Optimising Mechanical Ventilation
Mechanical ventilation is the primary therapy for patients with respiratory failure in the intensive care unit (ICU). Up to ~60% of all ICU patients require MV, and this patient group stays 50-100% longer in the ICU: MV almost doubles the cost of an ICU stay. When MV-associated damage to the lung occurs, this further increases the ICU stay, complicates patient management (leading to increased cost), and has long term consequences for patient quality of life. Clinicians are often faced with the task of making decisions based on an overwhelming amount of data that is difficult to interpret when caring for patients on MV. This can result in assessment that relies heavily on the subjective “clinician experience” as opposed to an objective assessment using patient data. What is needed is the ability to quantify and monitor the patient-specific state of the lung with regard to MV care, and the ability to do so at every breath so clinicians can be alerted to changes in patient response as soon as they occur.
Our Flagship is testing and validating the virtual lung as a bedside tool for providing rapid analysis of patient lung mechanics during MV. The tool is aimed at minimising lung damage in MV, identifying patients who are at risk of developing lung injury, and titrating ventilation protocols to individual patients to optimise their outcome. The goal is to provide the virtual lung in a comprehensive, easy-to-use software package that will integrate seamlessly with existing sensors in the ICU and provide a means for clinicians to make more objective decisions when assessing patients on mechanical ventilation.
Our collaborations with international groups complement the work of the Flagship: we are using the virtual lung to understand patient-specific differences in gas exchange and lung injury during prone posture in MV (NIH grant led by Prof Sue Hopkins, UCSD), optimisation of multiple frequency oscillatory ventilation (Department of Defense grant led by A/Prof David Kaczka, University of Iowa), and methods for improving electrical impedance tomography for use as a bedside imaging tool (Prof Knut Möller, Furtwangen University).