Mechanical Ventilation Modelling and Optimisation
Thesis DisciplineMechanical Engineering
Degree GrantorUniversity of Canterbury
Degree NameMaster of Engineering
Acute Respiratory Distress Syndrome (ARDS) is associated with lung inflammation and fluid filling, resulting in a stiffer lung with reduced intrapulmonary gas volume. ARDS patients are admitted to the Intensive Care Unit (ICU) and require Mechanical Ventilation (MV) for breathing support. Positive End Expiratory Pressure (PEEP) is applied to aid recovery by improving gas exchange and maintaining recruited lung volume. However, high PEEP risks further lung injury due to overstretching of healthy lung units, and low PEEP risks further lung injury due to the repetitive opening and closing of lung units. Thus, selecting PEEP is a balance between avoiding over-stretching and repetitive opening of alveoli. Furthermore, specific protocols to determine optimal PEEP do not currently exist, resulting in variable PEEP selection. Thus, ensuring an optimal PEEP would have significant impact on patient mortality, and the cost and duration of MV therapy.
Two important metrics that can be used to aid MV therapy are the elastance of the lungs as a function of PEEP, and the quantity of recruited lung volume as a function of PEEP. This thesis describes several models and model-based methods that can be used to select optimal PEEP in the ICU. Firstly, a single compartment lung model is investigated for its ability to capture the respiratory mechanics of a mechanically ventilated ARDS patient. This model is then expanded upon, leading to a novel method of mapping and visualising dynamic respiratory system elastance. Considering how elastance changes, both within a breath and throughout the course of care, provides a new clinical perspective. Next, a model using only the expiratory portion of the breathing cycle is developed and presented, providing an alternative means to track changes in disease state throughout MV therapy. Finally, four model-based methods are compared based on their capability of estimating the quantity of recruited lung volume due to PEEP.
The models and model-based methods described in this thesis enable rapid parameter identification from readily available clinical data, providing a means of tracking lung condition and selecting optimal patient-specific PEEP. Each model is validated using data from clinical ICU patients and/or experimental ARDS animal models.