Shun Kamoi

Continuous stroke volume estimation from aortic pressure using zero dimensional cardiovascular model: proof of concept study from porcine experiments.

Introduction Accurate, continuous, left ventricular stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status and response to therapy. However, direct measurements are highly invasive in clinical practice, and current procedures for estimating SV require specialized devices and significant approximation. Method This study investigates the accuracy of a three element Windkessel model combined with an aortic pressure waveform to estimate SV. Aortic pressure is separated into two components capturing; 1) resistance and compliance, 2) characteristic impedance. This separation provides model-element relationships enabling SV to be estimated while requiring only one of the three element values to be known or estimated. Beat-to-beat SV estimation was performed using population-representative optimal values for each model element. This method was validated using measured SV data from porcine experiments (N = 3 female Pietrain pigs, 29–37 kg) in which both ventricular volume and aortic pressure waveforms were measured simultaneously. Results The median difference between measured SV from left ventricle (LV) output and estimated SV was 0.6 ml with a 90% range (5th–95th percentile) −12.4 ml–14.3 ml. During periods when changes in SV were induced, cross correlations in between estimated and measured SV were above R = 0.65 for all cases. Conclusion The method presented demonstrates that the magnitude and trends of SV can be accurately estimated from pressure waveforms alone, without the need for identification of complex physiological metrics where strength of correlations may vary significantly from patient to patient.

Modelling of the nonlinear end-systolic pressure-volume relation and volume-at-zero-pressure in porcine experiments

The End-Systolic Pressure-Volume Relation (ESPVR) is generally modelled as a linear relationship between P and V as cardiac reflexes, such as the baroreflex, are typically suppressed in experiments. However, ESPVR has been observed to behave in a curvilinear fashion when cardiac reflexes are not suppressed, suggesting the curvilinear function may be more clinically appropriate. Data was gathered from 41 vena cava occlusion manoeuvres performed experimentally at a variety of PEEPs across 6 porcine specimens, and ESPVR determined for each pig. An exponential model of ESPVR was found to provide a higher correlation coefficient than a linear model in 6 out of 7 cases, and a lower Akaike Information Criterion (AIC) value in all cases. Further, the exponential ESPVR provided positive V0 values in a physiological range in 6 out of 7 cases analysed, while the linear ESPVR produced positive V0 values in only 3 out of 7 cases, suggesting linear extrapolation of ESPVR to determine V0 may be flawed.

Accuracy of Stroke Volume Estimation via Reservoir Pressure Concept and Three Element Windkessel Model

Abstract Accurate stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status. However, direct measurements are too invasive in clinical practice and current procedures for estimating SV require specialised devices. This study presents an analysis of the accuracy of SV estimation by combining pulse-wave and windkessel analyses. What makes this study different to existing pulse-contour analyses is that pressure contour variation due to altered arterial mechanical properties (resistance, characteristic impedance and compliance) were related to correct corresponding pressure zones, enabling this model to more accurately capture SV from aortic pressure measurements alone. Using data from three porcine experiments, the median difference between measured and estimated SV was 1.4 ml with a 90% range (5 th -95 th percentile) -11.3ml - 12.2ml. This result relies on an estimate of the average value of just one windkessel parameter. The presented method demonstrates that SV can be estimated from pressure waveforms alone, without the need for identification of complex physiological metrics where strength of correlations may vary from patient to patient.

Pre-ejection period, the reason why the electrocardiogram Q-wave is an unreliable indicator of pulse wave initialization

OBJECTIVE Pulse wave velocity measurements are an indicator of arterial stiffness and possible cardiovascular dysfunction. It is usually calculated by measuring the pulse transit time (PTT) over a known distance through the arteries. In animal studies, reliable PTT measures can be obtained using two pressure catheters. However, such direct, invasive methods are undesirable in clinical settings. A less invasive alternative measure of PTT is pulse arrival time (PAT), the time between the Q-wave of an electrocardiogram (ECG) and the arrival of the foot of the beats pressure waveform at one pressure catheter. Since the Q-wave signifies the start of ventricular contraction, PAT includes the pre-ejection period (PEP), a time where no blood is ejected. Thus, inter- or intra- subject variation in PEP could result in poor correlation between pulse arrival time (PAT) and the desired pulse transit time (PTT). APPROACH This study looks at the relationship between PAT and PTT, over a range of common critical care therapies and determines the effect of PEP on PAT as a possible surrogate of PTT in a critical care environment. The analysis uses data from five porcine experiments, where ECG, aortic arch and abdominal aortic pressure were measured simultaneously, over a range of induced hemodynamic conditions. RESULTS The resulting correlations of PAT verse PTT varied within pigs and across interventions (r 2  =  0.32-0.69), and across pigs (r 2  =  0.05-0.60). Variability was due to three main causes. First, the interventions themselves effect PEP and PTT differently, second, pig specific response to the interventions, and third, inter- and intra- pig variability in PEP, independent of PTT. SIGNIFICANCE The overall analysis shows PAT is an unreliable measure of PTT and a poor surrogate under clinical interventions common in a critical care setting, due to intra- and inter- subject variability in PEP.

Beat-by-Beat Estimation of the Left Ventricular Pressure–Volume Loop Under Clinical Conditions

This paper develops a method for the minimally invasive, beat-by-beat estimation of the left ventricular pressure–volume loop. This method estimates the left ventricular pressure and volume waveforms that make up the pressure–volume loop using clinically available inputs supported by a short, baseline echocardiography reading. Validation was performed across 142,169 heartbeats of data from 11 Piétrain pigs subject to two distinct protocols encompassing sepsis, dobutamine administration and clinical interventions. The method effectively located pressure–volume loops, with low overall median errors in end-diastolic volume of 8.6%, end-systolic volume of 17.3%, systolic pressure of 19.4% and diastolic pressure of 6.5%. The method further demonstrated a low overall mean error of 23.2% predicting resulting stroke work, and high correlation coefficients along with a high percentage of trend compass ‘in band’ performance tracking changes in stroke work as patient condition varied. This set of results forms a body of evidence for the potential clinical utility of the method. While further validation in humans is required, the method has the potential to aid in clinical decision making across a range of clinical interventions and disease state disturbances by providing real-time, beat-to-beat, patient specific information at the intensive care unit bedside without requiring additional invasive instrumentation.

A Minimally Invasive Method for Beat-by-Beat Estimation of Cardiac Pressure-Volume Loops.

This paper develops a minimally invasive means of estimating a patient-specific cardiac pressure-volume loop beat-to-beat. This method involves estimating the left ventricular pressure and volume waveforms using clinically available information including heart rate and aortic pressure, supported by a baseline echocardiography reading. Validation of the method was performed across an experimental data set spanning 5 Piétrain pigs, 46,318 heartbeats and a diverse clinical protocol. The method was able to accurately locate a pressure-volume loop, identifying the end-diastolic volume, end-systolic volume, mean-diastolic pressure and mean-systolic pressure of the ventricle with reasonable accuracy. While there were larger percentage errors associated with stroke work derived from the estimated pressure-volume loops, there was a strong correlation (average R value of 0.83) between the estimated and measured stroke work values. These results provide support for the potential of the method to track patient condition, in real-time, in a clinical environment. This method has the potential to yield additional information from readily available waveforms to aid in clinical

Stroke Volume Estimation using Aortic Pressure Measurements and Aortic Cross Sectional Area: Proof of Concept

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of measurement devices. Current devices for indirect monitoring of SV are shown to be inaccurate during sudden hemodynamic changes. This paper presents a novel SV estimation using readily available aortic pressure measurements and aortic cross sectional area, using data from a porcine experiment where medical interventions such as fluid replacement, dobutamine infusions, and recruitment maneuvers induced SV changes in a pig with circulatory shock. Measurement of left ventricular volume, proximal aortic pressure, and descending aortic pressure waveforms were made simultaneously during the experiment. From measured data, proximal aortic pressure was separated into reservoir and excess pressures. Beat-to-beat aortic characteristic impedance values were calculated using both aortic pressure measurements and an estimate of the aortic cross sectional area. SV was estimated using the calculated aortic characteristic impedance and excess component of the proximal aorta. The median difference between directly measured SV and estimated SV was -1.4ml with 95% limit of agreement +/- 6.6ml. This method demonstrates that SV can be accurately captured beat-to-beat during sudden changes in hemodynamic state. This novel SV estimation could enable improved cardiac and circulatory treatment in the critical care environment by titrating treatment to the effect on SV.

Estimating Relative Change in Ventricular Stroke Work from Aortic Pressure Alone: Proof of Concept Study

Introduction Shortening of myocyte fibers is responsible for the contraction of the human heart and is the main driving mechanism for blood circulation in the cardio vascular system. Currently, two methods exist for initialization of the fiber orientation in a computational heart model. Setting of fiber orientation using diffusion tensor MRI is preferable, but can introduce possibly large inaccuracies and is nearly impossible for alive patients. As alternative approach, rule-based algorithms are used, being able to generate a fiber orientation based a few physiological parameters only. The drawback is, that model-based errors are introduced and a wrong choice of the parameter set might turn the whole simulation useless. In this study, the influence of different fiber angles in the ventricles on the pumping function of the heart was determined. Methods Different set of fiber angles were compared using computer simulation of an elastomachanical model of the whole heart. The heart geometry was based on segmented MRI data from a healthy volunteer. Fibers in the ventricles were set using a semi-automatic Laplace-Dirichlet Rule-Based algorithm presented by J. D. Bayer et al. 2012. The algorithm took four angles as input parameters, describing absolute and relative orientation of fibers on the endoand epicard. Results From the simulation results, PV-diagrams were created for the left ventricle for all fiber orientation settings. Results showed, that the pump function of the heart depended significantly on the fiber orientation angles. Furthermore, the atrioventricular plane displacement was anaylzed for all fiber orientation settings. Also here, a strong dependency on the fiber orientation was found. Conclusion Since the pumping function of the heart depends significantly on the fiber orientation, it is essential to know in which way errors introduced to the model due to uncertainties in the fiber orientation affect the mechanical behavior. Here, a sensitivity analysis can help to gain insight. Biomed Tech 2014; 59 (s1)