Multiscale CT-Based Computational Modeling of Alveolar Gas Exchange during Artificial Lung Ventilation, Cluster (Biot) and Periodic (Cheyne-Stokes) Breathings and Bronchial Asthma Attack

et al. 2023

Preprint

A variety of pulmonary insults can result in the necessity for mechanical ventilation, which, when misused, used for prolonged periods of time, or associated with an excessive inflammatory response, can result in ventilator-induced lung injury. Older patients have been observed to have an increased risk for respiratory distress with ventilation and more recent studies suggest that this could be linked to disparities in the inflammatory response. To address this, we ventilated young (2-3 months) and old (20-25 months) mice for 2 hours using high pressure mechanical ventilation and extracted data for inflammatory cell ratios, namely macrophage phenotypes, and lung tissue integrity. A large difference in naive macrophages at baseline, alternatively-activated (M2) macrophages at baseline, and airspace enlargement after ventilation was observed in the old mice. The experimental data was used to fit a mathematical model for the inflammatory response to lung injury. Model variables include inflammatory markers and cells, namely neutrophils and macrophages, epithelial cells at varying states, and repair mediators. Parameter sampling was performed using an iterative sampling method and parameter sets were selected based on their ability to fit either the old or young macrophage phenotype percentages and epithelial variables at zero and two hours. Classification methods were performed to identify influential parameters separating the old and young parameter sets as well as user-defined health states. Parameters involved in repair and damage to epithelial cells and parameters regulating the pro-inflammatory response were shown to be important. Local sensitivity analysis preformed for the different epithelial cell variables produced similar results. A pseudo-intervention was also performed on the parameter sets. The results were most influential for the old parameter sets, specifically those with poorer lung health. These results indicate potential targets for therapeutic interventions prior to and during ventilation, particularly for old subjects.Author summaryA variety of inhaled pathogens and other pulmonary insults prompt the need for mechanical ventilation; a procedure that has become increasingly necessary following the 2019 coronavirus pandemic. A proportion of patients respond poorly to ventilation, some resulting in ventilator-induced lung injury. Observational data has shown increased instance of severe disease in older patients as well as differences in the inflammatory response to injury, although more research is needed to confirm this. We performed high-pressure ventilation on young (2-3 months) and old (20-25 months) mice and observed large disparities in inflammatory cell ratios at baseline and lung tissue integrity after ventilation. The experimental data was then used to fit a mathematical model of the inflammatory response to lung injury. We used a variety of analysis methods to identify important parameters separating the young and old parameter sets and user-defined health states of the resulting simulations. Parameters involved in damage and repair of epithelial cells in the lung as well as parameters controlling the pro-inflammatory response to injury were important in both classifying between old and young sets and determining predicted health after ventilation. These results indicate potential targets for therapeutic interventions prior to and during ventilation.

Section: Mathematical Backgroundmentioning

confidence: 99%

Age-dependent ventilator-induced lung injury: Mathematical modeling, experimental data, and statistical analysis

Hay

Grubb

et al. 2023

Preprint

“…From smoking to COPD and in asthma, the complex interplay from molecular to tissue scales in non-infectious injuries is unclear. Many models for these types of injuries have been developed but primarily focus on biomechanics and general inflammation [54,[192][193][194][195][196][197]. However, there have been some models for non-infectious injury that have explicitly studied the immune response.…”

Section: Models Of Non-infectious Injurymentioning

confidence: 99%

Review of Mathematical Modeling of the Inflammatory Response in Lung Infections and Injuries

Front. Appl. Math. Stat.

Heise

Reynolds

2020

Lung inflammation may occur due to viral and bacterial infections, structural damage, or inhalation of dangerous particles. These injuries may be quickly resolved by the immune system, treated effectively through various interventions, become chronic problems, or lead to death. Mathematical modeling has been used to understand immune system dynamics during a number of pulmonary infections and injuries, identify key mechanisms, and provide important insights into new treatments. In this review, we present long-accepted modeling techniques and novel strategies for simulating various lung injuries to highlight the usefulness of mathematical modeling in addressing these life-threatening conditions. Advances in computational power have allowed for a diverse collection of models ranging from those using only Boolean operatorsto complex hybrid multi-scale models, each specifically designed to address relevant biological questions. To illustrate the findings from these mathematical approaches, we present detailed examples, summarize results, and consider future directions from modeling influenza, pneumonia, COVID-19, tuberculosis, anthrax, and other non-infectious injuries.

“…Many models have examine the immune response to bacterial and viral infections, such as pneumonia (Schirm et al, 2016;Mochan et al, 2014;Smith et al, 2011), tuberculosis (Day et al, 2009;Raman et al, 2010;Segovia-Juarez et al, 2004), and influenza (Manchanda et al, 2014;Anderson et al, 2016;Hancioglu et al, 2007). Additionally, models related to smoking and asthma (Brown et al, 2011;Chernyavsky et al, 2014;Golov et al, 2017;Pothen et al, 2015), mechanical ventilation (Hickling, 1998;Marini et al, 1989;Pidaparti et al, 2013), and general inflammatory stress (Reynolds et al, 2010) have been developed, but these models generally deal with the mechanics of the airways, including airflow, pressure, and gas exchange, and how these mechanics respond to inflammation and particle inhalation without accounting for the various cells types involved in the immune response. Models have also been developed to understand and analyze the molecular mechanisms that govern the phenotype switch that macrophages undergo from pro-inflammatory to anti-inflammatory, as well as other important subcellular pathways (Anderson et al, 2016;Braun et al, 2013;Maiti et al, 2014).…”

Section: Mathematical Backgroundmentioning

confidence: 99%

Mathematical modeling of ventilator-induced lung inflammation

Heise

Valentine

et al. 2020

Preprint

Respiratory infections, such as the novel coronavirus (SARS-COV-2), and other lung injuries infect and damage the pulmonary epithelium. In the most severe cases this leads to acute respiratory distress syndrome (ARDS). Due to respiratory failure associated with ARDS, the clinical intervention is the use of mechanical ventilation. Despite the benefits of mechanical ventilators, pro-longed or misuse of these ventilators may lead to ventilation-induced lung injury (VILI). Damage caused to epithelial cells within the alveoli can lead to various types of complications and increased mortality rates. A key component of the immune response is recruitment of macrophages, immune cells that differentiate into phenotypes with unique proand/or anti-inflammatory roles based on the surrounding environment. An imbalance in pro-and anti-inflammatory responses can have deleterious effects on the individual's health. To gain a greater understanding of the mechanisms of the immune response to VILI and post-ventilation outcomes, we develop a mathematical model of interactions between the immune system and site of damage while accounting for macrophage polarization. Through Latin Hypercube Sampling and available data, we generate a virtual cohort of patients with biologically feasible dynamics. We use a variety of methods to analyze the results, including a random forest decision tree algorithm and parameter sensitivity with eFAST. Analysis shows that parame- * Corresponding author (Angela M. Reynolds ) ters and properties of transients related to epithelial repair and M1 activation and de-activation best predicted outcome. Using this new information, we hypothesize interventions and use these treatment strategies to modulate damage in select virtual patients.