Improved pressure contour analysis for estimating cardiac stroke volume using pulse wave velocity measurement.

peer reviewedBACKGROUND: Pressure contour analysis is commonly used to estimate cardiac performance for patients suffering from cardiovascular dysfunction in the intensive care unit. However, the existing techniques for continuous estimation of stroke volume (SV) from pressure measurement can be unreliable during hemodynamic instability, which is inevitable for patients requiring significant treatment. For this reason, pressure contour methods must be improved to capture changes in vascular properties and thus provide accurate conversion from pressure to flow. METHODS: This paper presents a novel pressure contour method utilizing pulse wave velocity (PWV) measurement to capture vascular properties. A three-element Windkessel model combined with the reservoir-wave concept are used to decompose the pressure contour into components related to storage and flow. The model parameters are identified beat-to-beat from the water-hammer equation using measured PWV, wave component of the pressure, and an estimate of subject-specific aortic dimension. SV is then calculated by converting pressure to flow using identified model parameters. The accuracy of this novel method is investigated using data from porcine experiments (N = 4 Pietrain pigs, 20-24.5 kg), where hemodynamic properties were significantly altered using dobutamine, fluid administration, and mechanical ventilation. In the experiment, left ventricular volume was measured using admittance catheter, and aortic pressure waveforms were measured at two locations, the aortic arch and abdominal aorta. RESULTS: Bland-Altman analysis comparing gold-standard SV measured by the admittance catheter and estimated SV from the novel method showed average limits of agreement of +/-26% across significant hemodynamic alterations. This result shows the method is capable of estimating clinically acceptable absolute SV values according to Critchely and Critchely. CONCLUSION: The novel pressure contour method presented can accurately estimate and track SV even when hemodynamic properties are significantly altered. Integrating PWV measurements into pressure contour analysis improves identification of beat-to-beat changes in Windkessel model parameters, and thus, provides accurate estimate of blood flow from measured pressure contour. The method has great potential for overcoming weaknesses associated with current pressure contour methods for estimating SV

Blood pressure waveform contour analysis for assessing peripheral resistance changes in sepsis.

peer reviewedBACKGROUND: This paper proposes a methodology for helping bridge the gap between the complex waveform information frequently available in an intensive care unit and the simple, lumped values favoured for rapid clinical diagnosis and management. This methodology employs a simple waveform contour analysis approach to compare aortic, femoral and central venous pressure waveforms on a beat-by-beat basis and extract lumped metrics pertaining to the pressure drop and pressure-pulse amplitude attenuation as blood passes through the various sections of systemic circulation. RESULTS: Validation encompasses a comparison between novel metrics and well-known, analogous clinical metrics such as mean arterial and venous pressures, across an animal model of induced sepsis. The novel metric Ofe --> vc, the direct pressure offset between the femoral artery and vena cava, and the clinical metric, DeltaMP, the difference between mean arterial and venous pressure, performed well. However, Ofe --> vc reduced the optimal average time to sepsis detection after endotoxin infusion from 46.2 min for DeltaMP to 11.6 min, for a slight increase in false positive rate from 1.8 to 6.2%. Thus, the novel Ofe --> vc provided the best combination of specificity and sensitivity, assuming an equal weighting to both, of the metrics assessed. CONCLUSIONS: Overall, the potential of these novel metrics in the detection of diagnostic shifts in physiological behaviour, here driven by sepsis, is demonstrated

Unexpected organellar locations of ESCRT machinery in Giardia intestinalis and complex evolutionary dynamics spanning the transition to parasitism in the lineage Fornicata

Background: Comparing a parasitic lineage to its free-living relatives is a powerful way to understand how that evolutionary transition to parasitism occurred. Giardia intestinalis (Fornicata) is a leading cause of gastrointestinal disease world-wide and is famous for its unusual complement of cellular compartments, such as having peripheral vacuoles instead of typical endosomal compartments. Endocytosis plays an important role in Giardia’s pathogenesis. Endosomal sorting complexes required for transport (ESCRT) are membrane-deforming proteins associated with the late endosome/multivesicular body (MVB). MVBs are ill-defined in G. intestinalis, and roles for identified ESCRT-related proteins are not fully understood in the context of its unique endocytic system. Furthermore, components thought to be required for full ESCRT functionality have not yet been documented in this species. Results: We used genomic and transcriptomic data from several Fornicata species to clarify the evolutionary genome streamlining observed in Giardia, as well as to detect any divergent orthologs of the Fornicata ESCRT subunits. We observed differences in the ESCRT machinery complement between Giardia strains. Microscopy-based investigations of key components of ESCRT machinery such as GiVPS36 and GiVPS25 link them to peripheral vacuoles, highlighting these organelles as simplified MVB equivalents. Unexpectedly, we show ESCRT components associated with the endoplasmic reticulum and, for the first time, mitosomes. Finally, we identified the rare ESCRT component CHMP7 in several fornicate representatives, including Giardia and show that contrary to current understanding, CHMP7 evolved from a gene fusion of VPS25 and SNF7 domains, prior to the last eukaryotic common ancestor, over 1.5 billion years ago. Conclusions: Our findings show that ESCRT machinery in G. intestinalis is far more varied and complete than previously thought, associates to multiple cellular locations, and presents changes in ESCRT complement which pre-date adoption of a parasitic lifestyle

Unexpected organellar locations of ESCRT machinery in Giardia intestinalis and complex evolutionary dynamics spanning the transition to parasitism in the lineage Fornicata

Background: Comparing a parasitic lineage to its free-living relatives is a powerful way to understand how that evolutionary transition to parasitism occurred. Giardia intestinalis (Fornicata) is a leading cause of gastrointestinal disease world-wide and is famous for its unusual complement of cellular compartments, such as having peripheral vacuoles instead of typical endosomal compartments. Endocytosis plays an important role in Giardia's pathogenesis. Endosomal sorting complexes required for transport (ESCRT) are membrane-deforming proteins associated with the late endosome/multivesicular body (MVB). MVBs are ill-defined in G. intestinalis, and roles for identified ESCRT-related proteins are not fully understood in the context of its unique endocytic system. Furthermore, components thought to be required for full ESCRT functionality have not yet been documented in this species. Results: We used genomic and transcriptomic data from several Fornicata species to clarify the evolutionary genome streamlining observed in Giardia, as well as to detect any divergent orthologs of the Fornicata ESCRT subunits. We observed differences in the ESCRT machinery complement between Giardia strains. Microscopy-based investigations of key components of ESCRT machinery such as GiVPS36 and GiVPS25 link them to peripheral vacuoles, highlighting these organelles as simplified MVB equivalents. Unexpectedly, we show ESCRT components associated with the endoplasmic reticulum and, for the first time, mitosomes. Finally, we identified the rare ESCRT component CHMP7 in several fornicate representatives, including Giardia and show that contrary to current understanding, CHMP7 evolved from a gene fusion of VPS25 and SNF7 domains, prior to the last eukaryotic common ancestor, over 1.5 billion years ago. Conclusions: Our findings show that ESCRT machinery in G. intestinalis is far more varied and complete than previously thought, associates to multiple cellular locations, and presents changes in ESCRT complement which pre-date adoption of a parasitic lifestyle

Minimally invasive, patient specific, beat-by-beat estimation of left ventricular time varying elastance.

peer reviewedBACKGROUND: The aim of this paper was to establish a minimally invasive method for deriving the left ventricular time varying elastance (TVE) curve beat-by-beat, the monitoring of which's inter-beat evolution could add significant new data and insight to improve diagnosis and treatment. The method developed uses the clinically available inputs of aortic pressure, heart rate and baseline end-systolic volume (via echocardiography) to determine the outputs of left ventricular pressure, volume and dead space volume, and thus the TVE curve. This approach avoids directly assuming the shape of the TVE curve, allowing more effective capture of intra- and inter-patient variability. RESULTS: The resulting TVE curve was experimentally validated against the TVE curve as derived from experimentally measured left ventricular pressure and volume in animal models, a data set encompassing 46,318 heartbeats across 5 Pietrain pigs. This simulated TVE curve was able to effectively approximate the measured TVE curve, with an overall median absolute error of 11.4% and overall median signed error of -2.5%. CONCLUSIONS: The use of clinically available inputs means there is potential for real-time implementation of the method at the patient bedside. Thus the method could be used to provide additional, patient specific information on intra- and inter-beat variation in heart function

Model based arterial flow and stroke volume estimation for hemodynamic monitoring in a critical care environment

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

Incorporating pulse wave velocity into model-based pulse contour analysis method for estimation of cardiac stroke volume

Background and Objectives: Stroke volume (SV) and cardiac output (CO) are important metrics for hemodynamic management of critically ill patients. Clinically available devices to continuously monitor these metrics are invasive, and less invasive methods perform poorly during hemodynamic instability. Pulse wave velocity (PWV) could potentially improve estimation of SV and CO by providing information on changing vascular tone. This study investigates whether using PWV for parameter identification of a model-based pulse contour analysis method improves SV estimation accuracy. Methods: Three implementations of a 3-element windkessel pulse contour analysis model are compared: constant-Z, water hammer, and BramwellHill methods. Each implementation identifies the characteristic impedance parameter (Z) differently. The first method identifies Z statically and does not use PWV, and the latter two methods use PWV to dynamically update Z. Accuracy of SV estimation is tested in an animal trial, where interventions induce severe hemodynamic changes in 5 pigs. Model-predicted SV is compared to SV measured using an aortic flow probe. Results: SV percentage error had median bias and [(IQR); (2.5th, 97.5th percentiles)] of -0.5% [(-6.1%, 4.7%); (-50.3%, +24.1%)] for the constantZ method, 0.6% [(-4.9%, 6.2%); (-43.4%, +29.3%)] for the water hammer method, and 0.8% [(-6.5, 8.6); (-37.1%, +47.6%)] for the Bramwell-Hill method. Conclusion: Incorporating PWV for dynamic Z parameter identification through either the Bramwell-Hill equation or the water hammer equation does not appreciably improve the 3-element windkessel pulse contour analysis model’s prediction of SV during hemodynamic changes compared to the constant-Z method

Proof of concept non-invasive estimation of peripheral venous oxygen saturation

Abstract Background Pulse oximeters continuously monitor arterial oxygen saturation. Continuous monitoring of venous oxygen saturation (SvO2) would enable real-time assessment of tissue oxygen extraction (O2E) and perfusion changes leading to improved diagnosis of clinical conditions, such as sepsis. Methods This study presents the proof of concept of a novel pulse oximeter method that utilises the compliance difference between arteries and veins to induce artificial respiration-like modulations to the peripheral vasculature. These modulations make the venous blood pulsatile, which are then detected by a pulse oximeter sensor. The resulting photoplethysmograph (PPG) signals from the pulse oximeter are processed and analysed to develop a calibration model to estimate regional venous oxygen saturation (SpvO2), in parallel to arterial oxygen saturation estimation (SpaO2). A clinical study with healthy adult volunteers (n = 8) was conducted to assess peripheral SvO2 using this pulse oximeter method. A range of physiologically realistic SvO2 values were induced using arm lift and vascular occlusion tests. Gold standard, arterial and venous blood gas measurements were used as reference measurements. Modulation ratios related to arterial and venous systems were determined using a frequency domain analysis of the PPG signals. Results A strong, linear correlation (r 2  = 0.95) was found between estimated venous modulation ratio (RVen) and measured SvO2, providing a calibration curve relating measured RVen to venous oxygen saturation. There is a significant difference in gradient between the SpvO2 estimation model (SpvO2 = 111 − 40.6*R) and the empirical SpaO2 estimation model (SpaO2 = 110 − 25*R), which yields the expected arterial-venous differences. Median venous and arterial oxygen saturation accuracies of paired measurements between pulse oximeter estimated and gold standard measurements were 0.29 and 0.65%, respectively, showing good accuracy of the pulse oximeter system. Conclusions The main outcome of this study is the proof of concept validation of a novel pulse oximeter sensor and calibration model to assess peripheral SvO2, and thus O2E, using the method used in this study. Further validation, improvement, and application of this model can aid in clinical diagnosis of microcirculation failures due to alterations in oxygen extraction