Optimising respiratory support for early COVID-19 pneumonia: a computational modelling study

Area of Science:

• Pulmonary Medicine
• Critical Care
• Biomedical Engineering

Background:

• Optimal respiratory support for early COVID-19 pneumonia remains debated.
• Computational modeling is used to assess the impact of various oxygen therapies on lung injury.

Purpose of the Study:

• To evaluate if conventional oxygen therapy (COT), high-flow nasal oxygen therapy (HFNOT), continuous positive airway pressure (CPAP), and noninvasive ventilation (NIV) exacerbate lung injury in early COVID-19 pneumonia.
• To compare the effects of different respiratory support methods on lung stress and strain.

Main Methods:

• A multi-compartmental cardiopulmonary simulator modeled COT, HFNOT, CPAP, and NIV in 120 spontaneously breathing patients.
• Respiratory effort was progressively reduced, and oxygenation, respiratory effort, and lung stress/strain were quantified.
• Lung-protective mechanical ventilation was also simulated.

Main Results:

• HFNOT, CPAP, and NIV improved oxygenation but initially increased lung stress and strain.
• CPAP improved oxygenation and reduced respiratory effort, but lung stress/strain remained high.
• HFNOT achieved better oxygenation with reduced lung stress and no increased strain.
• NIV at lower PEEP reduced stress but high strain persisted; lung-protective mechanical ventilation minimized injury.

Conclusions:

• Noninvasive ventilation may worsen pulmonary injury in COVID-19 pneumonia by failing to reduce respiratory effort.
• HFNOT offers comparable oxygenation to CPAP/NIV with reduced lung strain.
• Invasive mechanical ventilation may be less injurious than noninvasive support for patients with significant respiratory effort.