Model-based PEEP optimisation in mechanical ventilation.

Area of Science:

• Critical Care Medicine
• Respiratory Physiology
• Biomedical Engineering

Background:

• Acute Respiratory Distress Syndrome (ARDS) necessitates mechanical ventilation (MV).
• Optimal positive end-expiratory pressure (PEEP) selection is critical but lacks standardized methods.
• Patient-specific PEEP titration is encouraged for improved outcomes.

Purpose of the Study:

• To develop and evaluate a novel method for patient-specific PEEP selection in ALI/ARDS.
• To utilize lung elastance parameters (E lung and E drs) for optimizing PEEP.
• To compare model-based PEEP selection with clinically applied values.

Main Methods:

• A study involving 10 ALI/ARDS patients undergoing mechanical ventilation.
• Integral-based methods were used to calculate patient-specific constant lung elastance (E lung) and time-variant dynamic lung elastance (E drs) at varying PEEP levels.
• Optimal PEEP was identified by analyzing E lung vs. PEEP, E drs-Pressure curves, and E drs Area at minimum elastance and inflection points.

Main Results:

• The model demonstrated high accuracy in estimating E drs (median fitting error 0.9%) and E lung (median fitting error 5.6%).
• Both E lung and E drs exhibited a decrease to a minimum with increasing PEEP, followed by an increase, indicating potential over-inflation.
• The E drs-Pressure curves showed clear inflection points, simplifying PEEP selection. Model-based PEEP was higher than clinically selected PEEP in 70% of cases.

Conclusions:

• Continuous monitoring of patient-specific E lung and E drs provides a physiologically relevant metric for PEEP optimization.
• Minimally invasive PEEP titration using these elastance parameters offers a unique approach to individualized mechanical ventilation.
• This method allows for PEEP optimization with minimal disruption to ongoing MV therapy.