A damaged-informed lung ventilator model for ventilator waveforms
ABSTRACT Motivated by desire to understand pulmonary physiology and pathophysiology, scientists have developed models of pulmonary physiology. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present problems for modeling efforts. Real-world injury is too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting both the clinical utility with existing approaches. To address this gap, we present a damaged-informed lung ventilator (DILV) model to model and quantify patient-ventilator interactions and lung health. This approach relies on systematically mathematizing the pathophysiologic knowledge clinicians use to interpret lung condition from ventilator waveform data. This is achieved by defining clinically relevant features in the ventilator waveform data that contain hypothesis-driven information about pulmonary physiology, patient-ventilator interaction, and ventilator settings. To capture these features, we develop a modelling framework where the model has enough flexibility to reproduce commonly observed variability in waveform data. We infer the model parameters with clinical (human) and laboratory (mouse) data. The DILV model can reproduce essential dynamics of differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of respiratory effort. Estimated parameters correlate with known measures of lung physiology, including lung compliance. This method has the potential to translate laboratory physiology experiments to clinical applications, including pathways for high fidelity estimates of lung state and sources of VILI with an end goal of reducing the impact of VILI and acute respiratory distress syndrome..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 10. Okt. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Agrawal, Deepak K. [VerfasserIn] |
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| doi: |
10.1101/2020.10.23.351320 |
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| PPN (Katalog-ID): |
XBI019193998 |
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| 520 | |a ABSTRACT Motivated by desire to understand pulmonary physiology and pathophysiology, scientists have developed models of pulmonary physiology. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present problems for modeling efforts. Real-world injury is too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting both the clinical utility with existing approaches. To address this gap, we present a damaged-informed lung ventilator (DILV) model to model and quantify patient-ventilator interactions and lung health. This approach relies on systematically mathematizing the pathophysiologic knowledge clinicians use to interpret lung condition from ventilator waveform data. This is achieved by defining clinically relevant features in the ventilator waveform data that contain hypothesis-driven information about pulmonary physiology, patient-ventilator interaction, and ventilator settings. To capture these features, we develop a modelling framework where the model has enough flexibility to reproduce commonly observed variability in waveform data. We infer the model parameters with clinical (human) and laboratory (mouse) data. The DILV model can reproduce essential dynamics of differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of respiratory effort. Estimated parameters correlate with known measures of lung physiology, including lung compliance. This method has the potential to translate laboratory physiology experiments to clinical applications, including pathways for high fidelity estimates of lung state and sources of VILI with an end goal of reducing the impact of VILI and acute respiratory distress syndrome. | ||
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