An approach to study recruitment/derecruitment dynamics in a patient‐specific computational model of an injured human lung

Abstract

We present a new approach for physics‐based computational modeling of diseased human lungs. Our main object is the development of a model that takes the novel step of incorporating the dynamics of airway recruitment/derecruitment into an anatomically accurate, spatially resolved model of respiratory system mechanics, and the relation of these dynamics to airway dimensions and the biophysical properties of the lining fluid. The importance of our approach is that it potentially allows for more accurate predictions of where mechanical stress foci arise in the lungs, since it is at these locations that injury is thought to arise and propagate from. We match the model to data from a patient with acute respiratory distress syndrome (ARDS) to demonstrate the potential of the model for revealing the underlying derangements in ARDS in a patient‐specific manner. To achieve this, the specific geometry of the lung and its heterogeneous pattern of injury are extracted from medical CT images. The mechanical behavior of the model is tailored to the patient's respiratory mechanics using measured ventilation data. In retrospective simulations of various clinically performed, pressure‐driven ventilation profiles, the model adequately reproduces clinical quantities measured in the patient such as tidal volume and change in pleural pressure. The model also exhibits physiologically reasonable lung recruitment dynamics and has the spatial resolution to allow the study of local mechanical quantities such as alveolar strains. This modeling approach advances our ability to perform patient‐specific studies in silico, opening the way to personalized therapies that will optimize patient outcomes.

Solving Bayesian inverse problems with expensive likelihoods using constrained Gaussian processes and active learning

“…equations that describe the 3D fully resolved air flow in conducting airways and their viscoelastic wall mechanics can be integrated along the coordinates of the idealized cylindrical airways. This leads to the following simplified equations for each airway element of the model [54,55,57]:…”

“…κ and η determine the stiffness and curvature of this pressure-volume relation, respectively. For further details on the lung model, the reader is referred to [54,55,57]. We simulate the mechanical ventilation of an endotracheally intubated patient and therefore prescribe the inlet pressure at the proximal end of the tube, connected to the ventilator.…”

A Parametric 3D Model of Human Airways for Particle Drug Delivery and Deposition

“…In the last decade, different authors have exploited numerical tools to model the human airways [18][19][20][21][22][23]. Several studies explored the capabilities of CFD models as a tool to investigate lung injuries [24][25][26] as well as to optimize drug deposition in human airways [22,23,[27][28][29][30]. However, the numerical analysis of drug delivery in the respiratory system involves not only the challenges of particle deposition modeling but also considerations of the inherent complexity and time-consuming nature of CFD studies [31,32].…”