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

BACKGROUND: 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.

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

BACKGROUND: 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 Piétrain 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.

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.

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.

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.

An Assessment of the relationship between dicrotic notch timing and cardiac preload.

Cardiovascular disease (CVD) patient outcomes can be improved by extracting and synthesizing as much useful information as possible from a limited number of available measurements. An important metric in assessing the pathological state of CVD patients is cardiac preload. Left ventricular preload can be estimated through the surrogate measurement of left ventricular end diastolic volume (LVEDV). However, cardiac volumes are difficult to measure, clinically. This study develops a 3 parameter model relating the location of the dicrotic notch in the aortic waveform to LVEDV. This model was constructed using data from porcine experiments (N = 5 pietrain pigs, weights 20-28kg). The median difference between the observed LVEDV and modelled LVEDV was 5.4%, with a 100% range of 3.0% to 15.1%. Model parameters varied between individuals as well as contractile states. The median correlation was ρ = 0.77, with a minimum of 0.58 and maximum of 0.95. This model could be used to estimate prseload from the commonly measured aortic pressure waveform.

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.