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.
Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure–function relationship in the lung.
more » « less- Award ID(s):
- 1706801
- NSF-PAR ID:
- 10472626
- Publisher / Repository:
- Royal Society Publisher
- Date Published:
- Journal Name:
- Journal of The Royal Society Interface
- Volume:
- 19
- Issue:
- 191
- ISSN:
- 1742-5662
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1098/rsif.2022.0062
