Abstract:

Patient-ventilatory asynchrony (PVA) is common during mechanical ventilation, and is closely associated with elevated work of breath, prolonged mechanical ventilation, ventilator-induced lung injury,as well as worse clinical outcomes.Identifying PVA requires careful observation of the patient and their ventilator waveforms, but clinical healthcare providers vary in their ability to recognize PVA, and continuous bedside monitoring is challenging, urging the development of automated monitoring methods.PVA automatic detection algorithms have rapidly developed in recent years, showing a trend of synergistic development driven by data and knowledge.This article reviews the development history of PVA automatic detection methods, outlines the advantages and disadvantages of technologies based on rules, traditional machine learning, deep learning, and physiological system models, introduces the development and clinical application status of real-time PVA detection and analysis systems, and discusses the challenges faced in PVA detection based on mechanical ventilation waveform big data, such as the lack of standard datasets and insufficient algorithm generalization capability.

Key words: Mechanical ventilation, Patient-ventilator asynchrony, Machine learning, Deep learning, Physiological system modeling