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

TL;DR

This paper introduces a novel, patient-specific computational lung model that incorporates airway recruitment and de-recruitment dynamics, enabling better prediction of injury sites and personalized treatment planning for conditions like ARDS.

Contribution

The study develops a spatially resolved, physics-based lung model that uniquely includes airway recruitment dynamics and is tailored to individual patient data from CT images and ventilation measurements.

Findings

Model accurately reproduces clinical ventilation data.

Captures physiologically realistic recruitment and de-recruitment dynamics.

Provides spatially detailed insights into lung mechanics and injury risk.

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/de-recruitment 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.