Optimising mechanical ventilation through model-based methods and automation.

Area of Science:

• Biomedical Engineering
• Intensive Care Medicine
• Control Systems Theory

Background:

• Mechanical ventilation (MV) is critical for respiratory failure and ARDS patients, but high costs and clinician workload are significant challenges.
• Patient variability and over-sedation contribute to prolonged MV duration and increased ICU expenses.
• Reducing MV duration and clinical workload is essential for cost-effective critical care.

Purpose of the Study:

• To explore the application of model-based dynamic systems and control methods for automating mechanical ventilation.
• To present common lung models and a vision for automated MV care.
• To investigate the predictive capacity of current models for improving patient outcomes and reducing costs.

Main Methods:

• Review of common lung models and dynamic systems modeling techniques.
• Exploration of model-based methods for real-time patient monitoring and outcome prediction.
• Discussion of system identification for personalized oxygenation and MV therapy control.

Main Results:

• Model-based dynamic systems offer potential for real-time patient insights and outcome prediction.
• Virtual patients can be developed for in-silico testing of clinical protocols.
• Personalized control of oxygenation and MV therapy can be guided by dynamic system models.

Conclusions:

• Automating MV care through model-based dynamic systems can enhance safety, optimize patient care, and reduce ICU costs.
• Dynamic systems and control methods represent a transformative approach to personalized and predictive medicine in ICUs.
• This review highlights the state-of-the-art applications of these methods in critical care ventilation.