most critical and challenging ECMO component to assess. We investigate the feasibility of solutions proposed in the surrounding literature and explore two areas that hold promise for future research: the analysis of small-scale pressure fluctuations in the circuit, and real-time imaging of the oxygenator. It is concluded that the current methods of detecting blood clots cannot reliably predict clot volume, and their inability to predict clot location puts patients at risk of thromboembolism. It is posited that a more in-depth analysis of pressure readings using machine learning could better provide this information, and that purpose-built imaging could allow for accurate, real-time clotting analysis in ECMO components.
摘要：
体外膜氧合（ECMO）是一种用于治疗心脏和肺衰竭的生命维持技术，包括涉及急性呼吸窘迫综合征的COVID-19（2019年冠状病毒病）的严重病例。回路中的血凝块形成是ECMO中最常见的并发症之一，具有潜在的有害甚至致命的后果。因此,有必要定期监测电路内的凝块并采取适当的措施来预防或治疗它们。介绍了医院单位用于检测血凝块的各种方法。讨论了每种方法的优点和局限性，特别是关于检测氧合器中的血凝块，结论是这是最关键和最具挑战性的ECMO组件。我们研究了周围文献中提出的解决方案的可行性，并探索了两个对未来研究充满希望的领域：分析回路中的小规模压力波动，和氧合器的实时成像。结论是，当前检测血凝块的方法不能可靠地预测血凝块体积，而且他们无法预测血块位置会使患者面临血栓栓塞的风险。有人认为，使用机器学习对压力读数进行更深入的分析可以更好地提供这些信息。专门构建的成像可以允许准确的，ECMO组件中的实时凝血分析。
