Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19

Area of Science:

• Computational modeling
• Pulmonary biomechanics
• Medical physics

Background:

• COVID-19 causes heterogeneous lung damage affecting airflow and pressure.
• In silico models are crucial for understanding complex lung injury mechanics.
• Subject-specific modeling is needed to capture individual variations in disease progression.

Purpose of the Study:

• To develop a computational workflow coupling airflow and tissue mechanics for COVID-19 lung injury.
• To create a virtual platform for hypothesis testing of lung injury mechanics.
• To simulate regional lung dynamics in response to subject-specific COVID-19 damage.

Main Methods:

• Developed a CT-based, multi-scale, physics-based in silico modeling approach.
• Coupled airflow and tissue mechanics simulations.
• Investigated effects of varying inflammation and damage levels in a meso-scale acinar model.

Main Results:

• Simulations showed ventilation redistribution from damaged to healthier lung regions (e.g., right lower lobe to right middle lobe).
• Increased tissue injury and surfactant loss led to a simulated decrease in overall tidal volume.
• Model demonstrated reasonable simulation of global lung dynamics changes due to regional damage.

Conclusions:

• This study represents a significant advancement in multi-scale computational modeling for COVID-19 lung injury.
• The developed workflow can simulate the impact of heterogeneous, subject-specific lung damage on ventilation.
• The findings support the potential of in silico platforms for studying lung injury biomechanics.