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Updated: Sep 17, 2026

Point-of-Care Ultrasound for Peripheral Veno-Arterial Extracorporeal Membrane Oxygenation Without Left Ventricular Venting
Published on: January 17, 2025
Challenges and opportunities in ECMO monitoring: A mixed-methods appraisal
Wei Yin1, Alisa Permessur2, George Pittas1
1Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
Abstract:
Extracorporeal membrane oxygenation (ECMO) provides heart/lung support for critically ill patients. While advances in research have improved the overall design and performance of ECMO circuits, complications such as hemolysis and thrombosis persist, and the dysfunction of the ECMO machine itself can pose significant risk to patients. Many parameters need to be closely monitored during an ECMO run, and it is often labor and resource-intensive. Currently, there are few commercially available automated ECMO circuit monitoring systems that can provide continuous, holistic monitoring for ECMO circuit health and provide real-time clinical recommendations. This mixed-methods review (through literature review and customer discovery interviews) discussed key parameters relevant to ECMO circuits and monitoring, including bleeding, thrombosis, hemolysis, flow and pressure conditions, and gas exchange. Their importance in ECMO monitoring, current practices, and available technologies were highlighted. In addition, a customer discovery study, supported by the New York regional I-Corps program, was conducted to identify the "pain points" in ECMO circuit monitoring through interviews with healthcare professionals and other stakeholders experienced in ECMO care/use. Findings from both the literature review and the customer discovery study indicated that ECMO monitoring and management require significant resources, due to the variability and complexity of patients' conditions, challenges in collecting patient and ECMO circuit data, and the need for a comprehensive understanding of the entire patient-ECMO ecosystem. Improvements in understanding complex ECMO data, incorporating new sensors into the monitoring system, and establishing comprehensive ECMO data pipelines represent key steps towards developing "smart" ECMO monitoring that can serve as a clinical decision support system.