MEGA: a computational framework to simulate the acute respiratory distress syndrome

Area of Science:

• * Computational physiology
• * Respiratory mechanics
• * Medical imaging analysis

Background:

• * Acute Respiratory Distress Syndrome (ARDS) presents significant challenges in mechanical ventilation (MV) due to patient variability and limited mechanistic understanding.
• * Current ARDS management relies heavily on clinician experience, highlighting a need for tools to predict patient response to therapies like PEEP and prone positioning.
• * Deeper insights into the biomechanical and physiological underpinnings of ARDS patient responses are crucial for advancing MV strategies.

Purpose of the Study:

• * To develop and implement a coupled physiomechanical computational framework for simulating ARDS patient respiratory dynamics.
• * To utilize patient-specific computed tomography (CT) scan data for a spatially resolved model of lung function.
• * To evaluate the framework's potential in simulating responses to therapeutic interventions such as prone positioning and PEEP adjustments.

Main Methods:

• * A coupled physiomechanical computational model was developed using patient-specific CT scan data.
• * The model was used to simulate mechanical ventilation scenarios, including prone positioning and PEEP increment.
• * Simulations focused on calculating global parameters and detailed ventilation distribution within the lungs.

Main Results:

• * Model simulations demonstrated qualitative agreement with existing literature and clinical data for ARDS.
• * Quantitative discrepancies were observed, underscoring the need for rigorous model calibration.
• * The framework successfully simulated recruitment maneuvers, including prone positioning.

Conclusions:

• * The developed computational framework serves as a proof of concept for aiding intensivists in ARDS patient management.
• * This spatially resolved model offers a more detailed understanding of ventilation distribution than conventional single-compartment models.
• * The framework has the potential to support clinical decision-making and inform the development of novel mechanical ventilation strategies for ARDS.