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Physics-based in silico modelling of microvascular pulmonary perfusion in COVID-19
Due to its ability to induce heterogenous, patient-specific damage in pulmonary alveoli and capillaries, COVID-19 poses challenges in defining a uniform profile to elucidate infection across all patients. Computational models that integrate changes in ventilation and perfusion with heterogeneous dam...
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Published in: | Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Journal of engineering in medicine, 2024-05, Vol.238 (5), p.562-574 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Due to its ability to induce heterogenous, patient-specific damage in pulmonary alveoli and capillaries, COVID-19 poses challenges in defining a uniform profile to elucidate infection across all patients. Computational models that integrate changes in ventilation and perfusion with heterogeneous damage profiles offer valuable insights into the impact of COVID-19 on pulmonary health. This study aims to develop an in silico hypothesis-testing platform specifically focused on studying microvascular pulmonary perfusion in COVID-19-infected lungs. Through this platform, we explore the effects of various acinar-level pulmonary perfusion abnormalities on global lung function. Our modelling approach simulates changes in pulmonary perfusion and the resulting mismatch of ventilation and perfusion in COVID-19-afflicted lungs. Using this coupled modelling platform, we conducted multiple simulations to assess different scenarios of perfusion abnormalities in COVID-19-infected lungs. The simulation results showed an overall decrease in ventilation-perfusion (V/Q) ratio with inclusion of various types of perfusion abnormalities such as hypoperfusion with and without microangiopathy. This model serves as a foundation for comprehending and comparing the spectrum of findings associated with COVID-19 in the lung, paving the way for patient-specific modelling of microscale lung damage in emerging pulmonary pathologies like COVID-19.
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ISSN: | 0954-4119 2041-3033 |
DOI: | 10.1177/09544119241241550 |