Model-Based Mechanical Ventilation for the Critically Ill (2013)
Type of ContentTheses / Dissertations
Thesis DisciplineMechanical Engineering
Degree NameDoctor of Philosophy
PublisherUniversity of Canterbury. Mechanical Engineering
AuthorsChiew, Yeong Shiongshow all
Mechanical ventilation (MV) is the primary form of therapeutic support for patients with acute respiratory failure (ARF) or acute respiratory distress syndrome (ARDS) until the underlying disease is resolved. However, as patient disease state and response to MV are highly variable, clinicians often rely on experience to set MV. The result is more variable care, as there are currently no standard approaches to MV settings. As a result of the common occurrence of MV and variability in care, MV is one of the most expensive treatments in critical care. Thus, an approach capable of guiding patient-specific MV is required and this approach could potentially save significant cost.
This research focuses on developing models and model-based approaches to analyse and guide patient-specific MV care. Four models and metrics are developed, and each model is tested in experimental or clinical trials developed for the purpose. Each builds the understanding and methods necessary for an overall approach to guide MV in a wide range of patients.
The first model, a minimal recruitment model, captures the recruitment of an injured lung and its response to positive end expiratory pressure (PEEP). However, the model was only previously validated in diagnosed ARDS patients, and was not proven to capture behaviours seen in healthy patients. This deficiency could potentially negate its ability to track disease state, which is crucial in providing rapid diagnosis and patient-specific MV in response to changes in patient condition. Hence, the lack of validation in disease state progression monitoring from ARDS to healthy, or vice-versa, severely limits its application in real-time monitoring and decision support. To address this issue, an experimental ARDS animal model is developed to validate the model across the transition between healthy and diseased states.
The second model, a single compartment linear lung model, models the lung as a conducting airway connected to an elastic compartment. This model is used to estimate the respiratory mechanics (Elastance and Resistance) of an ARDS animal model during disease progression and recruitment manoeuvres. This model is later extended to capture high resolution, patient-specific time-varying respiratory mechanics during each breathing cycle. This extended model is tested in ARDS patients, and was used to titrate patient-specific PEEP using a minimum elastance metric that balances recruitment and the risk of lung overdistension and ventilation-induced injury.
Studies have revealed that promoting patients to breathe spontaneously during MV can improve patient outcomes. Thus, there is significant clinical trend towards using partially assisted ventilation modes, rather than fully supported ventilation modes. In this study, the patient-ventilator interaction of a state of the art partially assisted ventilation mode, known as neurally adjusted ventilatory assist (NAVA), is investigated and compared with pressure support ventilation (PS). The matching of patient-specific inspiratory demand and ventilator supplied tidal volume for these two ventilation modes is assessed using a novel Range90 metric. NAVA consistently showed better matching than PS, indicating that NAVA has better ability to provide patient-specific ventilator tidal volume to match variable patient-specific demand. Hence, this new analysis highlights a critical benefit of partially assisted ventilation and thus the need to extend model-based methods to this patient group.
NAVA ventilation has been shown to improve patient-ventilator interaction compared to conventional PS. However, the patient-specific, optimal NAVA level remains unknown, and the best described method to set NAVA is complicated and clinically impractical. The Range90 metric is thus extended to analyse the matching ability of different NAVA levels, where it is found that response to different NAVA levels is highly patient-specific. Similar to the fully sedated MV case, and thus requiring models and metrics to help titrate care. More importantly, Range90 is shown to provide an alternative metric to help titrate patient-specific optimal NAVA level and this analysis further highlights the need for extended model-based methods to better guide these emerging partially assisted MV modes.
Traditionally, the respiratory mechanics of the spontaneously breathing (SB) patient cannot be estimated without significant additional invasive equipment and tests that interrupt normal care and are clinically intensive to carry out. Thus, respiratory mechanics and model-based methods are rarely used to guide partially assisted MV. Thus, there is significant clinical interest to use respiratory mechanics to guide MV in SB patients. The single compartment model is extended to effectively capture the trajectory of time-varying elastance for SB patients. Results show that without additional invasive equipment, the model was able estimate unique and clinically useful respiratory mechanics in SB patients. Hence, the extended single compartment model can be used as ‘a one model fits all’ means to guide patient-specific MV continuously and consistently, for all types of patient and ventilation modes, without interrupting care.
Overall, the model-based approaches presented in this thesis are capable of capturing physiologically relevant patient-specific parameters, and thus, characterise patient disease state and response to MV. With additional, larger scale clinical trials to test the performance and the impact of model-based methods on clinical outcome, the models can aid clinicians to guide MV decision making in the heterogeneous ICU population. Hence, this thesis develops, extends and validates several fundamental model-based metrics, models and methods to enable standardized patient-specific MV to improve outcome and reduce the variability and cost of care.