ECMO-associated nosocomial infections in adults: immunopathogenesis and predictive modeling approaches

TL;DR

This review explores how ECMO increases infection risk and suggests using machine learning to improve infection prediction and control in patients.

Contribution

The paper proposes integrating machine learning and standardized criteria to enhance ECMO infection monitoring and prevention.

Findings

ECMO is associated with a high incidence of nosocomial infections, ranging from 8.8% to 64.0%.

Machine learning shows potential for developing personalized early warning systems for ECMO-related infections.

Standardized diagnostic criteria and multicenter studies are needed to improve infection control during ECMO.

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.

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