Investigating Non-Conventional Modes of Mechanical Ventilation for Acute Respiratory Disease using Computational Simulation

Acute respiratory distress syndrome (ARDS), characterised by rapid lung inflammation, is a global health challenge affecting over 3 million people annually, with a mortality rate of 35-50%. Causes include sepsis, pneumonia, and COVID-19, with the disease involving alveolar injury, surfactant dysfunction, immune activation, and coagulation issues. Despite the severity, effective pharmacological treatments are limited, and ARDS management primarily relies on mechanical ventilation in ICUs. This approach, though life-saving, is resource-intensive and linked to high morbidity and reduced quality of life among survivors.
Mechanical ventilation preserves oxygenation while allowing lung recovery. Standard methods, like volume or pressure controlled ventilation, are commonly used but carry risks of ventilator-induced lung injury (VILI) due to excessive volumes or pressures. Alternative modes, like Airway Pressure Release Ventilation (APRV), High-Frequency Oscillatory Ventilation (HFOV), ventilation personalised to lung morphology, and lung-diaphragm protective ventilation have been proposed as "lung protective" strategies. However, clinical efficacy data are limited due to the challenges of large-scale trials in critically ill patients. APRV, delivering continuous positive airway pressure with time-cycled releases, shows potential in minimising lung injury and improving gas exchange but is not the first-line choice due to variability in practice and limited data on VILI. HFOV, delivering small tidal volumes at high frequencies, is widely used in neonatal care but remains controversial in adults. Lung-diaphragm protective ventilation balances the protection of both lungs and diaphragm, prioritising lung protection when conflicts arise by monitoring respiratory effort and ensuring patient-ventilator synchrony. Recent evidence suggests that ARDS morphology (i.e. focal or non-focal) can significantly influence the effectiveness of ventilation strategies, offering a more personalised treatment approach for ARDS patients. Computational simulation offers a novel way to generate pre-clinical evidence and physiological insights, informing future clinical trial designs. This project aims to investigate the physiological mechanisms and efficacy of different non-conventional ventilation modes such as APRV, HFOV, lung-diaphragm protective ventilation and ventilation personalised to lung morphology using high-fidelity computational models, comparing them with conventional ventilation methods. Throughout this PhD project, a multi-compartmental computational model is employed to simulate integrated respiratory scenarios, covering all aspects of pulmonary dynamics and gas exchange. This model, developed by my supervisors, has been validated in studies on conventional ventilation but has not yet been applied to non-conventional modes of ventilation. By adapting this simulator with patient data and clinical expertise from clinicians, this research will create individualised simulations for adult and paediatric patients.
Dr Camporota, an expert in intensive care, and Dr Yehya, a paediatric mechanical ventilation specialist, will provide critical datasets and insights, ensuring the model's clinical relevance. Both collaborators have ethics approval to share data, facilitating a comprehensive evaluation of novel ventilation strategies across different patient populations.
The project's goal is to identify patient subgroups that could benefit most from these alternative ventilation strategies. The findings will be published in leading medical journals to influence clinical trials, shape clinical practice, and improve outcomes in acute respiratory disease management.