Estimation of the time-varying elastance of the left and right ventricles (2013)
Type of ContentTheses / Dissertations
Degree NameDoctor of Philosophy
PublisherUniversity of Canterbury. Mechanical Engineering
AuthorsStevenson, Davidshow all
The intensive care unit treats the most critically ill patients in the hospital, and as such the clinical staff in the intensive care unit have to deal with complex, time-sensitive and life-critical situations. Commonly, patients present with multiple organ dysfunctions, require breathing and cardiovascular support, which make diagnosis and treatment even more challenging. As a result, clinical staff are faced with processing large quantities of often confusing information, and have to rely on experience and trial and error. This occurs despite the wealth of cardiovascular metrics that are available to the clinician.
Computer models of the cardiovascular system can help enormously in an intensive care setting, as they can take the monitored data, and aggregate it in such a way as to present a clear and understandable picture of the cardiovascular system. With additional help that such systems can provide, diagnosis can be more accurate and arrived at faster, alone with better optimised treatment that can start sooner, all of which results in decreased mortality, length of stay and cost.
This thesis presents a model of the cardiovascular system, which mimics a specific patient’s cardiovascular state, based on only metrics that are commonly measured in an intensive care setting. This intentional limitation gives rise to additional complexities and challenges in identifying the model, but do not stand in the way of achieving a model that can represent and track all the important cardiovascular dynamics of a specific patient. One important complication that comes from limiting the data set is need for an estimation for the ventricular time-varying elastance waveform. This waveform is central to the dynamics of the cardiovascular model and is far too invasive to measure in an intensive care setting.
This thesis thus goes on to present a method in which the value-normalised ventricular time-varying elastance is estimated from only metrics which are commonly available in an intensive care setting. Both the left and the right ventricular time-varying elastance are estimated with good accuracy, capturing both the shape and timing through the progress of pulmonary embolism and septic shock. For pulmonary embolism, with the algorithm built from septic shock data, a time-varying elastance waveform with median error of 1.26% and 2.52% results for the left and right ventricles respectively. For septic shock, with the algorithm built from pulmonary embolism data, a time-varying elastance waveform with median error of 2.54% and 2.90% results for the left and right ventricles respectively. These results give confidence that the method will generalise to a wider set of cardiovascular dysfunctions.
Furthermore, once the ventricular time-varying elastance is known, or estimated to a adequate degree of accuracy, the time-varying elastance can be used in its own right to access valuable information about the state of the cardiovascular system. Due to the centrality and energetic nature of the time-varying elastance waveform, much of the state of the cardiovascular system can be found within the waveform itself. In this manner this thesis presents three important metrics which can help a clinician distinguish between, and track the progress of, the cardiovascular dysfunctions of pulmonary embolism and septic shock, from estimations based of the monitored pressure waveforms. With these three metrics, a clinician can increase or decrease their probabilistic measure of pulmonary embolism and septic shock.