BRPI0714207A2 - Method and apparatus for continuous assessment of a cardiovascular parameter using waveform and arterial pulse pressure propagation time - Google Patents

Abstract

METHOD AND APPARATUS FOR CONTINUOUS EVALUATION OF A CARDIOVASCULAR PARAMETER USING WAVE SHAPE AND PRESSURE TIME FOR ARTERIAL PULSE PRESSURE. The present invention relates to a method and apparatus for determining a cardiovascular parameter including receiving an input signal corresponding to a blood pressure measurement over an interval that covers at least one cardiac cycle, determining a signal propagation time input, determine at least one statistical moment of the input signal, and determine an estimate of the cardiovascular parameter using the propagation time and at least one statistical moment.

Description

Report of the Invention Patent for "METHOD AND APPARATUS FOR THE CONTINUOUS EVALUATION OF A CARDIOVASCULAR PARAMETER USING WAVE FORM AND PULSE PRESSURE TIME".

Priority Claim under 35 U.S.C. §119

The present patent application claims the priority of Provisional Application No. 60 / 830,735 entitled "METHOD AND APPARATUS FOR A CONTINUOUS EVALUATION OF A CARDIOVASCULAR PARAMETER USING THE WHERE AND TIME OF PROPAGATION OF THE ARTERIAL PULSE PRESSURE". July 2006, and assigned to its owner and hereby expressly incorporated herein by reference. Field of the Invention

The present invention generally relates to a system and method for hemodynamic monitoring. More particularly, the invention relates to a system and method for estimating at least one cardiovascular parameter, such as vascular tone, arterial performance or endurance, stroke volume, cardiac output (CO), etc. ., from an individual using a pulse pulse pressure propagation time measurement and a waveform. Description of Related Art

Cardiac output (CO) is an important indicator not only for disease diagnosis but also for continuous monitoring of the condition of both human and animal individuals, including patients. Few hospitals are therefore without some form of conventional equipment to monitor cardiac output.

One way to measure CO is by using the well-known formula:

CO = HR * SV, (Equation 1)

where SV represents the systolic volume and HR represents the heart rate. SV is typically measured in liters and HR is typically measured in beats per minute, although other volume and time units may be used. Equation 1 expresses that the amount of blood the heart pumps for one unit of time (such as one minute) is equal to the amount it pumps in each beat (stroke) times the number of beats per unit of time.

Since HR is easy to measure using a wide range

of instruments, the calculation of CO usually depends on some technique to estimate SV. Conversely, any method that directly produces a value for CO can be used to determine SV by dividing by HR. CO or SV estimates can then be used to estimate, or contribute to estimating any parameter that can be derived from any of these values.

An invasive method for determining CO (or equivalently SV) is to mount a flow measuring device on a catheter, and then pass the catheter on the individual and maneuver it such that the device is in or near the individual's heart. Some such flow metering devices inject either a bolus of (usually heated) material or energy in an upward position, such as in the right atrium, and determine the flow based on the characteristics of the material or energy injected in a downward position, such as as in the pulmonary artery. Patents describing implementations of such invasive techniques (in particular thermodilution) include:

U.S. Patent No. 4,236,527 (Newbower et al., December 2

1980),

U.S. Patent No. 4,507,974 (Yelderman, April 2, 1985), U.S. Patent No. 5,146,414 (McKown et al., September 8,

1992), and 1997).

U.S. Patent No. 5,687,733 (McKown et al., November 18

Still other invasive devices are based on Fick's known technique, according to which CO is calculated as a function of oxygenation of mixed venous and arterial blood. In most cases, oxygenation is captured using right-heart catheterization. However, there have been proposals for systems that noninvasively measure venous and arterial oxygenation, in particular using multiple wavelengths of light, but so far they have not been accurate enough to allow satisfactory CO measurements in real patients. .

Invasive methods have obvious disadvantages. One such

The advantage is that heart catheterization is potentially dangerous, especially considering that individuals (especially intensive care patients) in whom it is performed are often hospitalized due to some really or potentially serious condition. Invasive methods also have less obvious disadvantages. One such disadvantage is that thermodilution relies on assumptions such as uniform dispersion of injected heat that affects the accuracy of measurements depending on how they are performed. In addition, introducing an instrument into blood flow may affect the value (eg flow rate) that the instrument measures. So there was an old need for a method that determined CO that is noninvasive (or at least as minimally invasive as possible) and accurate.

One blood feature that has been particularly proven to promise accurate determination of CO in a less invasive or noninvasive form is blood pressure. Popular blood pressure-based systems rely on the pulse contour method (PCM), which calculates an estimate of CO from beat-to-beat blood pressure waveform characteristics. In PCM, Windkessel (German word for tube) parameters (characteristic aortic impedance, performance, and total peripheral resistance) are used to construct a linear or nonlinear hemodynamic model of the aorta. In essence, blood flow is analogous to an electric current flow in a circuit in which an impedance is in series with a resistance and capacitance connected in parallel (performance). The three required parameters of the model are generally determined

or empirically, through a complex calibration process, or compiled "anthropometric" data, ie data on the age, sex, height, weight, etc., of other patients or test subjects. US Patent No. 5,400,793 (Wesseling, March 28, 1995) and US Patent No. 5,535,753 (Petrucelli et al., July 16, 1996) are representative of systems using a Windkessel circuit model to determine CO .

Many extensions to the simple two-element Windkessel model have been proposed hoping for better accuracy. Such an extension was developed by the Swiss physiologists Broemser and Ranke in their 1930 article entitled "Ueber die Messung des Schlagvolumens des Herzens auf unblutigem Wegf," Zeitung für Biologie 90 (1930) 467-507. In essence, Broemser's model, also known as a three-element Windkessel model, adds a third element to the basic two-element Windkessel model to simulate resistance to blood flow due to the pulmonary or aortic valve. PCM systems can monitor more or less continuous CO

again, without the need for a catheter to be left on the patient. In fact, some PCM systems operate using fingertip blood pressure measurements. A disadvantage of PCM systems, however, is that they are no more accurate than the particularly simple three-parameter model from which they are derived; In general, a much higher order model would be needed to account for precisely another phenomenon, such as the complex pattern of pressure waveforms due to multiple unsuccessful combinations of impedances caused, for example, by arterial branching. Further improvements were then proposed, with varying degrees of complexity.

The "Method and Apparatus for Measuring Cardiac Output" described by Salvatore Romano in US Patent No. 6,758,822, for example, represents a different attempt to improve upon PCM methods by estimating SV, either invasively or noninvasively such as a function of the relationship between the area under the entire pressure curve and a linear combination of various impedance components. In an attempt to be responsible for pressure reflections, the Roman system not only relies on the accurate estimates of noise inherent derivatives of the pressure function, but also relies on a series of numerical adjustments empirically determined to a mean pressure value.

At the core of various methods for estimating CO is an expression of the form:

CO = HR * (K * SVest), (Equation 2)

where HR is the heart rate, SVest is the estimated systolic volume, and K is a scaling factor related to arterial performance. Romano and Petrucelli, for example, rely on this term, as for the apparatus described in US Patent No. 6,071,244 (Band et al., June 6, 2000) and US Patent No. 6,348,038 (Band et al. others, February 19, 2002).

Another expression often used to determine CO is:

CO = MAP * C / thy, (Equation 3)

where MAP is mean arterial pressure, tau is an exponential pressure drop constant, and C, such as K, is a scaling factor related to arterial performance Κ. U.S. Patent No. 6,485,431 (Campbell, November 26, 2002) describes an apparatus using such an expression.

The accuracy of these methods may depend on how the KeC scaling factors are determined. In other words, an accurate estimate of performance (or some other functionally performance-related value) may be required. For example, Langwouters ("The Static Elastic Properties of 45 Human Thoracic and 20 In vitro Abdominal Aortas and the Parameters of a New Model" J. Biomechanics, Vol. 17, No. 6, pp. 425-435, 1984) describes the measurement. vascular performance per unit length in human aortas and relates it to the age and gender of the patient. An aortic length is determined to be proportional to the patient's height and weight. A nomogram, based on this patient information, is then derived and used in conjunction with information derived from a blood pressure waveform to refine an estimate of the performance factor. Each of the different prior art apparatuses identified above is likely to have one or more disadvantages. The Band apparatus, for example, requires external calibration using an independent CO measurement to determine a factor related to vascular impedance that is then used in CO calculations. U.S. Patent No. 6,315,735 (Joeken et al., November 13, 2001) describes another device with the same disadvantage.

Wesseling (U.S. Patent No. 5,400,793, March 28, 1995) attempts to determine a factor related to vascular performance from anthropometric data such as height, weight, gender, patient age, etc. This method has a relationship that is determined from human nominal measurements and may not apply robustly to a wide range of patients.

Romano attempts to determine a factor related to vascular impedance solely from the characteristics of the arterial pressure waveform, and thus fails to take advantage of the known relationships between patient characteristics and performance. In other words, by freeing his system from a need for anthropometric data, Roman also loses the information contained in such data. In addition, Romano relies on several intermediate calculations on pressure waveform derivative values. As is well known, however, such derivative estimates are inherently noisy. Roman's method was therefore considered unreliable.

What is needed is a system and method for more robustly and more accurately estimating cardiovascular parameters such as blood performance or endurance (K or C), vascular tone, tau, or values calculated from these parameters such as SV and CO.

One of the present inventors has previously published that SV may be approximated by being proportional to the standard deviation of the blood pressure waveform P (t), or some other signal which is itself proportional to P (t): Patent Application US Published No. 2005/0124903 A1 (Luchy Roteliuk et al., June 9, 2005), "Pressure Based System and Method for Determining Cardiac Stroke Volume"). So one way to estimate SV is to apply the ratio:

SV = Κσ (Ρ) = K standard deviation (P) (Equation 4)

where K is a scaling factor and from which follows: CO = Ko (P) HR = K standard deviation (P) HR. (Equation 5)

This proportionality between the SV and the standard deviation of the blood pressure waveform is based on the observation that the pulsability of a pressure waveform is created by the cardiac SV in the artery tree as a function of vascular tone (ie. , vascular performance and peripheral resistance). The scaling factor K of equations 4 and 5 is an estimate of vascular tone. Recently one of the present inventors also published

that vascular tone can be reliably estimated using the characteristics of arterial pulse pressure waveform formation in combination with a pressure-dependent vascular performance measurement and anthropometric patient data such as age, sex, height , weight and body surface area (BSA): US Patent Publication No. 2005/0124904 A1 (Luchy Roteliuk, June 9, 2005), "Automatic blood pressure-based determination of a cardiovascular parameter"). To quantify arterial pulse pressure waveform information, he used statistical moments in the highest-order time domain of arterial pulse pressure waveform (such as kurtosis and obliquity) in addition to weighted statistical moments. of pressure again derived. Thus, vascular tone is calculated as a function of a combination of parameters using a multivariate regression model with the following general form:

K = χ (μΤι, μτ2, .. μτκ, μρι, μρ2, ··· μρκ, C (P), BSA1Age, G ...) (Equation 6)

25

Where

K is vascular tone (the calibration factor in equations 4 and 5), X is a multiple regression statistical model,

μτι · ..μτκ are the time-domain statistical moments from the 1st to the 3rd order of the arterial pulse pressure waveform,

μρι ... μρκ are the weighted statistical pressure moments from the 1st to the 1st order of the arterial pulse pressure waveform,

C (P) is the pressure-dependent vascular performance calculated using the methods proposed by Langwouters et al., 1984 ("The Static Properties of 45 Human Thoracic and 20 Abdominal Aortas in vitro and the Parameters of a New Model", J. Biomechanics, Vol 17, No. 6, pp 425-435, 1984), BSA is a surface area of the patient's body (function of height and weight),

Age is the age of a patient, and G is the gender of a patient.

The set of predictive variables for calculating vascular tone K factor using the multivariate model χ was related to the "true" measurement of vascular tone, determined as a function of CO measured by thermodilution and arterial pulse pressure. , for a test population or reference individuals. This creates a group of vascular tone measurements, each of which is a function of the χ component parameters. The multivariate approximation function is then calculated using known numerical methods, which best refer to the parameters of χ to a given group of CO measurements in some predefined sense. A polynomial multivariate adjustment function is used to generate the polynomial coefficients that provide a value of χ for each set of predictor variables. Thus, the multivariate model has the following general form:

2 '= [A1 A2 ... *

X ~ 2 no.

(Equation 7)

where Ai ... An are the coefficients of the polynomial multiregression model, and X are the predictor variables of the model: Π / \ Pll - Pl.m] \

I Lu -! ... Urk Up1 ... μΡί ... hpk C BSA Idcde G ...] Λ .......... J

Ρ, Λ Ργι, τη-

(Equation 8)

The method listed above relies solely on an arterial pulse pressure measurement. Its simplicity and the fact that it does not require calibration are the advantages of this method. However, due to the empirical nature of vascular tone estimation ratios, the accuracy of this method may be poor in some extreme clinical situations where the basic empirical ratios of the model are not valid. For this reason, a second independent measurement can be beneficial if added to the basic multiregression model. As shown above, many techniques have been invented, both

noninvasive and invasive methods to measure SV and CO, and particularly to detect vascular performance, peripheral resistance and vascular tone. It should be appreciated that there is a need for a system and method for estimating CO, or any parameter that can be derived from it or using CO, which is robust and accurate and which is less sensitive to calibration and computational errors. Brief Description of the Drawings

Figure 1 illustrates an example of two blood pressure curves depicting two different blood pressure measurements received from an individual according to one embodiment of the invention.

Figure 2 illustrates an example of an Electrocardiogram (ECG) measurement and a blood pressure measurement received from an individual according to one embodiment of the invention.

Figure 3 is a graph illustrating the relationship between arterial pulse pressure spread time and arterial performance according to one embodiment of the invention.

Figure 4 is a graph illustrating the relationship between the propagation time of arterial pulse pressure and vascular tone in the recovery of patients from cardiac interruption according to one embodiment of the invention. Figures 5 and 6 are graphs illustrating the correlation between the propagation time of arterial pulse pressure and vascular tone for different hemodynamic conditions of subjects according to various embodiments of the invention.

Figures 7 to 9 are graphs illustrating the correlation between the

CO calculated using arterial pulse pressure propagation time, Continuous Cardiac Output (COC) values, and CO values measured by thermodilution bolus measurements (TD-CO) for different hemodynamic states of individuals according to various embodiments of the invention. . Figure 10 is a graph showing the relationship between estimated CO

using the pulse pulse pressure propagation time according to various embodiments of the invention and the estimated CO using the pulse pulse pressure signal.

Figure 11 is a block diagram showing an exemplified system used to perform the various methods described herein according to various embodiments of the invention.

Figure 12 is a flow chart showing a method according to one embodiment of the invention. Summary of the Invention One embodiment of the invention provides a method for determining

determine a cardiovascular parameter that includes receiving an input signal corresponding to a blood pressure measurement over an interval covering at least one cardiac cycle, determining an input signal propagation time, determining at least one statistical signal input, and determine an estimate of the cardiovascular parameter using the propagation time and at least one statistical moment.

One embodiment of the invention provides an apparatus for determining a cardiovascular parameter including a processing unit for receiving an input signal that corresponds to a blood pressure measurement over a range covering at least one cardiac cycle, determining a heart rate. propagation time of the input signal, determine at least one statistical moment of the input signal and determine an estimate of the cardiovascular parameter using the propagation time and at least one

statistical moment.

Detailed Description of the Invention

Methods and systems that implement the embodiments of the various features of the invention will now be described with respect to the drawings.

The drawings and associated descriptions are provided to illustrate embodiments of the invention and do not limit the scope of the invention. Reference in the specification to "one embodiment" is intended to indicate that a particular feature, structure, or feature described in conjunction with the embodiment is included in at least one embodiment of the invention. Aspects of the phrase "one modality" in various places in the specification are not necessarily all referring to the same modality. Throughout the drawings, reference numbers are reused to indicate the correspondence between the referenced elements.

More broadly, the invention involves the determination of a cardiac value, such as a systolic volume (SV), and / or a value derived from the SV such as cardiac output (CO), using propagation time. of arterial pulse pressure. Arterial pulse pressure propagation time can be measured using arterial pressure waveforms that are proportional to or derived from arterial pulse pressure, electrocardiogram measurements, bioimpedance measurements, other cardiovascu- lar parameters, and so on. These measurements can be made with an invasive, noninvasive or minimally invasive instrument or a combination of instruments. The invention may be used with any type in question if

human or animal. As it is anticipated that the most common use of the invention will be in humans in a diagnostic configuration, the invention is described below primarily in use with a "patient". This is by way of example only, however, it is intended that the term "patient" should encompass all individuals, both humans and animals, without regard to configuration.

Figure 1 illustrates an example of two blood pressure curves representing two different blood pressure measurements received from an individual. The top curve represents a measurement of the central arterial pressure detected from the subject's aorta and the base curve represents a measurement from the individual's radial artery. Pulse pressure propagation time (tpr0p) can be measured as the transit time between the two blood pressure measurements.

The reasoning for using pulse pressure propagation time for hemodynamic measurements is based on a basic principle of cardiovascular biomechanics. That is, if blood pumped from the individual's heart through a completely rigid vessel, upon contraction of the heart, the pressure waveform would be instantly present at any distal arterial location in the individual's body. However, if blood pumped from the individual's heart through a compatible vessel, upon contraction of the heart, the pressure waveform would be present for some time after the heart was contracted at a distal arterial location in the body of the subject. individual.

Pulse pressure propagation time can be measured invasively or noninvasively at several different locations on the pressure waveform (or any other pressure waveform-related waveform). In the example shown in Figure 1, pulse pressure propagation time can be measured using two different blood pressure measurements, for example, a reference measurement from the aorta and a peripheral measurement from the radial artery.

Figure 2 illustrates an example of using an electrocardiogram signal as a reference signal for propagation time measurement. The top curve represents an electrocardiogram (ECG) signal detected with electrodes located near the subject's heart, and the base curve represents a measurement of blood pressure detected from the individual's peripheral artery. In this example, the propagation time of arterial pulse pressure (tpr0p) can be measured using the transit time between the ECG signal and peripheral arterial pressure. Similarly, a transthoracic bioimpedance measurement could be used as a reference site, and propagation time could be measured as a transit time against a peripheral measurement derived or proportional to blood pressure.

Arterial pulse pressure propagation time provides an indirect measurement of the physical (i.e. mechanical) properties of a vessel segment between the two recording sites. These properties primarily include the geometric and elastic properties of arterial walls. The properties of arterial walls, for example, their lumen thickness and diameter, are some of the main determinants of the propagation time of arterial pulse pressure. As a result, pulse pressure spread time depends mainly on arterial performance.

Figure 3 illustrates an example where pulse pressure propagation time increases with increasing arterial performance (C). Therefore, pulse pressure propagation time (tpr0p) can be represented as a function of arterial performance (C), that is,

ttrop = f (C). (Equation 9)

The propagation time of arterial pulse pressure can then

be used as a simple measurement to estimate arterial performance. Propagation time may be used as a separate measurement to assess a patient's vascular status or may be used in a pulse contour cardiac output algorithm along with other parameters to account for the effects of vascular performance, vascular resistance. and vascular tone. In one embodiment, the propagation time of arterial pulse pressure is measured using an arterial pulse pressure signal from relatively large arteries (eg, radial, femoral, etc.) and then the influence of peripheral resistance is minimum. Also, this measurement may include mean arterial performance between measurement sites and may not reflect pressure dependence on arterial performance.

The basic relationship could be derived from the well-known Bramwell-Hill equation used to calculate pulse wave velocity (PVW): PWVz = ---- V

dv P (Equation 10)

Where

dP is the change in pressure, dV is the change in volume, ρ is the blood density, and V is the baseline volume.

Arterial performance (C) can be defined as the ratio of incremental change in volume (dV) resulting from an incremental change in pressure (dP), ie

áV C = -

dP (Equation 11)

Substituting equation (11) into equation (10) gives the following equation:

ι ι

PWV2 ----- F

c P (Equation 12)

On the other hand, PWV is defined as follows:

L

PWV = -

tProp (Equation 13)

where L is the vascular length between two recording sites and tpr0p is the propagation time of arterial pulse pressure.

If equation 13 is replaced by equation 12, the performance of

can be given by:

_ ι_ ι

c-v-I ^ v (Equation 14)

If γ is defined as:

1 1 γ = - ■ - ■ V

^ P (Equation 15)

Arterial performance can be represented as:

C = Vipr0p (Equation 16)

where the scaling factor y is a function that depends on blood density, the effective vascular distance between the two recording sites and the basic volume, that is, y depends on the physical vascular volume between the two recording sites and blood viscosity. (ie Hematocrit, etc.).

Based on the above equations, the arterial pulse pressure spread time can be used in a number of different ways.

1. The use of arterial pulse pressure spread time to estimate arterial performance. Pulse pressure propagation time can be used as an input to a hemodynamic model based on the standard deviation of arterial pulse pressure to assess the dynamic changes in blood pressure created by systolic ejection. CO can be represented as a function of the standard deviation of arterial pulse pressure as follows:

CO = K * dvp (P) * HR (Equation 17)

where K, as shown above, is a scaling factor proportional to arterial performance, dvp (P) is the standard deviation of arterial pulse pressure, and HR is the heart rate.

It is also understood that:

MAP

CO = C--

τ (Equation 18)

where MAP is mean arterial pressure, τ is an exponential pressure drop constant, and C1 as K is a scaling factor related to arterial performance.

From equations 17 and 18, the scaling factor K is a measure equal to vascular performance. If the scaling factor K in equation 17 were replaced by performance as given in equation 16, CO can be calculated using the standard deviation of the arterial pulse pressure waveform and the arterial pulse pressure propagation time:

^ O = Y- top • dvp (Ρ) · H R (Equation 19) where the standard deviation of arterial pulse pressure can be calculated using the equation:

I n

dvp (p) = j_J_ £ [P (>) _ pmeia] z

fe = 1 (Equation 20)

where η is the total number of samples, P (k) is the instantaneous pulse pressure, and Pmedia is the mean arterial pressure. Mean blood pressure can be defined as:

no

Pmiàia ~ ~ ^ P (Ji)

k = i (Equation 21)

Figure 4 is a graph illustrating the relationship between the square of the propagation time of arterial pulse pressure and the scaling factor K of patients during recovery from cardiac graft surgery. Figure 4 outlines ten (10) average data points from ten (10) different patients. In the example in Figure 4, the propagation time of arterial pulse pressure was calculated as a transit time between the ECG signal and radial arterial pressure. The data shown in Figure 4 illustrate that the scaling factors K of equation 17 can be effectively estimated using the arterial pulse pressure propagation time as given by equation 16.

Figures 5 and 6 are graphs illustrating the correlation between the propagation time of arterial pulse pressure and the scaling factor K of equation 17 for different hemodynamic states of the two individuals. Both trends correspond to animal data obtained from experiments using porcine animal models. These figures show identical trends in the scaling factor K and squared pulse pressure time. The data in Figures 5 and 6 illustrate that the scaling factors K or C of equations 17 and 18 can be effectively estimated using the arterial pulse pressure propagation time.

The scaling factor γ of equation 19 can be determined using any predetermined propagation time and pressure function P (t); like this,

Y = Titpmp, P) (Equation 22)

where Γ is a predetermined function of propagation time and pressure, used to develop computational methods for estimating γ.

Any known and independent CO technique can be used to determine this relationship, whether invasive, for example, thermodilution, or noninvasive, for example, transesophageal echocardiography (TEE) or bioimpedance measurement. The invention provides continuous CO bias between intermittent measurements such as TD or TEE.

Even if an invasive technique such as catheterization is used to determine γ, it will not usually be necessary to leave the catheter on the patient during the subsequent CO monitoring session. In addition, even when using a catheter-based calibration technique to determine y, it is not necessary for the measurement to be made close to the heart, preferably the calibration measurement may be performed on the femoral artery. As such, even when an invasive technique is used to determine γ, the invention as a whole is still minimally invasive since any catheterization may be peripheral or temporary.

As discussed above, instead of measuring blood pressure directly, any other input signal can be used that is proportional to blood pressure. This means that calibration can be done at any or all of the various points in the calculations. For example, if any signal other than blood pressure itself is used as an input signal, then it can be calibrated for blood pressure before its values are used to calculate standard deviation, or subsequently, in which case the value of The resulting standard deviation can be scaled, or the resulting SV value can be calibrated (eg by setting γ appropriately), or some final SV function (such as CO) can be scaled. In summary, the fact that the invention may in some cases use an input signal other than a direct blood pressure measurement does not limit its ability to generate an accurate SV estimate.

In addition to blood viscosity, γ depends mainly on the physical vascular volume between two recording sites. Of course, the effective length (L) and effective volume (V) between the two recording sites may not be known. Vascular branching and patient-to-patient differences are two main reasons why the effective physical vascular volume between two recording sites may not be known. However, it is obvious that this physical volume is proportional to the patient's anthropometric parameters and so it can be estimated indirectly using the patient's anthropometric parameters. Anthropometric parameters can be derived from various parameters such as measured distance (I) between the two recording sites, patient weight, patient height, patient gender, patient age, patient's bsa, etc., or any combination of these. factors. In one embodiment, all anthropometric parameters, for example, the distance (I) between recording sites, patient weight, patient height, patient gender, patient age, patient bsa, can be used to calculate y. Additional values are also preferably included in the calculation to take other characteristics into account. In one embodiment, the HR heart rate (or R wave period) may be used. Like this,

γ = ΓΜ (Ι, Η, W, BSA1Age1G, HR) (Equation 23)

Where

I is the distance measured between the two recording sites, H is the patient's height, W is the patient's weight, BSA is the patient's bsa, Age is the patient's age, G is the patient's gender, HR is the Patient's heart rate, ΓΜ is a multivariate model.

The set of predictive variables for calculating γ using the multivariate model relacionado is related to the measurement of "true" vascular performance, determined as a CO function measured by thermodilution and arterial pulse pressure, for a test population. or reference individuals. This creates a group of performance measurements, each of which is a function of the component parameters of ΓΜ. The multivariate approximation function is then calculated using numerical methods that best refer to the parameters of ΓΜ to a given group of CO measurements in a predefined way. A polynomial multivariate adjustment function is used to generate the polynomial coefficients that gives a value of ΓΜ for each set of predictor variables. Thus, the multivariate model has the following general equation:

ΓΜ = [O1 Q2 - qJ *

"1

(Equation 24)

Where ai ... an are the coefficients of the polynomial multiregression model, and Y are the predictor variables of the model:

Kn,! = [~] | [Z H W BSA Id G G HR] Λ ·. '· - ■ · ■ j

TM s ^ n 1 »· 'm ./

·· iHrnJ / (Equation 25)

The use of arterial pulse pressure propagation time is to estimate vascular tone. Vascular tone is a hemodynamic parameter used to describe the combined effect of vascular performance and peripheral resistance. In the prior art, blood pressure waveform characteristics in combination with patient anthropometric data and other cardiovascular parameters were used to estimate vascular tone (see Roteliuk, 2005, "Arterial pressure-based auto- matic determination"). of a cardiovascular parameter "). Arterial pulse pressure propagation time can also be used to estimate vascular tone. In one embodiment, arterial pulse pressure propagation time can be used as an independent term for a multivariate regression model to continually estimate vascular tone. In one embodiment, the arterial pulse pressure propagation time may be used in combination with the arterial pulse pressure waveform shape information to estimate vascular tone. Statistical moments of the highest order-sensitive blood pressure and pressure-weighted time points can be used as predictor variables in the multivariate model along with the propagation time of arterial pulse pressure. Additional values are also preferably included in the calculation to take other characteristics into account. For example, HR heart rate (or R-wave period), BSA body surface area as well as C (P) -dependent nonlinear performance value can be calculated using a known method described by Langwouters, which calculates performance as a polynomial function of the patient's pressure waveform and age and gender. Like this,

K = X (tProp, μη, μτ2, ·. ·, Μτκ, μρι, μρ2 ..... Μρκ, C (P), BSA, Age, G ...) (Equation

26) where

K is vascular tone, X is a multiregression statistical model,

tprop is the propagation time of arterial pulse pressure, μτι ... μτκ are the time-domain statistical moments from the 1st to the 1st of the arterial pulse pressure waveform,

μρι.,. μρκ are the pressure-weighted statistical moments from the 1st to the 1st order of the arterial pulse pressure waveform,

C (P) is the pressure-dependent vascular performance calculated using the methods proposed by Langwouters et al., 1984 ("The Static Elastic Pro- perties of 45 Human Thoracic and 20 Abdominal Aortas in vitro and the Parameters of a New Model" J. Biomechanics, Vol. 17, No. 6, pp. 425-435 (1984),

BSA is a surface area of the patient's body (function of height and weight),

Age is the age of the patient; and G is the patient's gender. Depending on the needs of a given implementation of the

invention, one may choose not to include either obliquity or kurtosis, or one may include even higher order moments. The use of the first four statistical moments has been proven to contribute to a robust and accurate performance estimate. In addition, anthropometric parameters other than HR and BSA may be used additionally, or instead of other methods that may be used to determine C (P) 1 which may even be completely omitted.

The described method exemplified below for calculating a current vascular tone value can be adjusted in a known manner to reflect the increased, decreased, or altered set of parameters. Once the parameter set for calculating K has been assembled, it may be related to a known variable. Existing devices and methods, including invasive techniques such as thermodilution, can be used to determine CO, HR, and SVest for a test population or reference individuals. For each concerned, anthropometric data such as age, weight, BSA, height, etc. can also be recorded. This creates a group of CO measurements, each of which is a (initially unknown) function of the K component parameters. A approximation function can then be calculated using the best known numerical methods. parameters for K given the CO measurement group in the same predefined direction. An easily calculated and well understood approximation function is the polynomial function. In one mode, a standard multivariate fitting routine is used to generate the coefficients of a polynomial that gives a value of K for each parameter set tprop, HR, C (P), BSA, μιΡ, oP, μ3Ρ, μ4ρ , μιτ, στ, μ3τ, Μ4Τ- In one embodiment, K is calculated as follows:

K = [A1 A2 ... An] *

A1 Xn

xη

(Equation 27)

Where

r-r /, Γ ^ -ι.χ - P

λ'η, Ι = I I f [tvrcp> P71, Pn, - PTk P2, -V-Pk "0 *) BSA ^ ade G ...]

Λ.1 ■ * «n J

(Equation 28)

The use of arterial pulse pressure propagation to directly estimate CO is discussed below.

Pulse pressure propagation time can be used as an independent method for estimating CO. That is, the arterial pulse pressure propagation time is independently proportional to SV1 as shown below:

L

SV = Kp ■

tprop (Equation 29)

CO can be estimated if equation 29 is multiplied by HR:

L

CO = Kp --- HR

tprop (Equation 30)

The scaling factor Kp can be estimated using a direct calibration, for example using a known CO value from a bolus thermodilution measurement or other gold standard CO measurement. Figures 7 to 9 are graphs illustrating the correlation between calculated CO using pulse pressure propagation time as shown in equation 30 (COprop), Continuous Cardiac Output (OCC), and CO values measured by bolus measurements by intermittent thermodilution (ICO). CCO and ICO are measured using the Surveillance monitor manufactured by Edwards Lifesciences of Irvine, California. Measurements were performed on models of swine animals in different hemodynamic states of the animals. These graphs show experimentally that changes in CO are referred to as changes in pulse pressure propagation time and that this can be used as an independent method to estimate CO. The scaling factor Kp of equation 30 can be determined by

using any predetermined propagation time function and CO or SV. Any CO-independent technique can be used to determine this relationship, whether invasive, for example, thermodilution, or noninvasive, for example, transesophageal echocardiography (TEE) or bioimpedance measurement. The invention provides the continuous trend of CO between intermittent measurements such as TD or TEE.

Even if an invasive technique, such as catheterization, is used to determine Kp, it may not be necessary to leave the catheter in the patient during the subsequent CO monitoring session. In addition, even when using the catheter-based calibration technique to determine Kp, it is not necessary for measurement to be made close to the heart, preferably calibration measurement can be performed on the femoral artery. As such, even when an invasive technique is used to determine Kp, the method is still minimally invasive since any catheterization may be peripheral or temporary.

The approach shown in equation 30 allows CO measurement to be performed completely noninvasively, if noninvasive techniques are used to measure propagation time and if a predefined function or ratio is used to measure Kp. Noninvasive techniques for measuring propagation time may include, but are not limited to: ECG, noninvasive blood pressure measurements, bioimpedance measurements, optical pulse oximetry measurements, Doppler ultrasound measurements, or any other measurements derived from or proportional to them or any combination thereof (for example: using Doppler ultrasound pulse rate measurement to measure the reference signal near the heart and using a bioimpedance measurement to measure the peripheral signal, etc.).

The scaling factor Kp depends mainly on blood viscosity and the physical vascular distance and volume between the two recording sites. Of course, the effective length (L) and effective volume (V) between the recording sites may not be known. Vascular branching and patient-to-patient differences are two main reasons why the effective physical vascular volume between the two recording sites may not be known. However, the physical volume may be proportional to the patient's anthropometric parameters and so it may be estimated indirectly using the patient's anthropometric parameters. Anthropometric parameters can be derived from various parameters such as measured distance (L) between two recording sites, patient weight, patient height, patient gender, patient age, patient's bsa, etc., or any combination. of these parameters. In one embodiment, all anthropometric parameters, for example, the distance (L) between recording sites, patient weight, patient height, patient gender, patient age, patient bsa, can be used to calculate Kp. Thus, Kp = M (L, H, W, BSA, Age, G) (Equation 31) where

L is the distance between the two recording sites, H is the patient's height, W is the patient's weight, BSA is the patient's bsa, Age is the patient's age, G is the patient's gender,

M is a multivariate linear regression model.

multivariate model M, is related to "true" CO measurement, determined as a function of propagation time, where CO is measured by thermodilution, for a test population or reference individuals. This creates a group of measurements, each of which is a function of the component parameters of Μ. The multivariate approximation function is then calculated using known numerical methods that best refer to the parameters of M to a given group of CO measurements in some predefined sense. A polynomial multivariate adjustment function is used to generate the polynomial coefficients that provide a value of M for each set of predictor variables. Thus, the multivariate model has the following equation:

where a-i ... an are the coefficients of the polynomial multiprogression model, and Y are the predictor variables of the model:

The set of predictor variables for calculating Kp using the

M = Ja1 a2 ... j *

Yl

(Equation 32)

Jl [L H W BSA Age G] A '(Equation 33)

Figure 10 is a graph showing the relationship between estimated CO using equation 17 (x-axis COstd) and estimated CO using equation 30 (y-axis COpr0p) from a series of experiments with animals. The data show CO measurements from a total of ten (10) pigs. Three (3) selected data points from each pig are used for the chart. In order to cover a wide range of CO1 each data point selected corresponds to a different hemodynamic state of the pig: vasodilated, vasoconstrictive, hypovolemic states, respectively. The proportionality shown in figure 10 is the experimental proof of the effectiveness and reliability of using propagation time to estimate CO. Figure 11 is a block diagram showing an electronic system.

This example is used to perform the various methods described here. The system may include a patient 100, a pressure transducer 201, a catheter 202, ECG electrodes 301 and 302, signal conditioning units 401 and 402, a multiplexer 403, an analog to digital converter 405 and a computing unit 500. Computing unit 500 may include a patient-specific data module 501, a scaling factor module 502, a momentum module 503, a standard deviation module 504, a propagation time module 505, a system volume module 506, a cardiac output module 507, a heart rate module 508, an input device 600, an output device 700, and a heart rate monitor 800. Each unit and module can be implemented in hardware, software, or a combination of hardware and software.

Patient Specific Data Module 501 is a memory module that stores patient data such as age, height, weight, gender, patient BSA, etc. This data can be entered using input device 600. The scaling factor module 502 receives the patient data and performs calculations to calculate the scaling performance factor. For example, the scaling factor module 502 places the parameters in the expression given above or some other derived expression by creating an approximation function that best fits a test data set. The scaling factor module 502 may also determine the time window [tO, tf] by which each vascular performance, vascular tone, SV and / or CO estimation is generated. This can be done simply by choosing which and how many of the stored, consecutive and discrete values are used in each calculation.

Momentum module 503 determines or estimates the momentum in the highest order statistical time domain of arterial pulse pressure and the weighted moments. Standard Deviation Module 504 determines or estimates the standard deviation of the arterial pulse pressure waveform. Propagation time modulus 505 determines or estimates the arterial pulse pressure waveform propagation time. The scaling factor, the statistical moments of order

In the highest range, the standard deviation and propagation time are entered into the stroke volume module 506 to produce or estimate an SV value. A heart rate monitor 800 or 508 software routine (for example, using Fourier or derivative analysis) can be used to measure the patient's heart rate. The SV value or estimate of the patient's heart rate is entered into the cardiac output module 507 to produce an estimate of CO using, for example, the equation CO = SV * HR.

As mentioned above, the system may need to calculate SV or CO if these values are not of interest. The same is true for vascular performance, vascular tone, and peripheral resistance. In such cases, the corresponding modules may not be required and may be omitted. For example, the invention may be used to determine arterial performance. However, as Figure 11 illustrates, any or all results, SV, CO, vascular performance, vascular tone, and peripheral resistance may be displayed on output device 700 (for example, a monitor) for presentation and interpretation by a user. As with input device 600, output device 700 may typically be the same when used by the system for other purposes.

The invention further relates to a computer program

chargeable loader on a computer unit or a computer unit 500 to perform the method of the invention. In addition, the various modules 501 to 507 may be used to perform the various calculations and perform the method steps referred to in accordance with the invention and may also be stored as computer executable instructions in a computer readable medium. to allow the invention to be carried and carried out by different processing systems.

While certain exemplary embodiments have been described and shown in the accompanying drawings, it is understood that such embodiments are merely illustrative and not restrictive in the broad invention, and that this invention is not limited to the specific constructions and arrangements shown and described, as various Other changes, combinations, omissions, modifications and substitutions, in addition to those presented in the paragraphs above, are possible. Those skilled in the art will appreciate that various adaptations and modifications of the preferred embodiment described may be configured without departing from the scope and spirit of the invention. Thus, it is to be understood that within the scope of the appended claims, the invention may be practiced in addition to as specifically described herein.

Claims (37)

A method for determining a cardiovascular parameter comprising: receiving an input signal corresponding to a blood pressure measurement over a range covering at least one cardiac cycle; determine a propagation time of the input signal; and determine an estimate of the cardiovascular parameter using propagation time. A method according to claim 1 further comprising determining at least one statistical moment of the input signal. A method according to claim 2, wherein the step of determining an estimate of the cardiovascular parameter using propagation time includes determining an estimate of the cardiovascular parameter using at least one statistical moment. A method according to claim 2, wherein at least one statistical moment of the input signal is selected from a group consisting of a standard deviation of the input signal and a statistical moment that has a larger order of the input signal. than two, the kurtosis of the input signal and the obliquity of the input signal. Method according to claim 1, wherein the cardiovascular parameter is selected from a group consisting of arterial performance, vascular resistance, cardiac output and stroke volume. The method of claim 1, wherein the step of determining an input signal propagation time includes determining a transit time between a detected reference signal near an individual's heart and a detected peripheral arterial signal close to an artery of the individual. A method according to claim 6, wherein the reference signal is selected from a group consisting of an electrocardiogram measurement, a central aortic pressure measurement, a transthoracic bioimpedance measurement and a measurement of the Doppler ultrasound blood velocity. A method according to claim 6, wherein the peripheral artery signal is selected from a group consisting of a blood pressure measurement, an optical oximetry measurement that measures oxygen saturation. individual's blood, a peripheral bioimpedance measurement and a Doppler ultrasound blood velocity measurement. The method of claim 1, wherein determining an estimate of the cardiovascular parameter using propagation time also includes using the standard deviation of the input signal to determine an estimate of the cardiovascular parameter. The method of claim 1 further comprising receiving an anthropometric parameter from the subject. The method of claim 10, wherein determining an estimate of the cardiovascular parameter using propagation time also includes using the anthropometric parameter to determine an estimate of the cardiovascular parameter. The method of claim 10, further comprising estimating an arterial performance value using the propagation time and the anthropometric parameter. A method according to claim 12 further comprising estimating a stroke volume using the arterial performance value and a standard deviation of the input signal. A method according to claim 13 further comprising: receiving a heart rate measurement from an individual; and estimate cardiac output using heart rate measurement and stroke volume. The method of claim 14, further comprising estimating cardiac output using arterial performance and standard deviation. The method of claim 15 further comprising: receiving a calibration cardiac output value; and calculate a calibration constant as a quotient between the estimated cardiac output calibration and the product of heart rate, arterial performance and standard deviation. A method according to claim 12, wherein estimating an arterial performance value further comprises: determining an approximation function by referring to a plurality of reference measurements for arterial performance, where the approximation function is a function of the propagation time of the input signal and the anthropometric parameter; and to estimate the individual's arterial performance value by assessing the approximation function with the propagation time of the input signal and the anthropometric parameter. The method of claim 1 further comprising: calculating a component propagation time value for each of the plurality of cardiac cycles; calculate a composite propagation time value as an average of component propagation time values; and use the composite propagation time value to calculate an estimate of the cardiovascular parameter. A device for determining a cardiovascular parameter, comprising: a processing unit for: receiving an input signal corresponding to a blood pressure measurement over a range covering at least one cardiac cycle; determine a propagation time of the input signal; and determine an estimate of the cardiovascular parameter using propagation time. Apparatus according to claim 19, wherein the processing unit determines at least one statistical moment of the input signal. Apparatus according to claim 20, wherein the processing unit determines an estimate of the cardiovascular parameter using at least one statistical moment. Apparatus according to claim 20, wherein at least one statistical moment of the input signal is selected from a group consisting of a statistical moment having an order greater than two, of the kurtosis of the input signal, the input signal obliquity and a standard deviation of the input signal. Apparatus according to claim 19, wherein the cardiovascular parameter is selected from a group consisting of arterial performance, vascular resistance, cardiac output and systolic volume. Apparatus according to claim 19, wherein the processing unit determines an input signal propagation time by determining a transit time between a detected reference signal near an individual's heart and a detected peripheral arterial signal. next to an artery of the individual. Apparatus according to claim 24, wherein the reference signal is selected from a group consisting of an electrocardiogram measurement, a central aortic pressure measurement, a transthoracic bioimpedance measurement, and a velocity measurement. Doppler ultrasound. Apparatus according to claim 24, wherein the peripheral arterial signal is selected from a group consisting of a blood pressure measurement, an optical oximetry measurement that measures the oxygen saturation of the blood. a bioimpedance measurement and a Doppler ultrasound blood velocity measurement. Apparatus according to claim 19, wherein the processing unit determines an estimate of the cardiovascular parameter using a standard deviation of the input signal. Apparatus according to claim 19, wherein the processing unit receives an anthropometric parameter from the individual. Apparatus according to claim 28, wherein the processing unit determines an estimate of the cardiovascular parameter using the propagation time and the anthropometric parameter. Apparatus according to claim 28, wherein the processing unit estimates an arterial performance value using the propagation time and the anthropometric parameter. The apparatus of claim 30 further comprising estimating a stroke volume using the arterial performance value and a standard deviation of the input signal. The apparatus of claim 31 further comprising: receiving a heart rate measurement from an individual; and estimate cardiac output using heart rate measurement and stroke volume. Apparatus according to claim 32, further comprising estimating cardiac output using arterial performance and standard deviation. The apparatus of claim 33 further comprising: receiving a calibration cardiac output value; and calculate a calibration constant as a quotient between the estimated cardiac output calibration and the product of heart rate, arterial performance and standard deviation. The apparatus of claim 29, wherein estimating an arterial performance value further comprises: determining an approximation function by referring to a plurality of reference measurements for arterial performance, where the approximation function is a function of the propagation time of the input signal and the anthropometric parameter; and to estimate the individual's arterial performance value by assessing the approximation function with the propagation time of the input signal and the anthropometric parameter. The apparatus of claim 19 further comprising: calculating a component propagation time value for each of the plurality of cardiac cycles; calculate a composite propagation time value as an average of the component propagation time values; and use the composite propagation time value to calculate an estimate of the cardiovascular parameter. 37. Machine readable medium providing instructions, which when executed by a processor, causes the processor to determine a cardiovascular parameter comprising: receiving an input signal corresponding to a blood pressure measurement over a range covering at least one cardiac cycle; determine a propagation time of the input signal; and determine an estimate of the cardiovascular parameter using propagation time.