Cardiac and circulartory dysfunction are responsible for approximately a third of all intensive care admissions and deaths in New Zealand, reflecting similar statistics globally. Diagnosing and treating cardiovascular disease is made more diWcult by the complex and interdependent nature of the circulatory system, where compounding symptomatology makes it diWcult to deduce the specific underlying mechanisms triggering the dysfunction. The result is high variability and cost of care, and suboptimal outcomes.
Currently, monitoring a patient’s hemodynamic state is undertaken using metrics like arterial and venous pressure, heart rate, gas exchange variables and electrocardiogram (ECG). While these metrics are easy to measure, they also change in response to many physiological factors. Thus, they are capable of indicating at a global level potential hemodynamic instability, but less capable of monitoring cardiac performance directly. Direct cardiac performance metrics, such as stroke volume (SV )/cardiac output (CO), are called for in consensus statements, but are difficult to measure. The trade-off between the level of invasion, accuracy and frequency/duration of monitoring, have not yet been satisfactorily mitigated.
Cardiovascular models provide a potential avenue for clinically applicable, minimally or non-additionally invasive hemodynamic monitoring. Cardiovascular models exploit the relationship between common clinical metrics, like pressure, and the preferred but more diWcult to measure cardiac performance metrics, like SV . The performance of a model is dictated by two facets. First, the theory of the model, often a mix of physiology and mathematics, ultimately seeking to provide a simplified/abstracted representation of the cardiovascular system. Second, how the method is actually implemented, including aspects of data acquisition, signal processing and parameter identification. The exact algorithms used in many commercial devices for monitoring SV /CO, are commercially sensitive and therefore it is often diWcult to critique specific aspects of the underlying model approach. However, generally there remains an issue of commercial devices performing well in stable patients, but struggling to capture unstable hemodynamics and stable behaviour thereafter, without re-calibration of the model. Model re-calibration often involves an independent measurement of the target variable, SV /CO, thus, frequent re-calibration defeats the purpose of continuous monitoring. Equally, there can be a delay in outward indicators of hemodynamic instability, making it diWcult to determine when re-calibration is required.
Thus, this thesis sought to develop a clinically applicable, non-additionally invasive cardio- vascular model for estimating SV to overcome the limitations of similar models, both commercial and in literature. Specifically, the model developed was based on three-element windkessel theory and parameters were identified via pulse contour analysis (PCA). Identifying parameters via PCA meant the model always rejects the current patient state, rather than relying solely on model calibration during a prior patient state.
Most models focus on clinically relevant SV /CO measures, despite three-element windkessel theory primarily describing a relationship between the pressure and flow waveforms. Thus, this thesis developed novel end-systole detection methods to improve PCA- based parameter identification, in clinically applicable arterial pressure waveforms. Having this focus meant the model implementation rejected the model theory well, enabling it to estimate the physiologically accurate flow waveforms that other methods cannot.
Moreover, the results showed failure to derive physiologically accurate profiles meant one or more of the windkessel model assumptions had been violated. Thus, any accurate SV estimation from unphysiological flow waveforms, was contingent on the independent SV calibration, and the resulting model performance would not reject its underlying theory. This research clearly delineates when and how these issues arise.
More specifically, a novel aspect of this thesis is its illustration of windkessel model limitations and their impact on PCA-based parameter identification. Specifically, two novel methods of end-systole detection are developed in the thesis, one specifically for detecting dicrotic notches. However, the three-element windkessel is not capable of describing rejected wave phenomena, like the dicrotic notch. Thus, the thesis illustrated the detrimental effects of dicrotic notch presence in the diastolic part of the pressure waveform for PCA parameter identification, as well as developing methods to mitigate its effect. Ultimately, the second novel end-systole detection method enabled the more clinically applicable femoral artery waveform to be used. The results showed its shape, often void of dicrotic notches, was more aligned with windkessel model theory, aiding parameter identification, as well as making the implementation more clinically applicable. Thus, the results showed how the advantages of easy end-systole detection, via the dicrotic notch, can be outweighed by its reduced compatibility with the well-accepted windkessel model and PCA parameter identification.
The analyses conducted in this research used porcine animal trials for the development, testing and validation of methods. Since the overall goal of the thesis was to develop a clinically applicable method for monitoring SV during periods of hemodynamic instability, the experimental protocols included clinically relevant disease states and treatments. Bland-Altman analysis showed beat-to-beat SV error between the model estimated and aortic flow probe measurement, had limits of agreement (95% of data) of ±32%, where 90% of the data falls within -24.2% and +27.9%. Mean beat-to-beat errors >24% were only associated with severe, rapid onset of a sepsis like response, which would be clinically evident.
The stated results are from the preferred model implementation in the thesis, where the only fixed model parameter was windkessel characteristic impedance (Zc,w). Specifically, the static Zc,w value was found via calibration using an independent SV measurement, during a period of stable hemodynamics. Two methods of updating Zc,w on a beat-to-beat basis, were also tested, both requiring pulse transit time (PTT) monitoring. One method was based on the water hammer equation, while the other was a hybrid approach, using the Bramwell-Hill equation and PCA. However, these dynamic approaches to Zc,w identification did not cause significant improvement in the results. Thus, the additional patient burden of monitoring PTT on a beat-to-beat basis could not be justified.
While it is diWcult to compare the model implementation presented in this thesis to commercial devices tested on different data sets, it appears the results represent a significant improvement over existing methods. In particular, the model developed in this thesis pro- vides a physiological flow waveform in conjunction with beat-to-beat SV , where the former enables quantitative and qualitative verification of successful parameter identification, instilling confidence in the subsequent SV estimate.
Finally, successful clinical implementation of the model would significantly impact intensive care unit (ICU) practice. The patient specific manner in which the model is implemented, could enable personalised titrating/optimisation of care to quantitatively estimate cardiac function on a beat-to-beat basis, something which is not yet possible in a clinical environment
