Abstract
Extracorporeal membrane oxygenation (ECMO) is a critical life-support intervention for patients with severe cardiopulmonary failure. However, its use is associated with a substantially increased risk of nosocomial infections, with reported incidence rates ranging from 8.8% to 64.0%. These infections-particularly ventilator-associated pneumonia and bloodstream infections-are linked to heightened morbidity, prolonged intensive care and hospital stays, and elevated mortality. This review aims to systematically compile Chinese and English literature published between 2018 and 2025, clarify the unique pathophysiological mechanisms of ECMO-related infections, analyze the limitations and breakthroughs of existing prediction models, and explore the potential role of machine learning in developing personalized early warning systems. Additionally, it seeks to establish a clinical decision-making framework for precise prevention and control. We conclude that improving ECMO infection control requires establishing standardized, clinically applicable diagnostic criteria, conducting a multicenter prospective validation study, and developing transparent, AI-enhanced predictive tools to enable real-time infection monitoring and improved patient prognosis during ECMO support.