Clinical applications

Pulmonary hypertension is a critical component of a wide range of cardiorespiratory disorders. We are developing a new understanding of factors contributing to pulmonary hypertension in pulmonary embolism (obstruction of the pulmonary vessels). We have constructed integrated functional models of perfusion, ventilation and gas exchange in subject-specific models of patients with pulmonary embolism, and found strong correlations with model predictions and patient outcome.

Lung cancer is a leading cause of death worldwide. Radiation therapy (RT) is one method to treat this disease, however a common side effect of this is radiation-induced lung damage (RILD) which causes a loss in lung function. It is difficult to predict individual patient response to RT due to large variability in individual response to RT. In this project, we are developing computational models to improve the prediction of patient lung function post-RT treatment to improve clinical decision-making and treatment planning.

In this project we aim to develop a quantitative computer-based tool that takes into account structural, functional as well as CT imaging-based parameters of lungs to improve prognosis in Idiopathic Pulmonary Fibrosis. This tool will predict the mortality in patients more accurately by considering a comprehensive library of biomarkers such as the pulmonary vessel volume, and parenchymal patterns.

Pulmonary rehabilitation (PR) is a structured program containing exercise training, education, and behavior change and is one of the most effective interventions for patients with chronic respiratory diseases. Despite this, adherence to these critical PR sessions is low. We are working with collaborators in the Department of Population Health to create a mobile PR (mPR) programme to improve participation and increase self-motivation. Our contribution to this work in a model-based lung app [add link to our model app] is included in the mPR program to improve health literacy and educate patients using realistic visualisations about how our lungs work and what changes occur during disease. We also created a movie, using our models, to explain how our lungs work and what changes may be occurring with disease.

Several changes in structure and function are associated with the ‘older’ lung. In the human respiratory system it is difficult to distinguish between the changes that occur due to normal ageing and those associated with disease. We are using a combination of experimental, imaging, and modelling approaches to understand changes in lung function with health ageing.

Prone position (breathing when lying on one’s stomach) has been clinically shown to improve oxygenration and overall gas exchange in mechanically ventilated patients. This has been shown to be effective in various patients including ARDS (Acute respiratory distress) and COVID-19 (a disease caused by the SARS-CoV-2 virus) patients. We employ state of the art models to understand the underlying physiology of the lungs when a patient is ventilated in prone posture versus supine (breathing when lying down on spine).

Electronic cigarettes or vapes have not been around long enough to understand the impact they may have on long-term health. We are developing a research programme to study these health effects including measuring the chemicals in e-cigarette aerosol, looking at lung cells after exposure to e-cigarette aerosol, using magnetic resonance imaging (MRI) to measure airflow in vapers lungs, and pulling all this together using our computational models.

We have contributed to the development of a predictive computational model of the lung that integrates experimental data relevant to airway hyperresponsiveness from all relevant length (molecular to whole organ levels) and time scales. This includes models of airway smooth muscle (ASM) dynamics, and the balance of ASM force development with force from the surrounding tissue and whole organ levels. This will allow us to identify variable material properties in normal and asthmatic lungs that correlate to experimental data on pulmonary dynamics.