  The acute respiratory distress syndrome (ARDS) is characterized by the acute
development of diffuse alveolar damage (DAD) resulting in increased vascular
permeability and decreased alveolar gas exchange. Mechanical ventilation is a
potentially lifesaving intervention to improve oxygen exchange but has the
potential to cause ventilator-induced lung injury (VILI). A general strategy to
reduce VILI is to use low tidal volume and low-pressure ventilation, but
optimal ventilator settings for an individual patient are difficult for the
bedside physician to determine and mortality from ARDS remains unacceptably
high. Motivated by the need to minimize VILI, scientists have developed models
of varying complexity to understand diseased pulmonary physiology. However,
simple models often fail to capture real-world injury while complex models tend
to not be estimable with clinical data, limiting the clinical utility of
existing models. To address this gap, we present a physiologically anchored
data-driven model to better model lung injury. Our approach relies on using
clinically relevant features in the ventilator waveform data that contain
information about pulmonary physiology, patients-ventilator interaction and
ventilator settings. Our lung model can reproduce essential physiology and
pathophysiology dynamics of differently damaged lungs for both controlled mouse
model data and uncontrolled human ICU data. The estimated parameters values
that are correlated with a known measure of lung physiology agree with the
observed lung damage. In future endeavors, this model could be used to
phenotype ventilator waveforms and serve as a basis for predicting the course
of ARDS and improving patient care.
