Smith, Abigail; (2025) The effects of gravity and patient positioning on lung mechanics: investigation via poroelastic modelling. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

This thesis explores the impact of body positioning on pulmonary mechanics in critically ill patients, particularly those requiring mechanical ventilation for conditions like ARDS. Changing a patient’s posture, such as moving them from supine to prone, can enhance lung function by improving ventilation and perfusion distribution and reducing the risk of Ventilator-Induced Lung Injury (VILI). The primary goal of this research is to identify key parameters influenced by body positioning and to develop a mathematical model that simulates lung behaviour under different conditions. Central to this thesis is the understanding of how gravity influences lung tissue deformation, a phenomenon directly affecting airflow and blood flow within the lungs. Through detailed analyses, we identify that alveolar displacement is more pronounced when a patient is upright, and less pronounced in the prone, compared to the supine. We demonstrate that lung deformation due to gravity is a critical factor in the observed differences in pulmonary function between positions. After an in-depth review of the current and historic literature, we begin by modelling the lung as a static object using solid mechanics, to determine the deformation of lung tissue when fully deflated. This model will then inform the governing equations of a poroelastic model, coupling the solid tissue with the intricate airflow through the system. We will look at this from its simplest linear approximation, increasing complexity until a fully non-linear physiologically accurate model is realised. Finishing this thesis, we will connect the non-linear poroelastic model to the branching upper airways and blood column, in order to assess the spatial ventilation and perfusion mismatch of the lung in different orientations. This approach validates the concept that mathematical models can accurately capture multi-variate lung behaviour, with demonstrated predictive fidelity, and can be used for in-silico experimentation to provide clinical inference and aid decision making.

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